AI Ethics Principles in Practice: Perspectives of Designers and Developers
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Abstract (arXiv)
As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia's national scientific research agency (CSIRO), who are involved in designing and developing AI systems for many application areas. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles proposed by the Australian Government. The principles comprise: (1) privacy protection and security, (2) reliability and safety, (3) transparency and explainability, (4) fairness, (5) contestability, (6) accountability, (7) human-centred values, (8) human, social and environmental wellbeing. Discussions on the gained insights from the interviews include various tensions and trade-offs between the principles, and provide suggestions for implementing each high-level principle. We also present suggestions aiming to enhance associated support mechanisms.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 13 of its 107 references that are in the map. First posted on arXiv 14 Dec 2021.
Builds on
13 of 107 references.
References · 13
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
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- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
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Citing papers per year: 2023: 6; 2024: 2; 2025: 1; 2026: 3.
12 papers.
- 2025Is Trust Correlated With Explainability in AI? A Meta-Analysiscited by 7
- 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challengescited by 6
- 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspectscited by 5
- 2023Exploring Qualitative Research Using LLMscited by 5
- 2023Responsible Design Patterns for Machine Learning Pipelinescited by 4
- 2023The Value-Sensitive Conversational Agent Co-Design Frameworkcited by 3
- 2024Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigationcited by 2
- 2024SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interestcited by 2
- 2023Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeingcited by 1
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Productscited by 0
- 2026Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflowscited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2026Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflows6 Aug 2026
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products13 Jun 2026
- 2025Is Trust Correlated With Explainability in AI? A Meta-Analysis16 Apr 2025
- 2024SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest1 Jul 2024
- 2024Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation30 Apr 2024
- 2023The Value-Sensitive Conversational Agent Co-Design Framework18 Oct 2023
- 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges14 Jul 2023
- 2023Exploring Qualitative Research Using LLMs23 Jun 2023
- 2023Responsible Design Patterns for Machine Learning Pipelines31 May 2023
- 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects17 Apr 2023
- 2023Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing12 Apr 2023