Responsible Scoring Mechanisms Through Function Sampling
Abolfazl Asudeh, H. V. Jagadish

TL;DR
This paper introduces unbiased function sampling techniques for designing responsible scoring mechanisms, focusing on linear functions, and demonstrates their application in ranking scenarios to promote fairness and accountability.
Contribution
It provides unbiased samplers for the entire function space and a novel hyperplane arrangement approximation method, enabling scalable responsible scoring design.
Findings
Unbiased samplers for linear scoring functions and their vicinity.
A linear-in-samples algorithm for hyperplane arrangement approximation.
Application of sampling methods to improve responsible ranking algorithms.
Abstract
Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input and generates a score.The scoring functions are either machine-learned or human-designed and can be used for different decision purposes such as ranking or classification. Given the potential impact of these scoring mechanisms on individuals' lives and on society, it is important to make sure these scores are computed responsibly. Hence we need tools for responsible scoring mechanism design. In this paper, focusing on linear scoring functions, we highlight the importance of unbiased function sampling and perturbation in the function space for devising such tools. We provide unbiased samplers for the entire function space, as well as a…
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Taxonomy
TopicsMachine Learning and Algorithms · Machine Learning and Data Classification · Bayesian Modeling and Causal Inference
