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Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances.
Setting the Right Expectations: Algorithmic Recourse Over Time
The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context.
Cooling down and waking up: feedback cooling switches an unconscious neural computer into a conscious quantum computer
This paper presents a theory of how feedback cooling in the brain reduces thermal noise to the point where macroscale quantum phenomena - crucially Bose-Einstein condensation - can operate at body temperature.
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and performance when fitting statistical models, except in two special cases: when the prevalence of the outcome being predicted is equal across groups, or when a perfectly accurate predictor is used.
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors.
