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AI Ethics Issues in Real World: Evidence from AI Incident Database

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arxiv 2206.07635 v2 pith:CSYXBAPT submitted 2022-06-15 cs.AI cs.CY

Mengyi Wei , Zhixuan Zhou This is my paper · ORCID
classification cs.AIcs.CY
keywords issuesethicsethicalrealworlddatabasedifferentguidelines
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With the powerful performance of Artificial Intelligence (AI) also comes prevalent ethical issues. Though governments and corporations have curated multiple AI ethics guidelines to curb unethical behavior of AI, the effect has been limited, probably due to the vagueness of the guidelines. In this paper, we take a closer look at how AI ethics issues take place in real world, in order to have a more in-depth and nuanced understanding of different ethical issues as well as their social impact. With a content analysis of AI Incident Database, which is an effort to prevent repeated real world AI failures by cataloging incidents, we identified 13 application areas which often see unethical use of AI, with intelligent service robots, language/vision models and autonomous driving taking the lead. Ethical issues appear in 8 different forms, from inappropriate use and racial discrimination, to physical safety and unfair algorithm. With this taxonomy of AI ethics issues, we aim to provide AI practitioners with a practical guideline when trying to deploy AI applications ethically.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Red Light, Grey Zone: A Multi-Perspective Interactive Narrative for Autonomous Driving Ethics

    cs.CY 2026-07 conditional novelty 5.0 of 10

    A 12-participant study suggests that a multi-perspective interactive narrative can encourage non-experts to reason about autonomous-driving ethics as distributed responsibility rather than single-actor blame.

  2. The Only Way is Ethics: A Guide to Ethical Research with Large Language Models

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A practitioner-focused guide that distills existing AI ethics literature into actionable Do's and Don'ts for each stage of LLM research projects.

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