Fairness-aware Summarization for Justified Decision-Making
- URL: http://arxiv.org/abs/2107.06243v1
- Date: Tue, 13 Jul 2021 17:04:10 GMT
- Title: Fairness-aware Summarization for Justified Decision-Making
- Authors: Moniba Keymanesh, Tanya Berger-Wolf, Micha Elsner, Srinivasan
Parthasarathy
- Abstract summary: We focus on the problem of (un)fairness in the justification of the text-based neural models.
We propose a fairness-aware summarization mechanism to detect and counteract the bias in such models.
- Score: 16.47665757950391
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In many applications such as recidivism prediction, facility inspection, and
benefit assignment, it's important for individuals to know the
decision-relevant information for the model's prediction. In addition, the
model's predictions should be fairly justified. Essentially, decision-relevant
features should provide sufficient information for the predicted outcome and
should be independent of the membership of individuals in protected groups such
as race and gender. In this work, we focus on the problem of (un)fairness in
the justification of the text-based neural models. We tie the explanatory power
of the model to fairness in the outcome and propose a fairness-aware
summarization mechanism to detect and counteract the bias in such models. Given
a potentially biased natural language explanation for a decision, we use a
multi-task neural model and an attribution mechanism based on integrated
gradients to extract the high-utility and discrimination-free justifications in
the form of a summary. The extracted summary is then used for training a model
to make decisions for individuals. Results on several real-world datasets
suggests that our method: (i) assists users to understand what information is
used for the model's decision and (ii) enhances the fairness in outcomes while
significantly reducing the demographic leakage.
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