Office: N4.004 Max-Planck-Ring 4 72076 Tübingen +49 7071 601 532 ahkarimi Instead, we propose a shift of paradigm from recourse via nearest counterfactual explanations to recourse through minimal interventions, shifting the focus from explanations to interventions. "any proposal that maximizes fairness and transparency and supports market growth".650. Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. PDF. 3. As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also . Ordered chronologically, we summarize the goal, formulation, solution, and properties of each algorithm. There are two tracks of submissions: paper track and dataset track. As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing . CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. Call for Submissions. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020: Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Contributed Talk 3: Algorithmic Recourse: from Counterfactual Explanations to Interventions (Prerecorded talk) Q&A for contributed talks 1,2,3 (Q&A session) Break 2 (Break) Causal discovery in complex environments, e.g., in the presence of distribution shifts, latent confounders, selection bias, cycles, measurement error, small samples, or . ei plg Karimi, A., Schölkopf, B., Valera, I. Algorithmic Recourse: from Counterfactual Explanations to Interventions 4th Conference on Fairness, Accountability, and Transparency (FAccT 2021), pages: 353-362, (Editors: Madeleine Clare Elish and William Isaac and Richard S. Zemel), ACM, March 2021 (conference) link (url) DOI Project Page a non-parametric model with independent errors according to Judea Pearl [127] , [128] . The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. 02/14/2020 ∙ by Amir-Hossein Karimi, et al. Unfortunately, in practice, the true underlying structural causal model is generally unknown. Algorithmic recourse: from theory to practice. Causal constraints . Ustun et al., 2019 pdf. Recommendations are offered as actions in the real world governed by causal relations, whereby actions on a variable may have consequential effects on others. What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability Free Access. Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. This paper will draw on literature from the . Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers. The individual then exerts time and effort to positively change their circumstances. 15 Algorithmic Recourse: from Counterfactual Explanations to Interventions. Deep Variational Sufficient Dimensionality Reduction. A Semiotics-based epistemic tool to reason about ethical issues in digital technology design and development. Algorithmic Recourse: from Counterfactual Explanations to Interventions. Unfortunately, in practice, the true underlying structural causal model is generally unknown. Algorithmic Recourse: from Counterfactual Explanations to Interventions. The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution). In many applications, it is important to be able to explain the decisions of machine learning systems. 2018. Yi Su, Thorsten Joachims. Measurement and Fairness. In this context, I will discuss the inherent limitations of counterfactual explanations, and argue for a shift of paradigm from recourse via nearest counterfactual explanations to recourse through interventions, which directly accounts for the underlying causal structure in the data.
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