Exploring the Landscape of Fairness Interventions in Software Engineering

TL;DR

This survey reviews various fairness interventions in AI applied to software engineering, highlighting methods to mitigate bias and ensure equitable outcomes across different domains.

Contribution

It provides a comprehensive overview of existing fairness strategies in AI, identifying gaps and future directions for research in software engineering contexts.

Findings

Multiple fairness interventions exist with varying effectiveness.

Bias mitigation techniques are crucial for ethical AI deployment.

Research gaps identified in applying fairness methods to real-world systems.

Abstract

Current developments in AI made it broadly significant for reducing human labor and expenses across several essential domains, including healthcare and finance. However, the application of AI in the actual world poses multiple risks and disadvantages due to potential risk factors in data (e.g., biased dataset). Practitioners developed a number of fairness interventions for addressing these kinds of problems. The paper acts as a survey, summarizing the various studies and approaches that have been developed to address fairness issues