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1. Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explanation Type and Presentation on Lay User Understanding

   VanNostrand, P; Hofmann, D; Ma, L; Rundensteiner, E (June 2024, Association for Computing Machinery)

   Automated decision-making systems are increasingly deployed in domains such as hiring and credit approval where negative outcomes can have substantial ramifications for decision subjects. Thus, recent research has focused on providing explanations that help decision subjects understand the decision system and enable them to take actionable recourse to change their outcome. Popular counterfactual explanation techniques aim to achieve this by describing alterations to an instance that would transform a negative outcome to a positive one. Unfortunately, little user evaluation has been performed to assess which of the many counterfactual approaches best achieve this goal. In this work, we conduct a crowd-sourced between-subjects user study (N = 252) to examine the effects of counterfactual explanation type and presentation on lay decision subjects’ understandings of automated decision systems. We find that the region-based counterfactual type significantly increases objective understanding, subjective understanding, and response confidence as compared to the point-based type. We also find that counterfactual presentation significantly effects response time and moderates the effect of counterfactual type for response confidence, but not understanding. A qualitative analysis reveals how decision subjects interact with different explanation configurations and highlights unmet needs for explanation justification. Our results provide valuable insights and recommendations for the development of counterfactual explanation techniques towards achieving practical actionable recourse and empowering lay users to seek justice and opportunity in automated decision workflows. 

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2. Counterfactual Explanation Analytics: Empowering Lay Users to Take Action Against Consequential Automated Decisions

   https://doi.org/10.14778/3685800.3685872  

   VanNostrand, Peter M; Hofmann, Dennis M; Ma, Lei; Genin, Belisha; Huang, Randy; Rundensteiner, Elke A (August 2024, Proceedings of the VLDB Endowment)

   Machine learning is routinely used to automate consequential decisions about users in domains such as finance and healthcare, raising concerns of transparency and recourse for negative outcomes. Existing Explainable AI techniques generate a static counterfactual point explanation which recommends changes to a user's instance to obtain a positive outcome. Unfortunately, these recommendations are often difficult or impossible for users to realistically enact. To overcome this, we present FACET, the first interactive robust explanation system which generates personalized counterfactual region explanations. FACET's expressive explanation analytics empower users to explore and compare multiple counterfactual options and develop a personalized actionable plan for obtaining their desired outcome. Visitors to the demonstration will interact with FACET via a new web dashboard for explanations of a loan approval scenario. In doing so, visitors will experience how lay users can easily leverage powerful explanation analytics through visual interactions and displays without the need for a strong technical background. 

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3. FACET: Robust Counterfactual Explanation Analytics

   https://doi.org/10.1145/3626729  

   VanNostrand, Peter M; Zhang, Huayi; Hofmann, Dennis M; Rundensteiner, Elke A (December 2023, Proceedings of the ACM on Management of Data)

   Machine learning systems are deployed in domains such as hiring and healthcare, where undesired classifications can have serious ramifications for the user. Thus, there is a rising demand for explainable AI systems which provide actionable steps for lay users to obtain their desired outcome. To meet this need, we propose FACET, the first explanation analytics system which supports a user in interactively refining counterfactual explanations for decisions made by tree ensembles. As FACET's foundation, we design a novel type of counterfactual explanation called the counterfactual region. Unlike traditional counterfactuals, FACET's regions concisely describe portions of the feature space where the desired outcome is guaranteed, regardless of variations in exact feature values. This property, which we coin explanation robustness, is critical for the practical application of counterfactuals. We develop a rich set of novel explanation analytics queries which empower users to identify personalized counterfactual regions that account for their real-world circumstances. To process these queries, we develop a compact high-dimensional counterfactual region index along with index-aware query processing strategies for near real-time explanation analytics. We evaluate FACET against state-of-the-art explanation techniques on eight public benchmark datasets and demonstrate that FACET generates actionable explanations of similar quality in an order of magnitude less time while providing critical robustness guarantees. Finally, we conduct a preliminary user study which suggests that FACET's regions lead to higher user understanding than traditional counterfactuals. 

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4. Counterfactual Explanations Can Be Manipulated

   Slack, Dylan; Hilgard, Anna; Lakkaraju, Himabindu; Singh, Sameer (January 2021, Advances in Neural Information Processing Systems (NeurIPS))

   Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilties of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations. 

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5. Counterfactual Explanations Can Be Manipulated

   Slack, Dylan; Hilgard, Anna; Lakkaraju, Himabindu; Singh, Sameer. (December 2021, Advances in neural information processing systems)

   Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilities of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations. 

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