- Research article
- Open Access
Reframing data ethics in research methods education: a pathway to critical data literacy
International Journal of Educational Technology in Higher Education volume 20, Article number: 11 (2023)
This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities.
Data permeates every dimension of our lives, as numbers are used to rate, compare, and allocate us into different categories. Our own data is used to define our worth, measure our effectiveness and, in a myriad of other ways, to inform or construct what we are today. We are ‘governed by numbers’—subject to numbers and numbered subjects (Ball, 2015; Ozga, 2008), so are our scholarly practices, including teaching and research (Grant, 2022).This situation has been widely touted as a breaking dawn of increasingly information based on ever-expanding volumes of data, critical voices have observed that everyone and everything is now quantified and subject to automated and potentially discriminatory decision-making processes (Eubanks, 2018; Kleinberg et al., 2018; Lambrecht & Tucker, 2019). Research education must acknowledge that datafication of society has an impact on how research is conducted and the ethical challenges researchers and data scientists face in order to conduct studies that benefit society.
In this datafied environment, higher education (HE) educators and students must become aware of how technology driven data collection and processing affects themselves and others, and develop a critical research approach to the use of data, including technical and socially driven data literacies (Atenas et al., 2020; Ross et al, 2022; Williamson et al., 2020). Current educational disadvantages in relation to critical data literacies risk rendering people into mere objects of both history (Freire, 1968) and data (Johnson, 2014), exacerbating existing inequalities. Indeed, according to the European Union’s recent framework for digital competence,
Everyone should acquire a basic understanding of new and emerging technologies including AI. This has the potential to support them to engage positively, critically and safely with this technology, and be aware of potential issues related to ethics, environmental sustainability, data protection and privacy, children rights, discrimination and bias, including gender bias and disability and ethnic and racial discrimination. (Redecker & Punie, 2020, p.14)
Such requirements point to the need for pedagogies that foster the development of lifelong and lifewide learning and transversal skills across the disciplines. In particular, it is incumbent upon those designing research methods, data science and data literacy programmes and learning units to systematically incorporate critical, ethical and political dimensions of data and datafication.
The critical orientation of our approach arises from the need, as identified in critical theory (Bohman, 2005; Bronner, 2009; Foucault, 1980) and in education, critical pedagogy (Freire, 1968; Giroux, 2010), to challenge economic and political domination by fostering inquiry into the operations and structures of power. Thus, we seek to support the development of up-to-date research methods courses that develop skills not just in technical data management and analysis, but support the development of critical data literacy and of the concept of agency in students as future researchers, so they are aware of and acknowledge the circumstances of oppression in the current pervasive datafication of human beings and society, often in the service of surveillance capitalism (Zuboff, 2015).
Therefore, in this paper we adopt an approach aligned with critical data literacy, defined by Brand and Sander (2020) as “the ability to critically engage with datafication by reflecting on the societal implications of data processing and implementing this understanding in practice” (p. 2). Drawing upon the aforementioned broad, critical orientations to society and education, we also situate ourselves in conversation with an emerging literature of critical data studies which is especially concerned with challenging power dynamics in the context of datafication (e.g. Hepp et al., 2022; Iliadis & Russo, 2016; Markham, 2018; Pangrazio & Selwyn, 2019; Richterich, 2018; Tygel & Kirsch, 2016). This article supports a re-framing of research methods and to ‘zoom in’ on the specific role of data ethics in data literacy and the shaping of research practices according to the degree to which the ethical dimensions of data are understood and considered.
Ethics is generally concerned with the analysis of what is good for society and individuals. Applications of ethics to fields as diverse as medicine and environmental protection are now well-established, and data ethics is developing into a distinct branch of applied ethics (Véliz, forthcoming). In this article we follow a broad definition of data ethics, as the study and evaluation of moral problems arising from data (including its uses and harvesting), algorithms and corresponding practices, in view of developing morally desirable solutions (Floridi & Taddeo, 2016). According to this understanding, data ethics works at a higher level of abstraction than information ethics, as information ethics deals with practices where data is already given a meaning and an interpretation (Floridi, 2010; Floridi & Taddeo, 2016). It should also be understood as a core element of data literacy; for Prado and Marzal (2013), data literacy is a skillset that “enables individuals to access, interpret, critically assess, manage, handle and ethically use data” (p. 126).
While we contend that the need for improved data literacy and data ethics is universal (Atenas et al., 2020), we focus particularly on the role research methods educators can play, as they foster the development of future researchers, and therefore, are uniquely positioned to address this need. To effectively implement action-guiding ethical principles, we adopt an ethics of care perspective as complementary to our critical orientation to data, as it examines how data affects social and political relationships, rather than only individual interests (Held, 2006; Noddings, 1988, 2017; Robinson, 2011; Tronto, 1993). It seems clear that there is a need for a holistic and inclusive approach to the embedding of data ethics training, but it is unclear to which extent such concerns are present in the existing curriculum, or whether they are discussed at all. In designing this study we therefore focused our efforts on answering the following research questions:
To what extent is data ethics currently incorporated and represented in
existing academic training in research methods and data science
data ethics frameworks from diverse sectors? And,
According to relevant, and especially critically-oriented literature, what are the key and emerging areas of ethical concern in working with data, that should therefore be included into programmes of study covering research methods and data literacies?
To promote ethically-informed critical data literacy programmes in taught courses in HE, we have assembled a data ethics framework based upon a three-part review which examined data ethics issues from a range of areas including research and data science, industry, government, education, public, private and civil society sectors, and academic literature.
The purpose of this paper is to both establish the need for, and outline, a set of action-guiding ethical principles for embedding data ethics as a core element in teaching data skills within research methods and data science training, in which ethics is understood not just as a learning unit, but a core transversal component across the elements of the research cycle, from data collection to analysis. Understanding the ethical conundrums inherent in working with data is core to the challenge of considering how data collected for an initial purpose might be put to other uses in the future, and how data from different sources may be combined into new datasets that can remove anonymity or be used to predict and influence behaviour (Hand, 2018). The ascendancy of techno-solutionism amid a fluid technological landscape makes the precise shape of future data threats difficult to discern; but experience suggests that the already marginalised and vulnerable carry the most risks. Sustainable ethical principles to guide decision-making in data practices, in education and beyond, are urgently needed.
To identify the different ethical concepts and approaches under discussion we examined three sources dealing with data uses and harvesting: syllabi in higher education courses, ethical frameworks and academic literature.
Identification and selection of syllabi
We identified the different syllabi through a non-systematic internet search using Google with the keywords “data ethics”, “digital ethics” or “data justice” in combination with “course” and “university”. We repeated this search with the corresponding terms in Spanish, Portuguese, Italian, French and German, to complement this study with the language competence of the authors.
First, we searched for the term “ethics” in research methods curricula, initially retrieving over 600 records. Identified records were reviewed to determine if a description of the course units and content was provided, on the first pass narrowing the sample to 340 records which provide some information. On further investigation, only 250 of these provided sufficient detail to extract relevant information about how (or if) data ethics is taught at undergraduate (118), master’s (81) and doctoral level (51). Second, we aimed to identify postgraduate-level data science programmes with prospectuses available to download or review online. This yielded 170 programmes, of which 80 provided a detailed description of the modules, including the units taught, so we could identify how or if data ethics was embedded in curricula. To ensure a minimum quality level, we only included syllabi from universities. Syllabi that were not available online or only offered a brief synopsis were excluded.
In total, we reviewed the inclusion of data ethics in research methods courses from 250 syllabi (quantitative, qualitative and hybrid in focus) and 80 postgraduate programmes in data science, which were taught between 2017 and 2021 in the US, UK, Spain, France, Germany, Brazil, Portugal and Italy.
Identification and selection of ethical frameworks
In a second step, we reviewed a series of existing frameworks on data ethics, drawn from academia, public sector organisations, civil society, and industry. We searched for academic literature and web-based information on structured frameworks, which contain underpinning ethical principles, pillars or skills in order to identify which elements were discussed and how these were described by the different organisations promoting data ethics. To identify these frameworks, we carried out a non-systematic literature search using Google and DuckDuckGo with the keywords: “ethics” and “data” along with “framework”, “guideline”, “principle” or “recommendation”. The use of two databases was intended to reduce algorithmic biases within each of these databases and avoid missing important sources. As quality control criteria, we only included frameworks from institutions with international reputation. We excluded frameworks that were sector-specific, incomplete or unavailable online. We identified 12 frameworks that specifically focus on data ethics principles and values. An examination of these 12 frameworks allowed us to assess how ethics is currently envisaged across a range of data-led projects, schemes and guidelines.
Identification and selection of relevant literature
In a third step, we reviewed an extensive body of literature about data practices, research ethics, and data ethics from diverse disciplines. Our aim was to synthesise a range of key arguments and concepts into a transversal framework that can be adopted across the disciplines. While other authors with related interests, such as Saltz and Dewar (2019), have conducted systematic reviews of literature which reveal valuable insights, we question whether a systematic approach must be understood as a ‘gold standard’ or as always appropriate to address research questions. A systematic approach can be most effective when the inclusion criteria for relevance to the topic under investigation can be defined very precisely, while still returning a significant number of results; this may not be the case where newer topics of inquiry are concerned. Furthermore, in order to exclude ‘lower-quality’ sources, systematic reviews tend to depend on proprietary academic databases which favour highly-cited journals and therefore typically reflect the perspectives and research outcomes of authors based in the Global North and better-resourced, research-intensive universities, reinforcing existing knowledge inequalities (Almeida & Goulart, 2017; Kordzadeh & Ghasemaghaei, 2022). In our study, we resist the notion that the purpose of conducting a literature review is always to systematically identify ‘what is already known’ about the topic, as knowledge claims arise from a wide range of highly unequal stakeholders with different interests.
Identifying relevant literature within this context requires both searching with a wide net and reviewing from a critical perspective. To this end, we adopted an approach known as critical interpretative analysis, a type of non-systematic review that aims to gather the key ideas within a field (Dixon-Woods et al, 2006; McDougall, 2015). In our case, the primary purpose of our literature review was to identify and draw together key and critical perspectives on aspects of an emerging field; therefore, our approach to selection of sources favoured thematic salience. In order to scope the size of the potentially relevant literature we conducted searches using Google Scholar. While we acknowledge Google Scholar itself is not free from algorithmic biases, studies have found that it indexes a wider number of journals as well as other sources and research outcomes than widely-used proprietary scholarly databases, thereby surfacing reports and other types of grey literature (Martín-Martín et al., 2018), which contain essential criticism and ideas which help to broaden the inclusion of underrepresented voices in our review. We started our search by using the search string (“data ethics” OR “digital ethics” AND “education” OR “course” OR “classes” OR “teaching”), and specified in the review process the search to include additional sources of promising research areas.
We excluded sources that did not meet our broad thematic requirements. Thus, we tended to exclude articles which focused on sector or discipline-specific areas of knowledge (e.g. health, engineering, computer science) as these generally focus on the application of frequently discussed concepts in a given context, and also, literature reviews that did not reveal new significant findings in the field (Hammersley, 2020; Powell et al., 2022). When assessing quality of the sources, we kept in mind that grey literature (such as reports by international and independent organisations) may not strictly meet the highest standards of academic rigour (e.g. anonymous peer review), but nonetheless contain essential criticism and ideas. We therefore opted to judge quality on a case by case basis, instead of using blanket criteria, to incorporate a wide range of underrepresented voices in our review.
Thematic analysis and collection of ethical principles
Thematic analysis was performed following Braun and Clarke (2006) in order to distil a set of emerging key issues and topics for further attention. The preparation phase consisted of collecting suitable data for content analysis, organising the data, and selecting the unit of analysis, which in this case were the discussion of data ethics in course descriptions, data ethics frameworks, and academic literature. A qualitative analysis of textual data was conducted using this technique which involves the identification of core concepts through the review of the frequency of units of meaning, indicators, keywords and patterns in texts (Krippendorff, 2004). To identify the main themes in our data we performed a deductive analysis for the syllabi and an inductive analysis for the data ethics frameworks and the academic literature. As the syllabi mainly mentioned well-known terms to attract learners, we made a deductive analysis. In contrast, for frameworks and academic literature, an inductive analysis was chosen as there is no consensus on the main categories or principles under discussion, and because the identification of underrepresented ideas and emerging themes was part of our research aims (Elo & Kyngäs, 2008; Vaismoradi et al, 2013). To reduce biases, the themes identified by one author were reviewed and discussed by the other two authors until consensus was reached (Elo et al, 2014). Here we made use of our expertise and background knowledge as a research team coming from different disciplines: library and information science, media and communication studies, education and pedagogy, and applied philosophy.
As a last step, to develop the ethical framework we collected from all three sources action-guiding ethical principles. We subsumed closely related ethical principles, such as “do not harm” and “non-maleficence”, or “fairness” and “justice”, in a single principle by using the most-commonly referred to term. In cases of doubt, we discussed this synthesis between the authors until consensus was reached.
In our sources, data ethics was mostly understood as the process undertaken to ensure responsible and sustainable data practices across its whole cycle, from collection and management to analysis and publication, to understand the uses and risks of present and future uses of data. We observed that despite a broad consensus on the need for an ethical lens when examining the potential uses of data, the different sources placed a different emphasis on how and why we should address ethical issues.
Analysis of research methods and data science syllabi
To understand how ethics and data ethics were taught in research methods and data science courses, we reviewed the details of the curricula including the reading lists to identify inclusions of bibliographic references to data ethics, critical approaches to the use of data, or related elements such as general ethics, ethics of research or research data management. Hereby we observed that quantitative courses tend to lack scholarly and practical literature on these topics. In the case of data science syllabi, the core focus is the technical skills needed to work with data, which reflect industry practice and priorities. The geographic distribution of the syllabi reviewed and levels at which courses were taught are presented in Table 1.
The distribution of data ethics elements can be seen in Table 2.
In general terms, we did not find significant differences between the variables, but it is relevant to report that overall 52% have a unit on research ethics, which tend to focus in particular on the need to gain informed consent from participants, including the provision of detailed information about the meaning of their participation and what will be done with the information collected. However, only 16.8% go deeper by addressing issues related to data ethics, including ethical norms on data creation, collection, management, analysis, preservation, and more complex legal issues, such as privacy and data protection.
In research methods courses most of data ethics is taught in quantitative courses. However, the number of bibliographic references on data ethics is slightly higher in qualitative than in quantitative courses. Yet, at the same time, research methods courses are lacking pedagogic approaches to develop a thorough understanding of data ethics across the whole research process, from research design to science communication, that go beyond issues of anonymity. The ethics elements of these courses tend to focus on research ethics and integrity, critical appraisal of data, and analysis and interpretation of quantitative and qualitative data. Most of the reviewed syllabi structure research methods courses in introductions to research; development of hypothesis; conceptualising and conducting a research proposal; literature reviews; quantitative and qualitative research designs, methods, instruments; data analysis and presentation, and the importance of research ethics including research integrity (highlighting data fabrication and falsification). Some syllabi include elements of ethnographic observation; exploratory interviewing and focus groups; theory of science; lab design of experiments; field and natural experiments and principles and techniques of statistical analysis. Overall, there is limited presence of topics such as evaluation of ethical dilemmas, data collection, sampling, and power asymmetries and issues such as privacy.
The data science programmes tend to give only limited attention to personal data agency (consent, privacy), or the socio-technical relationships between data and power (justice, sovereignty), that is, the relations of individuals as members of specific communities with today’s ubiquitous data-intensive technologies. Furthermore, we noticed little information on ethical issues derived from uses of data, anonymisation, storage, distribution, management, reuse and publication of results, as teaching emphasises on legislation and legal frameworks instead of the ethical implication of data misuses. We observed that the data science curricula have a strong technocentric nature at the cost of humanistic approaches to data and data-driven technologies. There seems to be little discussion of how to involve participants and communities in research design, the challenges of fair recruitment and inclusion of vulnerable and subordinate people, while at the same time considering their well-being and anonymity.
Both types of curricula are missing opportunities to enhance the skills of learners in research ethics beyond informed consent and privacy, as there is little discussion on the syllabi and reading lists on issues related with other principles of data ethics, responsible uses of data, data power dynamics, data biases, data justice, data feminism, and indigenous data sovereignty, or other approaches which support learners and educators to situate research in the range of emerging ethical issues related with the datafication of society.
Analysis of the data ethics frameworks
In the second stage of our analysis, we reviewed 12 data ethics frameworks from academic, private and civil society sectors. We list the overarching ethical principles within these frameworks in Table 3.
The most frequently found principles are respect for autonomy (in terms of consent) and privacy, which are mostly discussed in the context of big-data and machine-learning, raising awareness of the need to inform users of how their data is being collected, processed and protected. Another frequently mentioned concern relates to data governance, explained as the set of rules that should apply to all to regulate data-led activities and to put people before data, which is understood as using data to solve social problems and to improve the quality of life.
The frameworks developed in academic contexts emphasise that human beings have to be placed at the centre of data led projects, and encourage examining or even challenging the power dynamics embedded in data ecosystems, while promoting transparency and responsible uses of data. Government-led frameworks have strong human-centred values, and tend to have a quite important legal dimension, aiming at supporting organisations to define and understand public benefits in data projects to ensure these act with honesty, integrity, and humility, respecting the public, individuals, and communities. The other key principles are transparency and accountability, condemning loopholes and impunity. The civil society frameworks suggest organisations to guarantee the security of data, individuals, and algorithms to prevent unauthorised uses of data, and to acknowledge and mitigate unfair bias throughout all aspects of data work. Likewise, frameworks developed in the private sector encourage organisations to give the highest priority to the persons behind the data and to adhere to data governance and AI model governance to advance proper AI ethics while advocating for ensuring privacy, accountability and legal compliance, while promoting the idea of self-regulation following internal codes of good practice.
The common elements of these frameworks are having a human-centred approach to uses of data, ensuring that individuals and organisations comply with the law, and making use of data governance protocols to ensure privacy and prevent biases. Although seldom stated explicitly, we can observe a broad concern that irresponsible practices and misuse will lead to tighter regulations and hesitancy to share data, limiting the freedom to work with data and increasing administrative burdens.
In order to look beyond the conceptualisation of ethics covered by existing frameworks and incorporate critical perspectives, we reviewed a wide range of academic literature on data ethics, including critical approaches to data and critical data literacy. We found a general consensus on the need to equip students with critical data and ethics literacies to prepare them to understand diverse phenomena such as Artificial Intelligence (AI), algorithmic discrimination through automated driven decisions, digital poverty, surveillance capitalism or platform governance, and the impact of interactions with data-driven systems on themselves and others, so people can assess, anticipate, and respond to social issues that are related to the collection, processing and use of data (Al-Nuaimi, 2020; Buckingham & Crick, 2016; Kumar et al, 2020; Powell, 2018; Sloane, 2019; Wheeler, 2018).
Despite the wide range of emerging issues, not all topics received equal consideration. Our literature review allowed us to identify a number of recurrent themes that are important for data ethics:
Algorithmic decision making tend to adversely affect those coming from lower-income households and neighbourhoods. This kind of behaviour is discussed as automating poverty or automating inequality, where AI is used to categorise groups, or to assign or remove services such as unemployment benefits, child support, housing and food subsidies, imposing systemic oppression (Bhaumik et al., 2006; Davies, 2020; Eubanks, 2018; Kleinberg et al, 2018; Sandvig et al, 2014). Thus, it is important to protect the rights of vulnerable persons, and be vigilant to the ways in which they may be impacted by automated decisions which increasingly determine, showcase, predict and map poverty, and depicting groups in a negative way, depending on the school they attend or where they live (Atenas & Havemann, 2019; Goldkind et al., 2021; Lo Piano, 2020; UNICEF, 2019, 2020).
The opacity of algorithms creates black boxes, and one of the key arguments towards the need to have regulatory frameworks is the problem of the “Racist Robots”, that for example are leading to consumer lending discrimination, or preventing certain groups obtaining visas to visit or live in countries. Moreover, they can harm marginalised groups, for example, through racially profiling predictive policing which then leads to longer incarceration (Alaieri & Vellino, 2016; Bartlett et al, 2019; Brantingham, 2017; Chander, 2017; Hepworth & Church, 2018; Khalifa et al, 2014; Kuzey et al., 2019; Roth, 2010; UNESCO, 2019).
Sex, gender and sexuality
Women, gendered and sexual minorities can be adversely affected by algorithmic decision-making in every aspect of their lives, including access to health, services and the labour market, for example, in clinical decisions, or psychometric tests. Thus, ethical use of data must consider the experiences and needs of women and members of LGBT+ communities (Asplund et al, 2020; Beaman et al, 2009; Cirillo et al, 2020; Kleinberg et al, 2018; Lambrecht & Tucker, 2019; Ruberg & Ruelos, 2020; Zou & Schiebinger, 2018). Furthermore, there needs to be greater awareness of the biases against female researchers and even against the very research that identifies such biases as bad scientific practices or injustices, and what one can do about it (Cislak et al., 2018; Orgeira-Crespo et al, 2021).
Businesses, employers, educational organisations and governments are engaging in surveillance capitalism (Zuboff, 2015), that is the ownership of rights for secondary use of data for profit-making, to monitor our behaviour online, in shops, at work, and while studying and taking exams. Personal data is continuously captured and tracked via engagements with near-ubiquitous technology giants; a growing number of states are deploying advanced AI tools to monitor, track, and surveil citizens to accomplish a range of murky policy objectives (Andrejevic & Selwyn, 2020; Azoulay, 2019; Feldstein, 2019; Introna & Wood, 2004; Newlands, 2021).
Closely connected to surveillance capitalism, AI has been used to target, influence and manipulate voters through social media, acting to further polarise political opinions and fuel anger and paranoia. Personal data and algorithmic social media vectors deliver targeted propaganda messages which for many, are the main source of ‘news’ about the political sphere. Consequently, radicalisation and conspiracy-theorising have become widely normalised, threatening democratic processes, indicating a need for better regulatory frameworks (Badawy et al., 2019; Bolsover & Howard, 2019; Crain & Nadler, 2019; Hood & Margetts, 2007; Véliz, 2020; Woolley & Howard, 2016).
There are elements of life people want to keep as part of our private sphere and others which people are willing to make public to facilitate social life and maintain public institutions (Rabotnikof, 2005). As data in the digital age can be easily accessed over large distances in time and space, it has become a central concern for individuals and democracies (Gstrein & Beaulieu, 2022). As it is difficult to assess what consequences data sharing has, it has led to calls to minimise the amount of data collected (Véliz, 2020) and to establish codes of conduct in regard to what data can be shared, with whom and under what specific contexts, in terms of contextual integrity (Nissenbaum, 2004; Zimmer, 2018). Furthermore, search engines make necessary new methods for controlling which parts of life one wants to share and with whom, and design features that facilitate control over one’s privacy, as scholarship on boundary regulation emphasises (McDonald & Forte, 2020).
Emerging from black feminist critique, the theory of intersectionality notes that each person’s identities are multiple, and therefore facilitates analysis of how people can be differentially affected by multiple layers of discrimination (Crenshaw, 1989). In a data context, data infrastructures are being introduced to predict socioeconomic behaviours, via collection and cross referencing of data points including socio-economic status, race, gender, and neighbourhood. Such processing of data aims to predict how likely certain students are to fail or succeed at school, or how much a person must pay for their car insurance, but worse, it is used in police work, to profile and predict the future criminality of members of marginalised groups. These issues have been taken up by D’Ignazio and Klein (2020) in designing their data feminism framework, which aims to embed principles of feminist theory and equity into data-related projects, as the less we understand how such systems work, the more likely it is that historically disadvantaged groups will continue to suffer from automated negative biases (McDonald & Pan, 2020).
Certain technical, social and political conditions may vastly expand the possible uses and impact of data. A holistic analysis needs to go beyond concentrating on the novelty of data-intensive technologies, and study the relations in which the different entities using data stand and what these can actually do with data under the conditions and circumstances they interact (Stahl, 2021). This perspective allows us to identify data practices and data uses that strive and become dominant.
Levelling the field
The emergence of more powerful technologies capable to process increasingly more data to gain knowledge about human activities, are generating social asymmetries between those who own the tools, the skills, and the computational power and the subjects whose data are subject to these applications (Belbis & Fumega, 2019; Zwitter, 2014). Thus, data-led research projects must refer to and adhere to the principles and values in which human rights and personal data protection laws are based, to ensure that the uses of data do not compromise or further harm vulnerable and marginalised groups (Azoulay, 2019; Bogroff & Guegan, 2019; Kleinberg et al., 2018; Lo Piano, 2020; Sandvig et al, 2014; Zuboff, 2015).
Developing guiding principles
A data ethics framework must be guided by a series of propositions and guidelines to which any data research-led project or activity must adhere. This will lead to actively design fair and less biased research and motivate students to learn, from the very beginning, the value of data protection and data agency. For this, raising awareness of the role of an ethical common ground when conducting research with data considering elements such as empathy, social justice and social good will be critical (Chang & Gray, 2013; Eisen & Parker, 2004; Stockley & Balkwill, 2013; Strohmetz & Skleder, 1992).
Recognising the diversity of values
Data literacy programmes should be supported by a range of social values to address the diversity in a pluralistic society. The early literature on value-sensitive design has identified privacy, non-discrimination, autonomy, and safety as widely shared values concerning information technologies. Yet, at the same time Friedman et al. (2008) note that they may conflict in practice and need to be balanced. For instance, monitoring non-discrimination often requires some invasion of privacy. To identify the different values, their relationship to each other and their importance, we need to build an ethical framework that is tailored to the needs of a datafied society.
Interest in data ethics is increasing rapidly and we can see a strong diversification of themes and ethical approaches. Scandals from industry and politics have triggered strong research interest in the field, enriching the discussion with new case studies and identifying new types of threats to individuals, communities and democracy. This research area also shows a deep interaction between academic scholarship and activism, with academics becoming activists, collaborations between activists and academics, and activists effectively using academic scholarship to back their arguments.
Identified action-guiding principles
In the analysed syllabi, ethical frameworks and scholarly literature we found repeated reference to action-guiding principles. We have synthesised the large number of ethical demands and appeals into eight action-guiding principles that constitute our extended data ethics framework (Table 4).
The syllabi mostly mention two principles: privacy and respect for autonomy, usually limited to issues of informed consent.
Depending on how broad “action-guiding principles” are understood, there was another element that could be included in this framework. As research methods and data science syllabi, and ethical framework were strongly user-centric, there was repeated reference to “research integrity”. This was mainly understood in a narrow sense, as a condemning data fabrication, data manipulation and data falsification. Due to the negative consequences of these acts on science and ultimately society, teaching materials and ethical frameworks repeatedly made references to scientific integrity, professional responsibility and abiding to professional rules of conduct. Academic literature however already deals with issues of data fabrication, manipulation and falsification under principles of “promote fairness”, “no harm” and “address bias”.
Lastly, it was interesting to observe that syllabi, ethical frameworks and academic literature tended to concentrate on a smaller set of principles (or even a single principle), or place emphasis on different principles in separate publications. Our results show the main emphases within the cited documents.
Discussion: an ethics as methods framework for critical data literacy and research methods
To address these wide range of themes and aims in the context of research-based learning activities and research methods courses, we consider that a critical approach to the ethical values concerning how we interrogate issues related to data is needed. An approach to expanding educators and learners capacities to identify and analyse ethical issues is to adopt an understanding of “ethics as methods”, as proposed by Markham et al. (2018), who note that: “Although ethics is often considered a philosophical stance that precedes and grounds action, it is a value-rationality that is actually produced, reinforced, or resisted through practice. Very quickly, indeed immediately, ethics, when practiced, becomes a matter of method.” (p. 2). In other words, learning how to “do” ethics is required if students are to identify ethical issues, analyse them and propose solutions in line with ethical norms.
A widely used approach to teach and learn ethical reasoning is the acquisition of ethical principles (Beauchamp & Childress, 2019; DeGrazia & Millum, 2021). Ethics training can start by acquiring a very basic set of principles that can be expanded. In follow-up courses and self-learning these principles can be analysed in more depth and expanded in number (see Table 4), and the relationship between these principles can be examined in constellations of increasing complexity. In this sense, data ethics can be framed within the idea of critical data literacy. When students associate a concept to a series of ethical considerations they start to think about such concepts as action-guiding principles, and they can put ethics into practice across the whole research data cycle, from the development of tools for data collection, gathering data from different sources and groups, managing and safeguarding data, analysing data and communicating their findings using an ethical approach to data storytelling and scientific communication.
Thus, for instance, by starting a discussion with a widely known concept, such as privacy, ethics teachers can motivate students to think about the many possible ethical issues that can be associated with the concept. Privacy as a principle is associated with a specific idea on how such a good is to be treated (Véliz, 2019). When other principles are added to discussion, such as respect for autonomy, students can develop on the basis of concrete examples how the principles interact. For instance, older adults are often very open to accept the privacy intrusive nature of smart sensors that monitor their movements, and can alert emergency services in cases of falls, as they are keen to regain autonomy (Predel et al, 2022). In contrast, public figures and activists have a strong interest to keep a high level of privacy, even at the cost of losing some of their autonomy, to reduce possible harms.
As we saw in Table 4, there is no definitive understanding or agreement on a fixed set of principles that should govern data ethics; rather, various authors and organisations are engaged in a struggle to set the agenda and expand or delimit the boundaries of the ethical analysis. Much of ethics training needs to be adapted to the needs and context of the learners and the society(-ies) they live and work in. Our analysis of syllabi, data ethics frameworks and academic literature has nonetheless shown that there are a few key issues that require special attention in HE: power structures, vulnerabilities and relationships, and social responsibility.
When it comes to power structures, a significant concern is that ultimately, dominant organisations are more capable of defining what ethical practices comprise (van Maanen, 2022; Washington & Kuo, 2020). It therefore becomes imperative that students learn at an early stage about the different interest groups that exert influence in data ethics discussions, so that they can identify potential conflicts of interest. Educational programmes need to build capacities in conducting research through a critical and ethical framework to enable them to challenge data power structures, by addressing structural social problems through an interdisciplinary and social justice approach (Iliadis & Russo, 2016; Dalton et al., 2016; Metcalf et al., 2016; Burns et al., 2018; BERA, 2018; Timmermann, 2018; Atenas et al., 2020; Mtawa & Nkhoma, 2020; Decuypere, 2021). Hence, for educators, a guiding question becomes: how can we ensure that training in data enables students to identify and challenge power asymmetries? Familiarity with the principle of “challenging power structures” is a first step in thinking about the ethical dimensions of power and power abuses related to data harvesting and use.
Teaching ethical research practices requires activities that promote respect for autonomy, privacy and dignity of individuals, groups and communities and how to equally distribute the risks and benefits of the research. It also requires developing a sensitivity to intersectional considerations that negatively affect vulnerable groups (McDonald & Pan, 2020) and factors that give undeserved advantages over others to those already well-off. An ethics of care perspective provides sensitivity to these issues, positioning ethics as relational, contextualised, embodied and realised through practices rather than residing in stand-alone principles. Care is considered politically, that is, in relation to the intersecting hierarchies of power and privileges that are inherent in the context of modernity. This poses further ethical challenges in terms of race, indigeneity, class and gender. A care ethics approach asks us to reflect on the question of privilege while also creating spaces to build solidarities. Using an ethical framework to enable the critical understanding of the wide spectrum of data issues in the context of HE, can therefore support educators in assessing their own teaching and research practices, and foster participatory and collaborative learning activities, co-creating knowledge for social transformation (Atenas, 2021; Atenas et al., 2021).
Conducting research with data about people is a privilege, not a right (Atenas et al., 2020; Carpenter, 2018; Dencik & Sanchez-Monedero, 2022; Floridi & Taddeo, 2016). When we conduct research with human data, we are not simply examining data points but entering into peoples lives, places and stories, their culture and beliefs. Therefore, when entering the field, we must acknowledge people and communities as subjects not objects of research. Research educational programmes must be designed to acknowledge ethical boundaries following established principles of good scientific practice and research ethics: respect for persons, beneficence and justice putting the common good at the heart of research (Carpenter, 2018; Oates, 2021). Graduates entering the commercial space, where the profit imperative may be seen to come into conflict with their ethical training, must also be equipped to make the case for ethical practice as ultimately, not only socially responsible, but better business practice. The unethical use of data will undermine the willingness to share and curate data, and expand protective legislation, thereby reducing the amount of data available for use and increasing the administrative burden to clear freedom to operate.
Lastly, ethical practice must learn to operate within a context of constant change, with the continuous development of technologies, and evolution in the rule of law and accountability of data exploiters. This requires continuously adapting ethical frameworks to emerging challenges. Under this context, it is also important that ethics training leads to assuming a certain degree of social responsibility. As Johnson (2014) and Metcalf et al. (2016) note, teaching critical data literacy involves integrating case studies with practical work, fostering collaboration, co-creation and collective responsibility by examining social privileges in data and the norms of data systems. It is important that data-led research and learning activities are designed to address inequalities, to improve quality of life, to explore issues that may be harming a community, and also to improve data governance, as it is key that people acquire the skills to participate in developing policy frameworks that go beyond data protection, and provide a fair, safe, unbiased and equitable data landscape, regulating what the public and private sectors can do with data. Data should help identify deeply-anchored inequities and emerging cases of discrimination and malpractices, and not serve as a tool to perpetuate injustices.
We consider that ethics, and more specifically, data ethics in the context of teaching research methods, should be actionable. Thus, to develop critical data literacies and research skills in HE, ethics should be a transversal element rather than a formality or ‘tick-box’ exercise, hence embedding the concept of ethics as a method, guiding research from design through to communication and every stage in between (Markham, 2006; Markham et al., 2018).
In designing a curriculum for research methods and data science courses, educators should foster an understanding and discussion of ethical norms and dilemmas, and thereby, of potentially beneficial and harmful uses of data. While we have shown a clear preference for using an extended list of ethical principles in our synthesis of the different frameworks, even the use of shorter sets of principles is a substantial improvement. As most courses in use focus on the need for informed consent, thereby placing individual autonomy at the centre of the question of ethics, we strongly recommend expanding to a further set of principles to also discuss issues of social justice.
The use of ethical principles in the training of medical students has shown great success with mastering four principles: respect for autonomy, beneficence, non-maleficence and justice (Beauchamp & Childress, 2019). Based on over four decades experience of teaching ethical principles in the healthcare context, building upon the Belmont Report (National Commission for the Protection of Human Subjects of Biomedical & Behavioral Research, 1979), educators should be free to limit the set of principles for introductory courses to three or four, and expand the set of principles as course participants become more proficient in analysing the social issues of data practices. The selection of principles should depend on the ethical training of the educators, the time available and the pressing social issues course participants may encounter. The selection of ethical principles should however cover as a minimum one principle from each of the following three dimensions: (i) to defend primarily individual interests (respect for autonomy, privacy), (ii) to fight injustice (do not harm, address bias) and (iii) to promote collective well-being (promote fairness, address equality, promote sovereignty, challenge power structures). These three dimensions are based on the Belmont Report’s original emphasis of the ethical principles commonly thought in bioethics—respecting autonomy, reducing harms and promoting social justice—and have proven to be an adequate minimum standard for the ethical training of health workers which can be adapted to other professions. Learning about these three dimensions may motivate taking additional ethics courses and facilitates self-learning by being able to position additional ethical principles in a basic normative framework and identify further applications.
Our decision to opt for a non-systematic review and carry out a thematic analysis inductively comes at the cost of a certain bias towards our own professional interests and disciplinary perspectives. We nonetheless defend our approach as it facilitates the inclusion of voices which are often marginalised in the academic discussion or overshadowed by reports of financially strong institutions (Powell & Koelemay, 2022). Furthermore, databases that are reputable in academic venues are often dominated by for-profit publishing houses and also not free from algorithmic biases. We therefore judge that our analysis can complement previously published systematic reviews by diversifying the scope of ethical perspectives.
Our purpose in this paper has been to both establish the need for, and outline, a set of action-guiding ethical principles for embedding data ethics as a core element in teaching data skills within research methods and data science training. Thus, methods and data literacy programmes should be teaching ethics beyond informed consent, as ethics itself must be considered a research method and a research praxis, making ethics actionable knowledge (Marco & Larking, 2000; Simon, 2015; Nielsen, 2016; Bonatti, et al. 2018; Decuypere, 2021). We suggest incorporating a selection or the complete set of described ethical principles in research methods courses to explore with students issues of ethics and data, including through data-led learning activities across the disciplines.
We consider, as suggested by Reijers, et al. (2018) that any research should be carried out approaching ethics as: (i) ex ante methods, to understand how data is used and its potential impact; (ii) intra methods, to understand the impact of data at different levels; and (iii) ex post methods, to understand how the research has had an impact in different communities. This approach can build an understanding of the core ethical elements within the discipline of study, including the codes of conduct in professional fields, with attention to the fundamental distinction between the ethics of data practices and laws which govern them, because an action may be legal but unethical, or illegal but ethical. The notion that these are the same can be an accidental or deliberate cause of confusion or obfuscation. Students therefore need to become acquainted with ethical practice both within and beyond legal and regulatory frameworks, as ethics can be understood as a method, a procedure, and a perspective to guide decisions around how to study and analyse data as social phenomena.
As scholars working on indigenous data sovereignty have repeatedly emphasised, we consider that data and research literacy should explore participatory and inclusive research design, involving those who will provide data or be affected by the research, paying attention to vulnerable communities, so that biases and prejudices that shape the presentation of results are minimised. Key to achieving this is to acknowledge that humans are not objective (Saini, 2020) and systematically incorporate diverse viewpoints. A practical way to do this is to co-construct the data with the participants of the study.
Thus, it is key to promote interdisciplinary dialogues and seek input from those who deal with sensitive topics, high-risk research situations, and/or vulnerable populations in the context of the epistemic and axiological shifts, where we are all potentially vulnerable, and all data are potentially sensitive (Tiidenberg, 2018). We consider that it is important that curriculum design is person- and community- centred, this will include supporting people in finding ways to be in control and empowered by their data, as well as challenging pervasive power dynamics and making data users accountable for their actions.
Availability of data and materials
No additional data is associated to this research.
Accenture. (2016). Universal Principles of Data Ethics: 12 Guidelines for Developing Data Ethics Codes. Accenture.
Ai-min, Q., & Jia, P. (2015). Right to Data, Data Sovereignty and the Basic Principle of Big Data Protection. Journal of Soochow University (Philosophy & Social Science Edition), 1. Retrieved from http://en.cnki.com.cn/Article_en/CJFDTotal-SZDX201501013.htm
Alaieri, F., & Vellino, A. (2016). Ethical decision making in robots: autonomy, trust and responsibility. In A. Agah, J. J. Cabibihan, A. Howard, M. Salichs, & H. He (Eds.), International conference on social robotics (pp. 159–168). Springer. https://doi.org/10.1007/978-3-319-47437-3_16
Al-Nuaimi, M. N. (2020). Organisational Ethics of Big Data: Lessons Learned from Practice. In Paradigm Shifts in ICT Ethics: Proceedings of the ETHICOMP* 2020 (pp. 371–374). Universidad de La Rioja. Retrieved from https://dialnet.unirioja.es/descarga/libro/768026.pdf#page=373
Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014
Asplund, J., Eslami, M., Sundaram, H., Sandvig, C., & Karahalios, K. (2020). Auditing race and gender discrimination in online housing markets. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 14, pp. 24–35). Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/7276
Atenas, J., Havemann, L., Timmermann, C., & Kuhn, C. (2021). Understanding critical data literacy beyond data skills - A workshop for the GO_GN Network. https://doi.org/10.5281/zenodo.5651807
Australian Government. (2019). Australia’s AI Ethics Principles. Retrieved from https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles
Atenas, J., & Havemann, L. (2019). Open data and education. In T. Davies, S. Walker, M. Rubinstein, & F. Perini (Eds.), The state of open data: Histories and horizons. African Minds and International Development Research Centre.
Atenas, J., Havemann, L., & Timmermann, C. (2020). Critical literacies for a datafied society: Academic development and curriculum design in higher education. Research in Learning Technology. https://doi.org/10.25304/rlt.v28.2468
Atenas, J. (2021). The datafied present and future. In C. Kühn, J. Atenas, & L. Havemann (Eds.), Understanding data: Praxis and politics. HDI Data, Praxis and Politics.
Azoulay, A. (2019). Towards an ethics of artificial intelligence. UN Chronicle, 55(4), 24–25.
Badawy, A., Lerman, K., & Ferrara, E. (2019). Who falls for online political manipulation? In Companion Proceedings of The 2019 World Wide Web Conference (pp. 162–168). https://doi.org/10.1145/3308560.3316494
Ball, S. J. (2015). Education, governance and the tyranny of numbers. Journal of Education Policy, 30(3), 299–301. https://doi.org/10.1080/02680939.2015.1013271
Bartlett., R, Morse, A., Stanton, R., & Wallace, N. (2019). Consumer-lending discrimination in the FinTech era (No. w25943). National Bureau of Economic Research. Retrieved from https://www.nber.org/papers/w25943
Beaman, L., Chattopadhyay, R., Duflo, E., Pande, R., & Topalova, P. (2009). Powerful women: Does exposure reduce bias? The Quarterly Journal of Economics, 124(4), 1497–1540. https://doi.org/10.1162/qjec.2009.124.4.1497
Beauchamp, T. L., & Childress, J. F. (2019). Principles of biomedical ethics. Oxford University Press.
Belbis, J., & Fumega, S. (2019). Gobierno Abierto y Datos Abiertos. Estado Abierto a Través De Datos Abiertos. https://doi.org/10.5281/zenodo.3243389
British Educational Research Association [BERA] (2018). Ethical Guidelines for Educational Research, British Educational Research Association. Retrieved from https://www.bera.ac.uk/researchersresources/publications/ethicalguidelines-for-educational-research-2018
Berman, G., & Albright, K. (2017). Children and the data cycle: Rights and ethics in a big data world. Retrieved from https://arxiv.org/abs/1710.06881
Bertino, E., Kundu, A., & Sura, Z. (2019). Data transparency with blockchain and AI ethics. Journal of Data and Information Quality (JDIQ), 11(4), 1–8. https://doi.org/10.1145/3312750
Bezuidenhout, L., Quick, R., & Shanahan, H. (2020). “Ethics when you least expect it”: A modular approach to short course data ethics instruction. Science and Engineering Ethics. https://doi.org/10.1007/s11948-020-00197-2
Bhaumik, S. K., Gang, I. N., & Yun, M. S. (2006). A note on decomposing differences in poverty incidence using regression estimates: Algorithm and example. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=928808
Bogroff, A., & Guegan, D. (2019). Artificial Intelligence, Data, Ethics An Holistic Approach for Risks and Regulation. University Ca'Foscari of Venice, Dept. of Economics Research Paper Series, 19. Retrieved from https://halshs.archives-ouvertes.fr/halshs-02181597/document
Bohman, J. (2005). Critical Theory. In E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2021 Edition). Retrieved from https://plato.stanford.edu/archives/spr2021/entries/critical-theory/
Bolsover, G., & Howard, P. (2019). Chinese computational propaganda: Automation, algorithms and the manipulation of information about Chinese politics on Twitter and Weibo. Information, Communication & Society, 22(14), 2063–2080. https://doi.org/10.1080/1369118X.2018.1476576
Bonatti, P. A., Bos, B., Decker, S., Fernandez Garcia, J. D., Kirrane, S., Peristeras, V., Polleres, A., & Wenning, R. (2018). Data privacy vocabularies and controls: Semantic web for transparency and privacy. 1–1. Retrieved from https://research.wu.ac.at/files/21761326/SW4SG_2018.pdf
Brand, J., & Sander, I. (2020) Critical data literacy tools for advancing data justice: A guidebook. Data Justice Lab. Retrieved from https://datajusticelab.org/wp-content/uploads/2020/06/djl-data-literacy-guidebook.pdf
Brantingham, P. J. (2017). The logic of data bias and its impact on place-based predictive policing. OSJCL Ohio State Journal of Criminal Law, 15, 473–486.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Bronner, S. E. (2009). Critical theory. In the International Encyclopedia of Revolution and Protest. https://doi.org/10.1002/9781405198073.wbierp0418
Buckingham, S., & Crick, R. D. (2016). Learning analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21. https://doi.org/10.18608/jla.2016.32.2
Buenadicha, C., Galdon, G., Hermosilla, M. P., Loewe, D., & Pombo, C. (2019). La Gestión Ética de los Datos. Por qué importa y cómo hacer un uso justo de los datos en un mundo digital. BID. Retrieved from http://www.codajic.org/sites/www.codajic.org/files/La_Gesti%C3%B3n_%C3%89tica_de_los_Datos.pdf
Burns, R., Dalton, C. M., & Thatcher, J. E. (2018). Critical data, critical technology in theory and practice. The Professional Geographer, 70(1), 126–128. https://doi.org/10.1080/00330124.2017.1325749
Carpenter, D. (2018). Ethics, reflexivity and virtue. In R. Iphofen & M. Tolich (Eds.), The sage handbook of qualitative research ethics (pp. 35–50). SAGE.
Chander, A. (2017). The racist algorithm? Michigan Law Review, 115(6), 1023–1045. https://doi.org/10.3316/agispt.20190905016562
Chang, R. L., & Gray, K. (2013). Ethics of research into learning and teaching with Web 2.0: Reflections on eight case studies. Journal of Computing in Higher Education, 25(3), 147–165. https://doi.org/10.1007/s12528-013-9071-9
Cislak, A., Formanowicz, M., & Saguy, T. (2018). Bias against research on gender bias. Scientometrics, 115(1), 189–200. https://doi.org/10.1007/s11192-018-2667-0
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digital Medicine, 3(1), 1–11. https://doi.org/10.1038/s41746-020-0288-5
Corple, D. J., & Linabary, J. R. (2020). From data points to people: Feminist situated ethics in online big data research. International Journal of Social Research Methodology, 23(2), 155–168. https://doi.org/10.1080/13645579.2019.1649832
Crain, M., & Nadler, A. (2019). Political manipulation and internet advertising infrastructure. Journal of Information Policy, 9, 370–410. https://doi.org/10.5325/jinfopoli.9.2019.0370
Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics, University of Chicago Legal Forum, 1989, 8. Retrieved from https://chicagounbound.uchicago.edu/uclf/vol1989/iss1/8
Dalton, C. M., Taylor, L., & Thatcher, J. (2016). Critical data studies: A dialog on data and space. Big Data and Society, 3(1), 1–9. https://doi.org/10.1177/2053951716648346
DataEthics.eu. (2017). Data ethics principles. Retrieved from https://dataethics.eu/data-ethics-principles/
Data for Democracy. (2021). Global Data Ethics Pledge (GDEP). Retrieved from https://github.com/Data4Democracy/ethics-resources
Datenethikkomission. (2019). Opinion of the Data Ethics Commission. Berlin. https://www.bmjv.de/SharedDocs/Downloads/DE/Themen/Fokusthemen/Gutachten_DEK_EN_lang.pdf
Davies, D. (2020). This year's A-level results are a fiasco—But the system was already broken. The Guardian. Retrieved from https://www.theguardian.com/commentisfree/2020/aug/15/a-level-results-system-ofqual-england-exam-marking
de Almeida, C. P. B., & de Goulart, B. N. G. (2017). How to avoid bias in systematic reviews of observational studies. Revista CEFAC, 19(4), 551–555. https://doi.org/10.1590/1982-021620171941117
Decuypere, M. (2021). The topologies of data practices: A methodological introduction. Journal of New Approaches in Educational Research, 10(1), 67–84. https://doi.org/10.7821/naer.2021.1.650
DeGrazia, D., & Millum, J. (2021). A theory of bioethics. Cambridge University Press.
Dencik, L., Hintz, A., & Cable, J. (2016). Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society. https://doi.org/10.1177/2053951716679678
Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review. https://doi.org/10.14763/2022.1.1615
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.
Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., & Sutton, A. J. (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Medical Research Methodology. https://doi.org/10.1186/1471-2288-6-35
Eisen, A., & Parker, K. P. (2004). A model for teaching research ethics. Science and Engineering Ethics, 10(4), 693–704. https://doi.org/10.1007/s11948-004-0047-z
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative content analysis: A focus on trustworthiness. SAGE Open. https://doi.org/10.1177/2158244014522633
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Feldstein, S. (2019). The global expansion of AI surveillance (Vol. 17). Washington, DC: Carnegie Endowment for International Peace. Retrieved from https://carnegieendowment.org/2019/09/17/global-expansion-of-ai-surveillance-pub-79847
Floridi, L. (2010). Information: A very short introduction. Oxford University Press.
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A. https://doi.org/10.1098/rsta.2016.0360
Foucault, M. (1980). Power/knowledge: Selected interviews and other writings 1972–1977, edited by C. Gordon. Pantheon.
Freire, P. (1968). Pedagogy of the oppressed. Bloomsbury.
Friedman, B., Kahn, P. H., & Borning, A. (2008). Value sensitive design and information systems. In K. EinarHimma & H. T. Tavani (Eds.), The handbook of information and computer ethics (pp. 69–101). Wiley.
Grant, L. (2022). Reconfiguring education through data: how data practices reconfigure teacher professionalism and curriculum. In: A. Hepp, J. Jarke, L. Kramp (Eds.), New perspectives in critical data studies: The ambivalences of data power. Springer.
Giroux, H. (2010). Rethinking education as the practice of freedom: Paulo Freire and the promise of critical pedagogy. Policy Futures in Education, 8(6), 715–721. https://doi.org/10.2304/pfie.2010.8.6.715
Global Indigenous Data Alliance. (2019). CARE Principles for Indigenous Data Governance. https://www.gida-global.org/s/CARE-Principles_One-Pagers-FINAL_Oct_17_2019.pdf
Goldkind, L., Wolf, L., & LaMendola, W. (2021). Data justice: Social work and a more just future. Journal of Community Practice, 29(3), 237–256. https://doi.org/10.1080/10705422.2021.1984354
Gstrein, O., & Beaulieu, A. (2022). How to protect privacy in a datafied society? A presentation of multiple legal and conceptual approaches. Philosophy & Technology. https://doi.org/10.1007/s13347-022-00497-4
Hammersley, M. (2020). Reflections on the methodological approach of systematic reviews. In O. Zawacki-Richter, M. Kerres, S. Bedenlier, M. Bond, & K. Buntins (Eds.), Systematic reviews in educational research (pp. 23–39). Springer Fachmedien Wiesbaden.
Hand, D. J. (2018). Aspects of data ethics in a changing world: Where are we now? Big Data, 6(3), 176–190. https://doi.org/10.1089/big.2018.0083
Heeks, R., & Shekhar, S. (2019). Datafication, development and marginalised urban communities: An applied data justice framework. Information, Communication & Society, 22(7), 992–1011. https://doi.org/10.1080/1369118X.2019.1599039
Held, V. (2006). The ethics of care: Personal, political, and global. Oxford University Press.
Hepp, A., Jarke, J., & Kramp, L. (2022). New perspectives in critical data studies. Springer.
Henderson, T. (2019). Teaching Data Ethics: We're going to ethics the heck out of this. In Proceedings of the 3rd Conference on Computing Education Practice (pp. 1–4). Retrieved from https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/16570/cep2019.pdf?sequence=1&isAllowed=y
Hepworth, K., & Church, C. (2018). Racism in the machine: Visualization ethics in digital humanities projects. DHQ: Digital Humanities Quarterly, 12(4). Retrieved from http://www.digitalhumanities.org/dhq/vol/12/4/000408/000408.html
Herschel, R., & Miori, V. M. (2017). Ethics & big data. Technology in Society, 49, 31–36. https://doi.org/10.1016/j.techsoc.2017.03.003
Hoffmann, A. L., Roberts, S. T., Wolf, C. T., & Wood, S. (2018). Beyond fairness, accountability, and transparency in the ethics of algorithms: Contributions and perspectives from LIS. Proceedings of the Association for Information Science and Technology, 55(1), 694–696. https://doi.org/10.1002/pra2.2018.14505501084
Hood, C. C., & Margetts, H. Z. (2007). The tools of government in the digital age. Macmillan International Higher Education.
Hummel, P., Braun, M., Augsberg, S., & Dabrock, P. (2018). Sovereignty and data sharing. ITU Journal: ICT Discoveries, 2. Retrieved from https://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I2-2018-11-PDF-E.pdf
Ienca, M., Ferretti, A., Hurst, S., Puhan, M., Lovis, C., & Vayena, E. (2018). Considerations for ethics review of big data health research: A scoping review. PLoS ONE. https://doi.org/10.1371/journal.pone.0204937
Iliadis, A., & Russo, F. (2016). Critical data studies: An introduction. Big Data and Society, 3(2), 1–7. https://doi.org/10.1177/2053951716674238
Introna, L., & Wood, D. (2004). Picturing algorithmic surveillance: The politics of facial recognition systems. Surveillance & Society, 2(2/3), 177–198.
Jo, E. S., & Gebru, T. (2020). Lessons from archives: strategies for collecting sociocultural data in machine learning. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 306–316). https://doi.org/10.1145/3351095.3372829
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
Johnson, J. A. (2014). From open data to information justice. Ethics and Information Technology, 16(4), 263–274. https://doi.org/10.1007/s10676-014-9351-8
Kazim, E., & Koshiyama, A. (2019). Data ethics principles: A comment on the house of lords report ‘regulating in a digital world’. SSRN. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3581754
Khalifa, M. A., Jennings, M. E., Briscoe, F., Oleszweski, A. M., & Abdi, N. (2014). Racism? Administrative and community perspectives in data-driven decision making: Systemic perspectives versus technical-rational perspectives. Urban Education, 49(2), 147–181. https://doi.org/10.1177/0042085913475635
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology, 50(6), 2855–2870. https://doi.org/10.1111/bjet.12868
Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018). Discrimination in the age of algorithms. Journal of Legal Analysis, 10, 113–174. https://doi.org/10.1093/jla/laz001
Kordzadeh, N., & Ghasemaghaei, M. (2022). Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388–409. https://doi.org/10.1080/0960085X.2021.1927212
Krippendorff, K. (2004). Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433. https://doi.org/10.1111/j.1468-2958.2004.tb00738.x
Kukutai, T., & Taylor, J. (2016). Indigenous data sovereignty: Toward an agenda. ANU Press.
Kumar, A., Braud, T., Tarkoma, S., & Hui, P. (2020). Trustworthy AI in the age of pervasive computing and big data. Retrieved from https://arxiv.org/pdf/2002.05657.pdf
Kuzey, C., Karaman, A. S., & Akman, E. (2019). Elucidating the impact of visa regimes: A decision tree analysis. Tourism Management Perspectives, 29, 148–156. https://doi.org/10.1016/j.tmp.2018.11.008
Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.1287/mnsc.2018.3093
Lo Piano, S. (2020). Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward. Humanities Soc Sci Commun, 7, 9. https://doi.org/10.1057/s41599-020-0501-9
Loukides, M., Mason, H., & Patil, D. J. (2018). Ethics and data science. O’Reilly Media.
Lovett, R., Lee, V., Kukutai, T., & Cormack, D. (2019). Good data practices for Indigenous data sovereignty and governance. Good Data (pp. 26–36). Amsterdam: Institute of Network Cultures.
Lundberg, I., Narayanan, A., Levy, K., & Salganik, M. J. (2019). Privacy, ethics, and data access: A case study of the Fragile Families Challenge. Socius. https://doi.org/10.1177/2378023118813023
Marco, C. A., & Larkin, G. L. (2000). Research ethics: Ethical issues of data reporting and the quest for authenticity. Academic Emergency Medicine, 7(6), 691–694. https://doi.org/10.1111/j.1553-2712.2000.tb02049.x
Markham, A. N. (2006). Ethic as method. Journal of Information Ethics, 15(2), 37–55.
Markham, A. N. (2018). Critical pedagogy as a response to datafication. Qualitative Inquiry, 25(8), 754–760. https://doi.org/10.1177/1077800418809470
Markham, A. N., Tiidenberg, K., & Herman, A. (2018). Ethics as methods: Doing ethics in the era of big data research—Introduction. Social Media Society. https://doi.org/10.1177/2056305118784502
Martín-Martín, A., Orduna-Malea, E., & Delgado López-Cózar, E. (2018). Coverage of highly-cited documents in Google Scholar, Web of Science, and Scopus: A multidisciplinary comparison. Scientometrics, 116, 1–14. https://doi.org/10.1007/s11192-018-2820-9
McDonald, N., & Forte, A. (2020). The politics of privacy theories: Moving from norms to vulnerabilities. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3313831.3376167
McDonald, N., & Pan, S. (2020). Intersectional AI: A study of how information science students think about ethics and their impact. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 147. https://doi.org/10.1145/3415218
McDougall, R. (2015). Reviewing literature in bioethics research: Increasing rigour in non-systematic reviews. Bioethics, 29(7), 523–528. https://doi.org/10.1111/bioe.12149
Metcalf, J., & Crawford, K. (2016). Where are human subjects in big data research? The emerging ethics divide. Big Data & Society. https://doi.org/10.1177/2053951716650211
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society. https://doi.org/10.1177/2053951716679679
Mtawa, N. N., & Nkhoma, N. M. (2020). Service-learning as a higher education pedagogy for advancing citizenship, conscientization and civic agency: A capability informed view. Higher Education Pedagogies, 5(1), 110–131. https://doi.org/10.1080/23752696.2020.1788969
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. Department of Health, Education, and Welfare.
Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through Lefebvrian conceived space. Organization Studies, 42(5), 719–737. https://doi.org/10.1177/0170840620937900
Nielsen, R. P. (2016). Action research as an ethics praxis method. Journal of Business Ethics, 135(3), 419–428. https://doi.org/10.1007/s10551-014-2482-3
Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79, 119–157.
Noddings, N. (1988). An ethic of caring and its implications for instructional arrangements. American Journal of Education, 96(2), 215–230. https://doi.org/10.1086/443894
Noddings, N. (2017). Care ethics and education. In N. Aloni & L. Weintrob (Eds.), Beyond bystanders moral development and citizenship education (pp. 183–190). SensePublishers.
Oates, J. (2021). BPS Code of human research ethics. British Psychological Society. Retrieved from https://www.bps.org.uk/sites/www.bps.org.uk/files/Policy/Policy%20-%20Files/BPS%20Code%20of%20Human%20Research%20Ethics.pdf
OECD. (2021). Good Practice Principles for Data Ethics in the Public Sector. OECD. http://www.oecd.org/gov/digital-government/good-practice-principles-for-data-ethics-in-the-public-sector.pdf
Orgeira-Crespo, P., Míguez-Álvarez, C., Cuevas-Alonso, M., & Rivo-López, E. (2021). An analysis of unconscious gender bias in academic texts by means of a decision algorithm. PLoS ONE. https://doi.org/10.1371/journal.pone.0257903
Ozga, J. (2008). Governing knowledge: Research steering and research quality. European Educational Research Journal, 7(3), 261–272. https://doi.org/10.2304/eerj.2008.7.3.261
Pangrazio, L., & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2), 419–437. https://doi.org/10.1177/1461444818799523
Pollach, I. (2005). A typology of communicative strategies in online privacy policies: Ethics, power and informed consent. Journal of Business Ethics, 62(3), 221. https://doi.org/10.1007/s10551-005-7898-3
Powell, A. (2018). The data walkshop and radical bottom-up data knowledge. Ethnography for a data-saturated world. Manchester: Manchester University Press. Retrieved from https://www.manchesterhive.com/view/9781526127600/9781526127600.00018.xml
Powell, J. T., & Koelemay, M. J. W. (2022). Systematic reviews of the literature are not always either useful or the best way to add to science. EJVES Vascular Forum, 54, 2–6. https://doi.org/10.1016/j.ejvsvf.2021.10.021
Prado, J., & Marzal, M. A. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri, 63(2), 123–134. https://doi.org/10.1515/libri-2013-0010
Predel, C., Timmermann, C., Ursin, F., Orzechowski, M., Ropinski, T., & Steger, F. (2022). Conflicting aims and values in the application of smart sensors in geriatric rehabilitation: Ethical analysis. JMIR mHealth and uHealth, 10(6), e32910. https://doi.org/10.2196/32910
Puaschunder, J. M. (2019). Big data ethics. Journal of Applied Research in the Digital Economy, 1, 55–75. https://doi.org/10.2139/ssrn.3371603
Rabotnikof, N. (2005). En busca de un lugar común. El espacio público en la teoría política contemporánea. Mexico, DF: UNAM, Instituto de Investigaciones Filosóficas.
Redecker, C, & Punie, Y. (2020), Digital Education Action Plan 2021–2027 Resetting education and training for the digital age. Luxembourg: Office of the European Union. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1602778451601&uri=CELEX:52020DC0624#footnote32
Raymond N.A. (2017) Beyond “do no harm” and individual consent: reckoning with the emerging ethical challenges of civil society’s use of data. In: Taylor L, Floridi L, van der Sloot B. (Eds), Group privacy. Philosophical Studies Series (pp. 67–82). https://doi.org/10.1007/978-3-319-46608-8_4
Reijers, W., Wright, D., Brey, P., et al. (2018). Methods for practising ethics in research and innovation: A literature review. Critical analysis and recommendations. Science and Engineering Ethics, 24, 1437–1481. https://doi.org/10.1007/s11948-017-9961-8
Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest Law Review, 49, 393–432.
Richterich, A. (2018). The big data agenda: Data ethics and critical data studies. University of Westminster Press.
Robinson, F. (2011). The ethics of care: A feminist approach to human security. Temple University Press.
Ross, J. N., Eastman, A., Laliberte, N., & Rawle, F. (2022). The power behind the screen: Educating competent technology users in the age of digitized inequality. International Journal of Educational Research, 115, 102014. https://doi.org/10.1016/J.IJER.2022.102014
Roth, W. D. (2010). Racial mismatch: The divergence between form and function in data for monitoring racial discrimination of Hispanics. Social Science Quarterly, 91(5), 1288–1311. https://doi.org/10.1111/j.1540-6237.2010.00732.x
Ruberg, B., & Ruelos, S. (2020). Data for queer lives: How LGBTQ gender and sexuality identities challenge norms of demographics. Big Data & Society. https://doi.org/10.1177/2053951720933286
Saini, A. (2020). Want to do better science? Admit you’re not objective. Nature, 579(7798), 175. https://doi.org/10.1038/d41586-020-00669-2
Saltz, J. S., & Dewar, N. (2019). Data science ethical considerations: A systematic literature review and proposed project framework. Ethics and Information Technology, 21(3), 197–208. https://doi.org/10.1007/s10676-019-09502-5
Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry, 22, 4349–4357.
SAS Analytics. (2022). Our Principles. Retrieved 9 Nov. 2022, from, https://www.sas.com/en_us/company-information/innovation/responsible-innovation.html
Schwartz, P. M. (2011). Privacy, ethics, and analytics. IEEE Security & Privacy, 9(3), 66–69.
Scottish Government. (2021). An ethics framework for the data and intelligence network. https://www.gov.scot/publications/ethics-framework-data-intelligence-network/documents/
Simon, J. (2015). Distributed epistemic responsibility in a hyperconnected era. In L. Floridi (Ed.), The onlife manifesto (pp. 145–159). Springer.
Sloane, M. (2019). inequality is the name of the game: Thoughts on the emerging field of technology, ethics and social justice. In Proceedings of the Weizenbaum Conference 2019 "Challenges of Digital Inequality - Digital Education, Digital Work, Digital Life", Berlin: Weizenbaum Conference. (p. 9). https://doi.org/10.34669/wi.cp/2.9
Snipp, C. M. (2016). What does data sovereignty imply: What does it look like. In T. Kukutai & J. Taylor (Eds.), Indigenous data sovereignty: Toward an agenda (pp. 39–55). ANU Press.
Stahl, B. C., & Wright, D. (2018). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26–33. https://doi.org/10.1109/MSP.2018.2701164
Stahl, B. C. (2021). From computer ethics and the ethics of AI towards an ethics of digital ecosystems. AI and Ethics. https://doi.org/10.1007/s43681-021-00080-1
Stockley, D., & Balkwill, L. L. (2013). Raising awareness of research ethics in SoTL: The role of educational developers. Canadian Journal for the Scholarship of Teaching and Learning, 4(1), 7.
Stoyanovich, J., Howe, B., & Jagadish, H. V. (2018, May). Special session: A technical research agenda in data ethics and responsible data management. In Proceedings of the 2018 International Conference on Management of Data (pp. 1635–1636). https://doi.org/10.1145/3183713.3205185
Strohmetz, D. B., & Skleder, A. A. (1992). The use of role-play in teaching research ethics: A validation study. Teaching of Psychology, 19(2), 106–108. https://doi.org/10.1207/s15328023top1902_11
Taylor, L., Floridi, L., & Van der Sloot, B. (Eds.). (2016). Group privacy: New challenges of data technologies. Springer.
Tiidenberg, K. (2018). Ethics in digital research. In U. Flick (Ed.), The SAGE handbook of qualitative data collection (pp. 466–479). SAGE.
Timmermann, C. (2018). Contributive justice: An exploration of a wider provision of meaningful work. Social Justice Research, 31(1), 85–111. https://doi.org/10.1007/s11211-017-0293-2
Tronto, J. C. (1993). Moral boundaries: A political argument for an ethic of care. Routledge.
Tusinski Berg, K. (2018). Big data, equality, privacy, and digital ethics. Journal of Media Ethics, 33(1), 44–46. https://doi.org/10.1080/23736992.2018.1407189
Tygel, A. F., & Kirsch, R. (2016). Contributions of Paulo Freire for a critical data literacy: A popular education approach. The Journal of Community Informatics. https://doi.org/10.15353/joci.v12i3.3279
UK Government. (2020). Data ethics framework. Government Digital Service. http://www.gov.uk/government/publications/data-ethics-framework
UNICEF. (2019). Memorandum on Artificial Intelligence and Child Rights. Where are the greatest opportunities for and risks to children’s rights in the AI age. Retrieved from https://www.unicef.org/innovation/media/10501/file/Memorandum%20on%20Artificial%20Intelligence%20and%20Child%20Rights.pdf
UNICEF. (2020). Policy guidance on AI for children. Retrieved from https://www.unicef.org/globalinsight/media/1171/file/UNICEF-Global-Insight-policy-guidance-AI-children-draft-1.0-2020.pdf
US Government. (2019). Federal Data Strategy: Data Ethics Framework. https://resources.data.gov/assets/documents/fds-data-ethics-framework.pdf
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15(3), 398–405. https://doi.org/10.1111/nhs.12048
van Maanen, G. (2022). AI ethics, ethics washing, and the need to politicize data ethics. Digital Society, 1, 9. https://doi.org/10.1007/s44206-022-00013-3
Véliz, C. (2019). Three things digital ethics can learn from medical ethics. Nature Electronics, 2(8), 316–318. https://doi.org/10.1038/s41928-019-0294-2
Véliz, C. (2020). Privacy is power: Why and how you should take back control of your data. Random House.
Véliz, C. (2021). The Oxford handbook of digital ethics. Oxford University Press.
Walter, M., & Suina, M. (2019). Indigenous data, indigenous methodologies and indigenous data sovereignty. International Journal of Social Research Methodology, 22(3), 233–243.
Washington, A. L., & Kuo, R. (2020, January). Whose side are ethics codes on? Power, responsibility and the social good. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 230–240). https://doi.org/10.1145/3351095.3372844
Wheeler, J. (2018). Mining the first 100 days: Human and data ethics in Twitter research. Journal of Librarianship and Scholarly Communication. https://doi.org/10.7710/2162-3309.2235
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in Higher Education: Critical issues and perspectives. Teaching in Higher Education, 25(4), 351–365. https://doi.org/10.1080/13562517.2020.1748811
Woolley, S. C., & Howard, P. N. (2016). Automation, algorithms, and politics| political communication, computational propaganda, and autonomous agents—Introduction. International Journal of Communication, 10, 9.
Yao-Huai, L. (2005). Privacy and data privacy issues in contemporary China. Ethics and Information Technology, 7(1), 7–15. https://doi.org/10.1007/s10676-005-0456-y
Zakharova, I., & Jarke, J. (2022). Educational technologies as matters of care. Learning, Media and Technology, 47(1), 95–108. https://doi.org/10.1080/17439884.2021.2018605
Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—It’s time to make it fair. Nature, 559, 324–326. https://doi.org/10.1038/d41586-018-05707-8
Zimmer, M. (2010). “But the data is already public”: On the ethics of research in Facebook. Ethics and Information Technology, 12(4), 313–325. https://doi.org/10.1007/s10676-010-9227-5
Zimmer, M. (2018). Addressing conceptual gaps in big data research ethics: An application of contextual integrity. Social Media+ Society. https://doi.org/10.1177/2056305118768300
Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75–89. https://doi.org/10.1057/jit.2015.5
Zwitter, A. (2014). Big data ethics. Big Data & Society. https://doi.org/10.1177/2053951714559253
We are very grateful for the highly constructive comments and observations made by the ETHE reviewers.
All authors contributed to writing, revising and reviewing the manuscript.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
All authors declare no conflicts of interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Atenas, J., Havemann, L. & Timmermann, C. Reframing data ethics in research methods education: a pathway to critical data literacy. Int J Educ Technol High Educ 20, 11 (2023). https://doi.org/10.1186/s41239-023-00380-y
- Critical data literacy
- Data literacy
- Data ethics
- Research methods
- Curriculum design
- Higher education