Table 3 Overview of data ethics frameworks

Data Feminism

Academia

D’Ignazio and Klein (2020)

• Examine power

• Challenge power

• Elevate emotion and embodiment

• Rethink binaries and hierarchies

• Embraces pluralism

• Consider context

• Make labour visible

Data Ethics Principles

Academia & Civil Society

DataEthics.eu (2017)

• The human being at the centre

• Individual data control

• Transparency

• Accountability

• Equality

CARE Principles for Indigenous Data Governance

Academia & Civil Society

Global Indigenous Data Alliance (2019)

• Collective benefit: for inclusive development and innovation, improved governance and citizen engagement, and equitable outcomes

• Authority to control: recognizing rights and interests, data for governance, and governance of data

• Responsibility: for positive relationships, expanding capability and capacity, and indigenous language and worldviews

• Ethics: for minimising harm and maximising benefit, justice, and future use

Global Data Ethics Pledge

Civil Society

Data for Democracy (2021)

• Fairness

• Openness

• Reliability

• Trust

• Social Benefit

Australia’s AI Ethics Principles

Government

Australian Government, Department of Industry, Science, Energy and Resources (2019)

• Human, social and environmental wellbeing

• Human-centred values

• Fairness

• Privacy protection and security

• Reliability and safety

• Transparency and explainability

• Contestability

• Accountability

General standards for data governance

Government

Datenethik-kommission (2019) [Germany]

• Background principles: Human dignity, self-determination, privacy, security, democracy, justice and solidarity, and sustainability

• Foresighted responsibility

• Respect for the rights of the parties involved

• Data use and data sharing for the public good

• Fit-for-purpose data quality

• Risk-adequate level of information security

• Interest-oriented transparency

An Ethics Framework for the Data and Intelligence Network*

Government

Scottish Government (2021)

• Responsible

• Accountable

• Insightful

• Necessary

• Beneficial

• Observant

• Widely Participatory

Data Ethics Framework

Government

UK Government (2020)

• Background principles: transparency, accountability and fairness

• Define and understand public benefit and user need

• Involve diverse expertise

• Comply with the law

• Review the quality and limitations of the data

• Evaluate and consider wider policy implications

Federal Data Strategy

Data Ethics Framework

Government

US Government (2019)

• Uphold applicable statutes, regulations, professional practices, and ethical standards

• Respect the public, individuals, and communities

• Respect privacy and confidentiality

• Act with honesty, integrity, and humility

• Hold oneself and others accountable

• Promote transparency

• Stay informed of developments in the fields of data management and data science

Good Practice Principles for Data Ethics in the Public Sector

International Organisation

OECD (2021)

• Manage data with integrity

• Be aware of and observe relevant government-wide arrangements for trustworthy data access, sharing and use

• Incorporate data ethical considerations into governmental, organisational and public sector decision-making processes

• Monitor and retain control over data inputs, in particular those used to inform the development and training of AI systems, and adopt a risk-based approach to the automation of decisions

• Be specific about the purpose of data use, especially in the case of personal data

• Define boundaries for data access, sharing and use

• Be clear, inclusive and open

• Publish open data and source code

• Broaden individuals’ and collectives’ control over their data

• Be accountable and proactive in managing risks

Our Principles (SAS)

Private sector—Tech Company

SAS Analytics (2022)

• Human Centricity: Promote human well-being, human agency and equity

• Inclusivity: Ensure accessibility and include diverse perspectives and experiences

• Accountability: Proactively identify and mitigate adverse impacts

• Transparency: Openly communicate intended use, potential risks and how decisions are made

• Robustness: Operate reliably and safely, while enabling mechanisms that assess and manage potential risks throughout a system’s life cycle

• Privacy & Security: Protect the use and application of an individual's data

Universal principles of data ethics**

Private sector—Tech Company

Accenture (2016)

• The highest priority is to respect the persons behind the data

• Attend the downstream uses of datasets

• Provenance of the data and analytical tools shapes the consequences of their use

• Strive to match privacy and security safeguards with privacy and security expectations

• Always follow the law, but understand that the law is often a minimum bar

• Be wary of collecting data just for the sake of more data

• Data can be a tool of inclusion and exclusion

• As much as possible, explain methods for analysis and marketing to data disclosers