mlsanityData to 7 Oct 2026

AI and Ethics -- Operationalising Responsible AI

arXiv 2105.08867 ↗First posted 19 May 2021cs.AIcited by 11

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Abstract (arXiv)

In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences. Building and maintaining public trust in AI has been identified as the key to successful and sustainable innovation. This chapter discusses the challenges related to operationalizing ethical AI principles and presents an integrated view that covers high-level ethical AI principles, the general notion of trust/trustworthiness, and product/process support in the context of responsible AI, which helps improve both trust and trustworthiness of AI for a wider set of stakeholders.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 19 May 2021.

Builds on

8 of 39 references.

References · 8

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  4. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 488
  5. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
  6. 2019Principles alone cannot guarantee ethical AIcited by 134
  7. 2014Differential Privacy and Machine Learning: a Survey and Reviewcited by 66
  8. 2019A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018cited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

11 papers.

  1. 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
  2. 2021Software Engineering for Responsible AI: An Empirical Study and Operationalised Patternscited by 9
  3. 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
  4. 2023Model-driven Engineering for Machine Learning Components: A Systematic Literature Reviewcited by 7
  5. 2023Investigating Responsible AI for Scientific Research: An Empirical Studycited by 4
  6. 2022Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United Statescited by 2
  7. 2024Integrating ESG and AI: A Comprehensive Responsible AI Assessment Frameworkcited by 2
  8. 2024Interacting Large Language Model Agents. Interpretable Models and Social Learningcited by 2
  9. 2023Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approachcited by 1
  10. 2026A federated architecture for sector-led AI governance: lessons from Indiacited by 1
  11. 2024Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagementcited by 0