Counterfactual Explanations for Arbitrary Regression Models

Spooner, Thomas; Dervovic, Danial; Long, Jason; Shepard, Jon; Chen, Jiahao; Magazzeni, Daniele

doi:10.48550/arxiv.2106.15212

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…As discussed earlier, several approaches have been proposed in literature to provide recourse to individuals who have been negatively impacted by model predictions (Tolomei et al, 2017;Laugel et al, 2017;Dhurandhar et al, 2018;Wachter et al, 2018;Ustun et al, 2019;Joshi et al, 2019;Van Looveren & Klaise, 2019;Pawelczyk et al, 2020a;Mahajan et al, 2019;Mothilal et al, 2020;Karimi et al, 2020a;Rawal & Lakkaraju, 2020;Karimi et al, 2020c;Dandl et al, 2020;Antorán et al, 2021;Spooner et al, 2021). These approaches can be roughly categorized along the following dimensions Verma et al (2020): type of the underlying predictive model (e.g., tree based vs. differentiable AR-LIME, Wachter, and GS) using CARLA (Pawelczyk et al, 2021).…”

Section: Algorithmic Approaches To Recoursementioning

confidence: 99%

Algorithmic Recourse in the Face of Noisy Human Responses

Pawelczyk¹,

Datta²,

van-den-Heuvel³

et al. 2022

Preprint

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As machine learning (ML) models are increasingly being deployed in high-stakes applications, there has been growing interest in providing recourse to individuals adversely impacted by model predictions (e.g., an applicant whose loan has been denied). To this end, several post hoc techniques have been proposed in recent literature. These techniques generate recourses under the assumption that the affected individuals will implement the prescribed recourses exactly. However, recent studies suggest that individuals often implement recourses in a noisy and inconsistent manner -e.g., raising their salary by $505 if the prescribed recourse suggested an increase of $500. Motivated by this, we introduce and study the problem of recourse invalidation in the face of noisy human responses. More specifically, we theoretically and empirically analyze the behavior of state-of-the-art algorithms, and demonstrate that the recourses generated by these algorithms are very likely to be invalidated if small changes are made to them. We further propose a novel framework, EXPECTing noisy responses (EXPECT), which addresses the aforementioned problem by explicitly minimizing the probability of recourse invalidation in the face of noisy responses. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework, and supports our theoretical findings.

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Section: Algorithmic Approaches To Recoursementioning

confidence: 99%

Algorithmic Recourse in the Face of Noisy Human Responses

Pawelczyk¹,

Datta²,

van-den-Heuvel³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…While counterfactual explanations (CE) can also be generated for arbitrary regression models (Spooner et al 2021), existing work has primarily focused on classification problems. Let Y = (0, 1) K denote the one-hot-encoded output domain with K classes.…”

Section: Introductionmentioning

confidence: 99%

Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals

Altmeyer,

Farmanbar,

Van Deursen

et al. 2024

AAAI

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Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is distributed. This effectively reallocates the task of learning realistic explanations for the data from the model itself to the surrogate. Consequently, the generated explanations may seem plausible to humans but need not necessarily describe the behaviour of the black-box model faithfully. We formalise this notion of faithfulness through the introduction of a tailored evaluation metric and propose a novel algorithmic framework for generating Energy-Constrained Conformal Counterfactuals that are only as plausible as the model permits. Through extensive empirical studies, we demonstrate that ECCCo reconciles the need for faithfulness and plausibility. In particular, we show that for models with gradient access, it is possible to achieve state-of-the-art performance without the need for surrogate models. To do so, our framework relies solely on properties defining the black-box model itself by leveraging recent advances in energy-based modelling and conformal prediction. To our knowledge, this is the first venture in this direction for generating faithful counterfactual explanations. Thus, we anticipate that ECCCo can serve as a baseline for future research. We believe that our work opens avenues for researchers and practitioners seeking tools to better distinguish trustworthy from unreliable models.

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Counterfactual Explanations for Arbitrary Regression Models

Cited by 2 publications

References 22 publications

Algorithmic Recourse in the Face of Noisy Human Responses

Algorithmic Recourse in the Face of Noisy Human Responses

Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals

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