Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives

TL;DR

This paper introduces a visualization method that helps designers and users understand and navigate the inherent trade-offs in AI algorithms, especially between accuracy and fairness, to better align with their goals.

Contribution

It presents a novel approach for exploring and visualizing algorithmic trade-offs, enabling better decision-making in designing AI systems with complex objectives.

Findings

Method improves understanding of trade-offs among designers and users

Experiment shows increased alignment with user goals

Domain experts find the method useful for decision-making

Abstract

Artificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stakes contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs. We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating.