Fairness in Machine Learning Algorithms: A Survey of Recent Approaches and Challenges

In recent years, machine learning (ML) has become increasingly popular in decision-making systems due to its impressive capabilities. However, this widespread adoption raises the need to ensure fairness and avoid discrimination, particularly against underrepresented groups. Most approaches focus on instantaneous fairness, neglecting long-term effects. This article addresses the problem of ensuring long-term fairness in ML-driven decision-making systems.

Instantaneous Fairness vs Long-term Fairness

Instantaneous fairness considers only the immediate context and ignores potential long-term consequences. In contrast, long-term fairness aims to ensure that the system’s decisions remain equitable over time. This difference is crucial because in applications requiring real-time decision-making, extending instantaneous approaches directly may not achieve long-term fairness.

Formulating the Problem

The article highlights the need for developing long-term fairness criteria for ML-based decision-making systems. It acknowledges that most approaches focus on instantaneous fairness and neglect long-term effects. The authors emphasize that understanding the social and ethical responsibility of ML-driven decision-making systems is essential, especially in applications involving sensitive attributes such as gender, race, or age.

Approaches to Long-term Fairness

The article introduces two main approaches to addressing long-term fairness: (a) time-averaged cost and (b) time-averaged dynamic regret (RoffT). These approaches aim to mitigate unfair outcomes by considering the system’s historical behavior.
Time-Averaged Cost: This approach calculates the average cost of decisions over a specific time interval. By averaging the costs across different periods, the system can identify and address potential fairness issues before they materialize.
Time-Averaged Dynamic Regret (RoffT): This approach measures the regret of not choosing the fairest option in a particular context. RoffT captures the long-term impact of suboptimal decisions and helps the system learn from its mistakes to improve fairness over time.

Key Findings

The article highlights several key findings

1. Long-term fairness is crucial in decision-making systems, especially in applications involving sensitive attributes.
2. Instantaneous fairness approaches may not guarantee long-term fairness, and developing criteria specifically designed for long-term fairness is essential.
3. Time-averaged cost and time-averaged dynamic regret (RoffT) are two promising approaches to addressing long-term fairness in ML-driven decision-making systems.
4. Understanding the social and ethical responsibility of ML-driven decision-making systems is critical, especially as these systems become increasingly ubiquitous in our lives.

In conclusion, the article underscores the importance of ensuring long-term fairness in machine learning decision-making systems. By developing criteria specifically designed for long-term fairness and leveraging approaches like time-averaged cost and time-averaged dynamic regret (RoffT), we can build more equitable systems that promote social and ethical responsibility. As these systems become increasingly prevalent in our lives, it is crucial to prioritize fairness and transparency to avoid perpetuating biases and discrimination.