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Abstract:
We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.Url=files/heidari2018preventing.pdf
Reference:
Preventing Disparate Treatment in Sequential Decision Making H. Heidari, A. KrauseIn the International Joint Conference on Artificial Intelligence (IJCAI), 2018
Bibtex Entry:
@inproceedings{heidari2018preventing,
    title = {Preventing Disparate Treatment in Sequential Decision Making},
    author = {Hoda Heidari and Andreas Krause},
    Booktitle = {the International Joint Conference on Artificial Intelligence (IJCAI)},
    year = {2018}}