Graphic credit- N. Hanacek/NIST
AI-generated content regularly demonstrates evidence of bias, sometimes it is very obvious, but sometimes it is subtle and difficult to detect. There are compounded layers of bias. This is an ethical concern for the creators of AI, but responsible users must also be aware of this. ALWAYS use a critical eye to evaluate answers and use the built-in reporting feature when you encounter these problems.
Recall that in both the classroom and your career, you are responsible for your work so YOU must critically examine AI-generated responses for bias prior to use.
October, 2023, the IBM Data and AI Team. published Shedding light on AI bias with real world examples on the official IBM Blog.
October, 2023, Scientific American published the article, Humans Absorb Bias from AI—And Keep It after They Stop Using the Algorithm, by Lauren Leffer.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://doi.org/10.1186/s40561-024-00316-7
"Biased AI can give consistently different outputs for certain groups compared to others. Biased outputs can discriminate based on race, gender, biological sex, nationality, social class, or many other factors. Human beings choose the data that algorithms use, and even if these humans make conscious efforts to eschew bias, it can still be baked into the data they select. Extensive testing and diverse teams can act as effective safeguards, but even with these measures in place, bias can still enter machine-learning processes. AI systems then automate and perpetuate biased models."
Rutgers- Battling Bias in AI
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