Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges
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Abstract (arXiv)
Ethics in AI has become a debated topic of public and expert discourse in recent years. But what do people who build AI - AI practitioners - have to say about their understanding of AI ethics and the challenges associated with incorporating it in the AI-based systems they develop? Understanding AI practitioners' views on AI ethics is important as they are the ones closest to the AI systems and can bring about changes and improvements. We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics. Based on 100 AI practitioners' responses, our findings indicate that majority of AI practitioners had a reasonable familiarity with the concept of AI ethics, primarily due to workplace rules and policies. Privacy protection and security was the ethical principle that majority of them were aware of. Formal education/training was considered somewhat helpful in preparing practitioners to incorporate AI ethics. The challenges that AI practitioners faced in the development of ethical AI-based systems included (i) general challenges, (ii) technology-related challenges and (iii) human-related challenges. We also identified areas needing further investigation and provided recommendations to assist AI practitioners and companies in incorporating ethics into AI development.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 81 references that are in the map. First posted on arXiv 14 Jul 2023.
Builds on
20 of 81 references.
References · 20
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8 more
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Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2024Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysiscited by 9
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Conceptscited by 4
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspectivecited by 2
- 2024Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Developmentcited by 1
- 2025RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robotscited by 0
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Productscited by 0
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products13 Jun 2026
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective9 Nov 2025
- 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts27 Sep 2025
- 2025RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots30 Jul 2025
- 2024Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis11 Jun 2024
- 2024Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development21 Mar 2024