  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.
