🤖 AI Summary
Contemporary AI systems frequently exhibit discriminatory decisions in high-stakes domains—including healthcare, finance, and law enforcement—where algorithmic optimization alone proves insufficient for ensuring fairness.
Method: This study transcends conventional computational approaches by centering human cognitive biases as primary analytical entities in AI fairness assessment. We propose the “human–machine bias transmission” theoretical framework, systematically mapping cognitive heuristics (grounded in cognitive science) onto implicit bias pathways across the AI fairness lifecycle—data curation, model development, and deployment. Integrating socio-technical systems analysis with bias provenance modeling, we construct an interpretable bias reflection map and define quantitative metrics for fairness intensity and interdependence.
Contribution/Results: The work establishes a human-centered paradigm for AI fairness evaluation, delivering novel conceptual tools, a rigorously grounded explanatory framework, and empirical foundations for diagnosing and mitigating bias propagation at the human–AI interface.
📝 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.