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

TL;DR

This paper analyzes real-world AI incidents from a database to identify common ethical issues, their forms, and application areas, providing practical guidelines for ethical AI deployment.

Contribution

It introduces a taxonomy of AI ethics issues based on incident analysis, highlighting prevalent problem areas and forms of unethical AI use in practice.

Findings

Identified 13 application areas with frequent unethical AI use

Categorized AI ethics issues into 8 distinct forms

Provided a practical guideline for ethical AI deployment

Abstract

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.