Rolling in the deep of cognitive and AI biases

TL;DR

This paper emphasizes the importance of human cognitive biases in AI fairness, proposing a new sociotechnical approach that integrates human factors into understanding and mitigating AI biases.

Contribution

It introduces a novel methodology that incorporates human heuristics and biases into AI fairness analysis, highlighting overlooked human-societal influences on AI outcomes.

Findings

Identifies how human biases influence AI decision-making processes.

Proposes a new mapping linking human heuristics to AI biases.

Reveals hidden pathways of bias propagation in AI systems.

Abstract

Nowadays, we delegate many of our decisions to Artificial Intelligence (AI) that acts either in solo or as a human companion in decisions made to support several sensitive domains, like healthcare, financial services and law enforcement. AI systems, even carefully designed to be fair, are heavily criticized for delivering misjudged and discriminated outcomes against individuals and groups. Numerous work on AI algorithmic fairness is devoted on Machine Learning pipelines which address biases and quantify fairness under a pure computational view. However, the continuous unfair and unjust AI outcomes, indicate that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. Although, the synergy of humans and machines seems imperative to make AI work, the significant impact of human and societal factors on AI bias is currently overlooked. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. Inspired by the cognitive science definition and taxonomy of human heuristics, we identify how harmful human actions influence the overall AI lifecycle, and reveal human to AI biases hidden pathways. We introduce a new mapping, which justifies the human heuristics to AI biases reflections and we detect relevant fairness intensities and inter-dependencies. We envision that this approach will contribute in revisiting AI fairness under deeper human-centric case studies, revealing hidden biases cause and effects.