mlsanityData to 7 Oct 2026

Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns

arXiv 2111.09478 ↗First posted 18 Nov 2021cs.AI cs.SEcited by 9

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Abstract (arXiv)

Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To address this shortcoming, we first present an empirical study where we interviewed 21 scientists and engineers to understand the practitioners' perceptions on AI ethics principles and their implementation. We then propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns and suggest a list of patterns using the newly created template. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 71 references that are in the map. First posted on arXiv 18 Nov 2021.

Builds on

14 of 71 references.

References · 14

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2 more
  1. 2021Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researcherscited by 5
  2. 2016Privacy Patternscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 1; 2026: 2.

9 papers.