The paper explains credit decisions using Shapley decomposition for adverse actions.
problem Identifying predictors responsible for adverse credit decisions.
method Develops a simple and intuitive approach based on Shapley decomposition for models with low-order interactions.
result Shows the approach generalizes to Shapley decomposition and Baseline Shapley.
Detecting real-time price impact in algo trading
problem Identifying the impact of traders' actions on market prices
method Measuring timing synchronicity between trader actions and adverse market events
result Detecting price impact on a per-action basis
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding oth…
Proposes an efficient method for ordered counterfactual explanations.
problem Insufficient explanation of perturbation vectors for executing actions.
method Mixed-Integer Linear Optimization (MILP) approach for evaluating and extracting optimal pairs of actions and orders.
result Demonstrated effectiveness of the proposed method on real datasets.
Develops methods for finding counterfactual explanations in sequential decision making.
problem Finding counterfactual explanations for sequential decision making processes.
method Formal characterization of sequential actions and states using Markov decision processes and Gumbel-Max structural causal model. Introduces a polynomial time algorithm based on dynamic programming.
result Algorithm finds optimal counterfactual explanations for sequential decision making.
Recourse explanations can become invalid if collective actions change statistical data.
problem Recourse explanations may become invalid due to collective behavior changing data statistics.
method Formal characterization of conditions under which recourse explanations remain valid under performativity.
result Recourse actions may become invalid if they are influenced by or intervene on non-causal variables.
Improves global counterfactual explanations for model recourse.
problem Inability to provide explanations beyond local instances.
method Investigates and improves Actionable Recourse Summaries (AReS) for global counterfactual explanations.
result Develops more efficient and interactive explainability tools.
DECE visualizes machine learning decisions with counterfactual explanations.
problem Making machine learning models transparent and explainable.
method Interactive visualization system supporting counterfactual explanations at instance- and subgroup-levels.
result DECE enables users to explore and understand machine learning model decisions.
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 suggest actions to achieve a favorable decision. Counterfactual explanations -- "how the world would have (had) to b…
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that effective counterfactual explanations shoul…
Explains agent behavior through intended outcomes in reinforcement learning.
problem Proving impossibility of general post-hoc explanations in reinforcement learning.
method Derives local explanations based on intention for Q-function approximations, proving consistency with learned Q-values.
result Demonstrates the necessity of collecting information during training for accurate explanations.
CEILS generates feasible counterfactual explanations by considering causal impacts.
problem Current counterfactual explanations lack feasibility and causal impact consideration.
method CEILS integrates causal reasoning into existing counterfactuals generation algorithms.
result CEILS provides feasible recommendations to achieve desired outcomes.
Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learn…
Proposes a framework to adjust quotes for informational risk in markets with informed traders and price-revealing quotes.
problem Informational risk in markets with informed traders and price-revealing quotes.
method Proposes a tractable framework to adjust quotes considering adverse selection and price reading.
result Market makers can adjust their quotes to better manage informational risk.
This paper models AMM positions using CI options to calculate LVR and provide actionable guidance.
problem Calculating and managing adverse-selection costs in automated market makers (AMMs).
method Modeling AMM positions as perpetual American CI options to replicate delta and calculate LVR.
result LVR is identical to theta of CI options, and AMM positions have approximately constant LVR over long windows.
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a model, but…
Attack reveals model details from counterfactual explanations.
problem Extracting model details from counterfactual explanations.
method Adversary uses counterfactual explanations to build high-fidelity model.
result High-fidelity and high-accuracy model extraction possible.
BRACE generates efficient counterfactual explanations by integrating causal reasoning.
problem Challenges in traditional counterfactual explanations, especially neglecting causal relationships.
method Backtracking counterfactuals with causal reasoning.
result Our method provides deeper insights into model outputs and is computationally efficient.
We identify action representations from video data, proving their statistical benefits.
problem Identifying latent action policies from video data.
method Entropy-regularized LAPO objective, formalizing desiderata for action representations.
result Entropy-regularized LAPO identifies action representations satisfying desiderata under suitable conditions.
Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decision to be made, and to give an informative explanation. We explore a new method of generating counterfactual explanations, which instead of ex…
FATE predicts user engagement on social apps with explainable explanations.
problem Accurate user engagement prediction for social apps with explainability.
method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.
result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.
Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. F…
Proposes MOC method for better counterfactual explanations in ML models.
problem Difficulties in balancing multiple objectives for counterfactual explanations.
method Translates counterfactual search into a multi-objective optimization problem.
result Returns diverse counterfactuals with different trade-offs and maintains feature diversity.
Study fills and adverse selection effects on trading strategy simulation.
problem Effects of fill probabilities and adverse fills on trading strategy simulation.
method Stochastic optimal control market-making problem, empirical evidence on liquid futures contracts.
result Fill probabilities and adverse fills significantly affect trading strategy performance.
New approach to meaningful and robust algorithmic recourse.
problem Ineffective and unmeaningful algorithmic recourse explanations.
method Meaningful Algorithmic Recourse (MAR) and Effective Algorithmic Recourse (EAR).
result Proposes new constraints for algorithmic recourse that improve both prediction and target.
In this paper, we prove a Kastler-Kalau-Walze type theorem for perturbations of Dirac operators on compact manifolds with or without boundary. As a corollary, we give two kinds of operator-theoretic explanations of the gravitational action on boundary. We also compute the spectral action for Dirac operators with two-fo…
The paper introduces return parity for fairness in MDPs, addressing delayed and adverse effects.
problem Fairness in MDPs for dynamic domains with delayed and adverse effects.
method Proposes return parity, decomposes return disparity, and develops algorithms for state visitation distributional alignment.
result The proposed algorithms can successfully close the disparity gap while maintaining policy performance.
Method finds counterfactual explanations for random forest models.
problem Limited interpretability of random forest models in regulated industries.
method Similarity learning exploiting random forest's feature representation.
result Generated explanations are sparser and more useful than Shapley values.
ACE improves counterfactual explanations with fewer model queries.
problem Inefficient sampling for counterfactual explanations in machine learning models.
method Adaptive sampling combining Bayesian estimation and stochastic optimization.
result ACE achieves superior evaluation efficiency compared to state-of-the-art methods.
ARFs generate plausible counterfactuals for models, improving model understanding.
problem Creating realistic counterfactuals for model analysis.
method Adversarial Random Forests (ARFs) for generating plausible counterfactuals.
result ARFs efficiently generate plausible counterfactuals in a model-agnostic way.
DTOR explains anomalies with rule-based explanations.
problem Need to explain anomalies in data effectively.
method Applies Decision Tree Regressor to estimate anomaly scores and generate rule-based explanations.
result DTOR produces robust and consistent rule-based explanations.
The paper introduces a method to assess the reliability of model explanations.
problem Assessing the quality and reliability of model explanations.
method An Ordinal Consensus Approach using diverse bootstrapped surrogate explainers.
result Uncertainty estimates offer actionable insights beyond standard surrogate explainers.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.
Lapse-supported life insurance exacerbates adverse selection risks.
problem Lapse-supported life insurance increases adverse selection costs.
method Modeling 'Term to 100' contracts and analyzing three methods of managing lapse surplus.
result Adverse selection losses can be almost unlimited under certain conditions.
A new framework for robot block-stacking tasks using causal probabilistic models.
problem Robots fail outside controlled environments due to uncertainty and lack of explicit design for all scenarios.
method Causal probabilistic framework combining causal models and probabilistic representations of noise.
result Robots can perceive, reason about, and explain their environment for block-stacking tasks.
CFRecs uses counterfactual reasoning to improve graph-based recommendations in real estate.
problem Improving model interpretability and actionable insights in graph-based recommender systems.
method A two-stage architecture combining GNN and Graph-VAE to propose minimal yet impactful changes in graph structure and node attributes.
result Demonstrates effectiveness in delivering actionable recommendations for home buyers and sellers.
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI d…
This study analyzes counterfactual explanations for student success models.
problem Improving trust in machine learning models for student success prediction.
method Comparison of counterfactual generation methods (WhatIf, Multi-Objective, Nearest Instance) for student success prediction models.
result WhatIf Counterfactual Explanations are more effective for student success prediction models.
Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the system should be transparent. System transparency enables humans to fo…
MCCE generates realistic counterfactual explanations for tabular data.
problem Creating valid and actionable counterfactual explanations for complex tabular data.
method MCCE models the joint distribution of features and decision using an autoregressive generative model with decision trees. It samples counterfactuals and removes invalid ones.
result MCCE outperforms state-of-the-art methods on various performance metrics and is faster.
PatternLocal improves XAI for non-linear models by suppressing suppressor variables.
problem Suppressor variables cause false-positive feature attributions in non-linear models.
method PatternLocal uses locally linear surrogate models and transforms weights into a generative representation.
result PatternLocal reduces false-positive attributions and provides more reliable explanations.
In this paper, we get a Kastler-Kalau-Walze type theorem associated to nonminimal de Rham-Hodge operators on compact manifolds with boundary. We give two kinds of operator-theoretic explanations of the gravitational action in the case of four dimensional compact manifolds with flat boundary.
New method reduces regret in budgeted learning problems.
problem Decision-making with limited reward queries.
method Confidence-Budget Matching (CBM) principle.
result CBM-based algorithms perform well in adversarial settings.
We prove a Kastler-Kalau-Walze type theorem for the Dirac operator and the signature operator for 3,4-dimensional manifolds with boundary. As a corollary, we give two kinds of operator theoretic explanations of the gravitational action in the case of 4-dimensional manifolds with flat boundary.
Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research has focused almost entirely on learning from passively collected data. We study the problem of learning to acquire followers through normative user behavior, as opposed to the mass following…
IMM uses imitation learning and predictive representation learning to improve market making strategies.
problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.
To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of su…