Algorithmic recourse in sequential decision-making for long-term fairness.

Area of Science:

• Artificial Intelligence
• Computer Science
• Machine Learning

Background:

• Sequential decision-making poses challenges for long-term fairness, as early choices can worsen disparities.
• Current fairness methods often adjust decision models directly, overlooking individual interventions.

Purpose of the Study:

• To introduce a novel framework for achieving long-term fairness through sequential algorithmic recourse.
• To develop a causally grounded approach for generating personalized, temporally coherent recourse plans.

Main Methods:

• Introduced Sequential Causal Algorithmic Recourse for Fairness (SCARF).
• Integrated structural causal modeling with sequential generative modeling.
• Incorporated short-term and long-term fairness constraints with budget limitations.

Main Results:

• SCARF generates personalized recourse trajectories mitigating disparities over multiple decision cycles.
• Empirical analysis revealed trade-offs between short-term and long-term fairness under sequential interventions.
• Demonstrated SCARF's effectiveness on synthetic and semi-synthetic datasets.

Conclusions:

• SCARF offers a practical framework for analyzing and improving long-term fairness in dynamic decision-making.
• The approach provides actionable interventions for individuals to ensure equitable outcomes.
• Highlights the importance of considering temporal dynamics in fairness interventions.