AI brain balancing accuracy and fairness in a futuristic cityscape

Fairness vs. Accuracy: Can AI Be Both?

Jordan Keane in Tech & Innovation December 2025 • 4 min read.

"A New Study Reveals How to Navigate the Algorithmic Frontier and Build Less Discriminatory AI Systems"

Artificial intelligence is increasingly shaping decisions that impact our lives, from loan applications to job opportunities. Algorithms, designed to be objective, can inadvertently perpetuate and even amplify existing societal biases. These biases manifest as systematic variations in predictive ability across different subgroups of the population, raising critical questions about fairness and equity.

While accuracy—the ability of an algorithm to make correct predictions—is a primary goal, fairness is equally essential. However, these two properties often clash, creating a difficult trade-off. Improving fairness may come at the cost of reduced accuracy, and vice versa. This dilemma forces policymakers and AI developers to confront a crucial question: How can we design algorithms that are both accurate and fair, especially when working with limited data?

Recent research by Liang et al. (2023) has introduced a theoretical framework to address this challenge, offering a way to formalize the fairness-accuracy trade-off. Building on this foundation, a new study provides a consistent estimator for the theoretical fairness-accuracy frontier and proposes methods to test hypotheses related to fairness, such as whether excluding a specific covariate from the algorithm's training data improves overall fairness. This research also explores whether less discriminatory alternatives to existing algorithms exist, providing practical tools for evaluating and improving AI systems.

The Algorithmic Frontier: Mapping Fairness and Accuracy

AI brain balancing accuracy and fairness in a futuristic cityscape

The concept of a fairness-accuracy frontier is central to understanding the trade-offs involved in algorithm design. This frontier represents the set of all possible algorithms, plotting their accuracy against their fairness. The goal is to identify the algorithms that perform best in terms of both properties, lying on the boundary of what is achievable.

Fairness, in this context, is often measured by the gap between group-specific expected losses. This metric quantifies how differently the algorithm performs for various subgroups within the population. For example, an algorithm might be considered unfair if it consistently makes less accurate predictions for one racial group compared to another. The fairness-accuracy frontier helps visualize the extent to which reducing this gap impacts overall accuracy.

Group-Balanced Data: In this scenario, the algorithm performs similarly across all groups, and the frontier represents a smooth trade-off between fairness and accuracy.
Group-Skewed Data: Here, the algorithm favors one group over another. The frontier highlights how fairness can be improved by shifting the algorithm's focus to reduce disparities.

Understanding whether the data used to train an algorithm is group-balanced or skewed is crucial for designing effective interventions. If the data is group-balanced, the focus might be on maintaining fairness without sacrificing accuracy. If the data is skewed, more aggressive measures might be needed to level the playing field, even if it means some reduction in overall accuracy.

Toward a More Equitable AI Future

The development of AI systems that are both fair and accurate is an ongoing challenge, but one that is essential for building a more equitable future. By providing tools to measure and test the fairness of algorithms, research like this empowers policymakers, developers, and individuals to challenge discriminatory outcomes and work toward AI systems that benefit everyone. As AI becomes further integrated into our lives, ensuring fairness will require continuous vigilance, adaptation, and a commitment to ethical design principles.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.08879,

Title: Inference For An Algorithmic Fairness-Accuracy Frontier

Subject: econ.em cs.lg

Authors: Yiqi Liu, Francesca Molinari

Published: 13-02-2024

Everything You Need To Know

What is the core challenge when designing AI algorithms?

The core challenge is balancing algorithmic fairness and accuracy. Algorithms are designed to be objective, but they can perpetuate existing societal biases. While accuracy is crucial, fairness is equally essential, and these two often clash. The challenge lies in designing algorithms that are both accurate and fair, particularly when working with limited data. The research aims to provide actionable strategies for policymakers and developers to create equitable AI systems, including methods to challenge discriminatory outcomes.

How does the concept of the fairness-accuracy frontier help in AI algorithm design?

The fairness-accuracy frontier is central to understanding trade-offs in algorithm design. It represents a set of possible algorithms, plotting their accuracy against their fairness. The goal is to identify algorithms that perform best in both aspects. The frontier helps visualize the extent to which reducing the gap between group-specific expected losses impacts overall accuracy. This is crucial for deciding what interventions are needed. For instance, the frontier can show the impact of addressing biases in group-skewed data, which may necessitate shifts in the algorithm's focus to reduce disparities.

How is fairness measured in the context of AI algorithms, and why is it important?

Fairness is often measured by the gap between group-specific expected losses. This metric quantifies how differently an algorithm performs for various population subgroups. If an algorithm consistently makes less accurate predictions for one racial group compared to another, it is considered unfair. Ensuring fairness is vital because AI systems significantly impact our lives, influencing decisions from loan applications to job opportunities. Addressing this is crucial for equitable outcomes and avoiding the amplification of societal biases.

What are the implications of using group-balanced versus group-skewed data in AI algorithm training?

The nature of the data used to train an algorithm significantly impacts its design. Group-balanced data, where the algorithm performs similarly across all groups, typically results in a smoother trade-off between fairness and accuracy. The focus may be on maintaining fairness without sacrificing accuracy. Conversely, group-skewed data, where the algorithm favors one group over another, requires more aggressive measures to level the playing field, even if it means some reduction in overall accuracy. Understanding data characteristics is crucial for effective interventions and fairer AI systems.

What practical tools and approaches can be used to improve the fairness of AI systems?

The research provides practical tools for evaluating and improving AI systems. These include methods to test hypotheses related to fairness, such as excluding a specific covariate from the algorithm's training data to improve overall fairness. The study offers a consistent estimator for the theoretical fairness-accuracy frontier, which helps visualize the trade-offs. The framework helps policymakers and developers to challenge discriminatory outcomes by offering testable methods. By understanding the fairness-accuracy frontier, developers can identify algorithms that balance these two critical aspects, working towards more equitable AI.