Summary of Rolling in the Deep Of Cognitive and Ai Biases, by Athena Vakali and Nicoleta Tantalaki
Rolling in the deep of cognitive and AI biases
by Athena Vakali, Nicoleta Tantalaki
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel methodology is proposed to address the pressing issue of artificial intelligence (AI) bias by considering the sociotechnical system comprising humans, machines, and societal factors. The study recognizes that AI systems, designed to be fair, can still deliver discriminatory outcomes due to human biases. To mitigate this, the authors draw upon cognitive science definitions and taxonomies of human heuristics to identify how human actions influence the overall AI lifecycle, revealing hidden pathways of bias. A new mapping is introduced, linking human heuristics to AI biases and detecting fairness intensities and inter-dependencies. This approach aims to contribute to revising AI fairness assessments through deeper human-centric case studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have a big problem on their hands. Even if they design AI systems to be fair, those systems can still make biased decisions because of the people who build them. This paper is about finding a solution to this issue by looking at how humans and machines work together to create biases in AI. The authors use ideas from cognitive science to understand how human actions affect the way AI works, and they develop a new way of measuring bias that takes into account both human and machine factors. |
