AI Ethics Issues in Real World: Evidence from AI Incident Database
This work addresses the problem of vague AI ethics guidelines for practitioners by offering a data-driven taxonomy, though it is incremental as it builds on existing incident cataloging efforts.
The paper analyzed the AI Incident Database to identify 13 application areas and 8 forms of ethical issues in real-world AI deployments, such as intelligent service robots and racial discrimination, aiming to provide a practical taxonomy for ethical AI deployment.
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.