# Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents

arXiv: https://arxiv.org/abs/2504.01029 · HTML: https://mlsanity.com/paper/2504.01029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 244 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 25 of 25)

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- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
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- [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021)
- [The impact of generative artificial intelligence on socioeconomic inequalities and policy making](https://mlsanity.com/paper/2401.05377/) (2023)
- [A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms](https://mlsanity.com/paper/2407.01294/) (2024)
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- [Sora OpenAI's Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generation](https://mlsanity.com/paper/2403.14665/) (2024)
- [Atlas of AI Risks: Enhancing Public Understanding of AI Risks](https://mlsanity.com/paper/2502.05324/) (2025)
- [The Great AI Witch Hunt: Reviewers Perception and (Mis)Conception of Generative AI in Research Writing](https://mlsanity.com/paper/2407.12015/) (2024)
- [A Survey of the Potential Long-term Impacts of AI](https://mlsanity.com/paper/2206.11076/) (2022)
- [Privacy in Immersive Extended Reality: Exploring User Perceptions, Concerns, and Coping Strategies](https://mlsanity.com/paper/2503.21010/) (2025)

## Abstract (arXiv)

The rapid growth of artificial intelligence (AI) technologies has raised major privacy and ethical concerns. However, existing AI incident taxonomies and guidelines lack grounding in real-world cases, limiting their effectiveness for prevention and mitigation. We analyzed 202 real-world AI privacy and ethical incidents to develop a taxonomy that classifies them across AI lifecycle stages and captures contributing factors, including causes, responsible entities, sources of disclosure, and impacts. Our findings reveal widespread harms from poor organizational decisions and legal non-compliance, limited corrective interventions, and rare reporting from AI developers and adopting entities. Our taxonomy offers a structured approach for systematic incident reporting and emphasizes the weaknesses of current AI governance frameworks. Our findings provide actionable guidance for policymakers and practitioners to strengthen user protections, develop targeted AI policies, enhance reporting practices, and foster responsible AI governance and innovation, especially in contexts such as social media and child protection.