From Search To Sampling: Generative Models For Robust Algorithmic Recourse
Prateek Garg, Lokesh Nagalapatti, Sunita Sarawagi
TL;DR
This paper introduces GenRe, a generative model for algorithmic recourse that jointly optimizes proximity, plausibility, and validity, resulting in more effective and efficient recourse recommendations than existing methods.
Contribution
GenRe is the first generative model trained to jointly optimize all three recourse objectives, improving recommendation quality and inference efficiency.
Findings
GenRe outperforms state-of-the-art baselines on multiple metrics.
Sampling from GenRe yields lower-cost recourses.
GenRe offers a better trade-off between cost, plausibility, and validity.
Abstract
Algorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome. Effective recourse methods must balance three conflicting goals: proximity to the original profile to minimize cost, plausibility for realistic recourse, and validity to ensure the desired outcome. We show that existing methods train for these objectives separately and then search for recourse through a joint optimization over the recourse goals during inference, leading to poor recourse recommendations. We introduce GenRe, a generative recourse model designed to train the three recourse objectives jointly. Training such generative models is non-trivial due to lack of direct recourse supervision. We propose efficient ways to synthesize such supervision and further show that GenRe's training leads to a…
Peer Reviews
Decision·ICLR 2025 Poster
Some strengths of this work include: - The development of a single generative model that converts negative examples into positive samples suitable for addressing the algorithmic recourse problem. - The model enhances the overall performance of recourse mechanisms. - The authors have anonymously open-sourced their codebase, allowing others to reproduce their approach.
## Clarity The notation, definitions, and equations should be carefully rechecked. In the current form of the paper, the following points severely reduce the paper's readability, and the reader has to guess what is most likely implied. - Quantities are not properly defined. 1. In Eq. (2), the cost function employed in this work is not defined. What is the cost? Did the authors provide clarifications regarding its final form? 2. Line 251, $N^+$ is not defined. 3. Eq (6) what is $
- First of all, the paper is well-written and organized, making it easily followed. The problem definition and motivation are presented clearly, allowing readers to understand the contribution. - Undoubtedly, the problem of recourse via AI is an important and open problem. It is also closely related to conditional generation, and consequently, the technical discussion (if useful) can be apply to a wide range of problems beyond. - I appreciate that the authors analyze their proposed method on var
I think the major weaknesses are a lack of careful motivation of the proposed method, as well as a more direct comparison with methods beyond the field of algorithmic recourse (but can be directly applied). Specifically, - From the aspect of this specific problem (algorithmic recourse), if I understand correctly, the major improvement in the training process is the goal of Eq (4), which includes all properties that users care about (as well as the empirical distribution $Q(x^+|x)$ for sampling)
GenRe effectively integrates proximity, plausibility, and validity into a single generative model, addressing the core challenges of algorithmic recourse in a unified way. This contrasts with traditional methods that optimize these objectives separately, offering a more balanced solution. By using forward sampling instead of gradient-based search, GenRe reduces computational costs during inference. This makes the model more scalable and applicable to real-world scenarios where speed is crucial.
While GenRe shows improvements in the tested scenarios, its generalizability to highly diverse datasets or domains with different types of constraints might require additional adjustments or fine-tuning. The paper acknowledges the challenge of synthesizing recourse supervision for training, which may introduce assumptions or heuristics that could impact the model's robustness in practice. The success of GenRe heavily depends on the quality of the generative model and the training data. If the
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Generative Adversarial Networks and Image Synthesis · Ethics and Social Impacts of AI