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Found 10 papers, 2 papers with code
Causal Equal Protection as Algorithmic Fairness
Key to equal protection is equalizing the risks of erroneous classifications (in a sense to be specified) as opposed to equalizing the rates of erroneous classifications.
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems.
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs
This allows us to derive a fairness score that we then compare to the decision maker's utility.
Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory
For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness.
Towards Accountability in the Use of Artificial Intelligence for Public Administrations
We argue that the phenomena of distributed responsibility, induced acceptance, and acceptance through ignorance constitute instances of imperfect delegation when tasks are delegated to computationally-driven systems.
On the Moral Justification of Statistical Parity
This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations
However, we show that the literature has neglected the problem of the time dependency of counterfactual explanations.
The societal and ethical relevance of computational creativity
In this paper, we provide a philosophical account of the value of creative systems for individuals and society.
A blindspot of AI ethics: anti-fragility in statistical prediction
With this paper, we aim to put an issue on the agenda of AI ethics that in our view is overlooked in the current discourse.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness.
