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

arXiv: https://arxiv.org/abs/2206.07635 · HTML: https://mlsanity.com/paper/2206.07635/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 15 Jun 2022.

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

- [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://mlsanity.com/paper/1712.02976/) (2017)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [A Survey On Universal Adversarial Attack](https://mlsanity.com/paper/2103.01498/) (2021)
- [Fake News Detection via NLP is Vulnerable to Adversarial Attacks](https://mlsanity.com/paper/1901.09657/) (2019)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [An Analysis of Pedestrians' Behavior in Emergency Evacuation Using Cellular Automata Simulation](https://mlsanity.com/paper/1903.01229/) (2019)

## Built on by (most cited first, 14 of 14)

- [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://mlsanity.com/paper/2301.12867/) (2023)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [MERA: A Comprehensive LLM Evaluation in Russian](https://mlsanity.com/paper/2401.04531/) (2024)
- [Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie](https://mlsanity.com/paper/2309.09120/) (2023)
- [The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements](https://mlsanity.com/paper/2310.06269/) (2023)
- [The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI](https://mlsanity.com/paper/2410.04931/) (2024)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale](https://mlsanity.com/paper/2211.14358/) (2022)
- [AI for All: Identifying AI incidents Related to Diversity and Inclusion](https://mlsanity.com/paper/2408.01438/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?](https://mlsanity.com/paper/2412.13846/) (2024)
- [Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acacias](https://mlsanity.com/paper/2407.11360/) (2024)
- [Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?](https://mlsanity.com/paper/2412.15920/) (2024)

## Abstract (arXiv)

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.