Subjective Fairness

Dimitrakakis, Christos; Liu, Yang; Parkes, David; Radanovic, Goran

Fairness is in the eye of the beholder

Document type :

Pré-publication ou Document de travail

Title :

Subjective Fairness
Fairness is in the eye of the beholder

Author(s) :

Dimitrakakis, Christos [Auteur]
Université de Lille, Sciences Humaines et Sociales
Chalmers University of Technology [Göteborg]
Sequential Learning [SEQUEL]
Harvard John A. Paulson School of Engineering and Applied Sciences [SEAS]
Liu, Yang [Auteur]
Harvard John A. Paulson School of Engineering and Applied Sciences [SEAS]
Parkes, David [Auteur]
Harvard John A. Paulson School of Engineering and Applied Sciences [SEAS]
Radanovic, Goran [Auteur]
Harvard John A. Paulson School of Engineering and Applied Sciences [SEAS]

English keyword(s) :

fairness
Bayesian inference

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

We introduce a natural, and widely applicable framework for fairnessthat relies on the available information. We develop algorithms for achievinga few different notions of fairness within a subjective framework, and in ...
Show more >We introduce a natural, and widely applicable framework for fairnessthat relies on the available information. We develop algorithms for achievinga few different notions of fairness within a subjective framework, and in particularrecently proposed concepts of fairness that are grounded in concepts ofsimilarity and conditional independence. We argue that a suitable notion ofsimilarity in the Bayesian setting is distributional similarity conditioned on theobservations. For the latter, as independence is difficult to achieve uniformlyin the Bayesian setting, we suggest a relaxation, for which we provide a smallexperimental demonstration.Show less >

Language :

Anglais

Collections :