# AI and Ethics -- Operationalising Responsible AI

arXiv: https://arxiv.org/abs/2105.08867 · HTML: https://mlsanity.com/paper/2105.08867/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 8 of 8)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018](https://mlsanity.com/paper/1907.07874/) (2019)

## Built on by (most cited first, 11 of 11)

- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [Towards a Roadmap on Software Engineering for Responsible AI](https://mlsanity.com/paper/2203.08594/) (2022)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Investigating Responsible AI for Scientific Research: An Empirical Study](https://mlsanity.com/paper/2312.09561/) (2023)
- [Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States](https://mlsanity.com/paper/2212.00740/) (2022)
- [Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework](https://mlsanity.com/paper/2408.00965/) (2024)
- [Interacting Large Language Model Agents. Interpretable Models and Social Learning](https://mlsanity.com/paper/2411.01271/) (2024)
- [Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach](https://mlsanity.com/paper/2301.05517/) (2023)
- [A federated architecture for sector-led AI governance: lessons from India](https://mlsanity.com/paper/2603.26865/) (2026)
- [Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement](https://mlsanity.com/paper/2409.10520/) (2024)

## 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.