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RESEARCH PAPER · Knowledge and Information Systems · 2016

Auditing Black-Box Models for Indirect Influence

Philip Adler, Casey Falk, Sorelle A. Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon George Smith, Suresh Venkatasubramanian

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Auditing Black-Box Models for Indirect InfluenceOriginal[Construction of a multi-copy Pichia expression vector and expression of human brain-derived neurotrophic factor in Pichia pastoris].1Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models42Identifying Significant Predictive Bias in Classifiers38The ethics of algorithms: Mapping the debate3445Algorithmic Transparency via Quantitative Input Influence42Auditing Search Engines for Differential Satisfaction Across Demographics68Anti-discrimination learning: a causal modeling-based framework48Bias In, Bias Out? Evaluating the Folk Wisdom38A Causal Framework for Discovering and Removing Direct and Indirect Discrimination152Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations496Interpretability of deep learning models: A survey of results422Achieving Non-Discrimination in Data Release59On Ensuring that Intelligent Machines Are Well-Behaved9Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation22Black-Box Model Explained Through an Assessment of Its Interpretable Features11Comparison-Based Inverse Classification for Interpretability in Machine Learning133Considerations for Evaluation and Generalization in Interpretable Machine Learning162Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges242Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks33Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute5Beyond Sparsity: Tree Regularization of Deep Models for Interpretability195Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients589Examining CNN Representations With Respect to Dataset Bias90Explainable artificial intelligence: A survey1130A Hierarchical Bayesian Model for Personalized Survival Predictions18Equity of Attention: Amortizing Individual Fairness in Rankings26On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning8EVE: explainable vector based embedding technique using Wikipedia34Equity of Attention335Posthoc Interpretability of Learning to Rank Models using Secondary Training Data34Achieving Non-Discrimination in Prediction23Panel10Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach159AURORA: Auditing PageRank on Large Graphs14Distill-and-Compare125A survey of methods for explaining black box models5022Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning383Euler Recurrent Neural Network: Tracking the Input Contribution to Prediction on Sequential Data0Sparse Robust Regression for Explaining Classifiers10Value-Based Core Areas of Trustworthiness in Online Services3A comparative study of fairness-enhancing interventions in machine learning586Explaining Explanations in AI759Artificial intelligence, bias and clinical safety947Fairness risk measures41A Framework for Benchmarking Discrimination-Aware Models in Machine Learning16Actionable Auditing647Noise-tolerant fair classification30An Evaluation of the Human-Interpretability of Explanation117An algorithm for removing sensitive information: Application to race-independent recidivism prediction140Just in Time: Personal Temporal Insights for Altering Model Decisions4Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model11“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems9Does Object Recognition Work for Everyone?95Exploiting patterns to explain individual predictions18Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data614Disentangling Influence: Using Disentangled Representations to Audit Model Predictions16Axiomatic Characterization of Data-Driven Influence Measures for Classification13Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making128Auditing Data Provenance in Text-Generation Models187Improving the Quality of Explanations with Local Embedding Perturbations25AI and Discrimination. A Proposal for a Compliance System for Protecting Privacy and Equality0What's in the box? Explaining the black-box model through an evaluation of its interpretable features2Understanding community structure in layered neural networks20Optimizing for Interpretability in Deep Neural Networks with Tree Regularization8Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis219ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia22Fuzzy logic interpretation of quadratic networks23Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms51Clinical implementation of AI technologies will require interpretable AI models113Ethical Considerations in AI-Based Recruitment132Explaining black box decisions by Shapley cohort refinement26Discerning Edge Influence for Network Embedding6Preventing undesirable behavior of intelligent machines142Leveraging Semi-Supervised Learning for Fairness using Neural Networks9Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI10111Fair Learning: an optimal transport based approach0Fairness in Machine Learning291Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study21The Citizen and the Automated State: Exploring the Implications of Algorithmic Decision-making in the New Zealand Public Sector0The Citizen and the Automated State: Exploring the Implications of Algorithmic Decision-making in the New Zealand Public Sector0Effect of Feature Hashing on Fair Classification1Indirect Auditing Methods for Individuals Subject to Income Tax2An empirical study on the perceived fairness of realistic, imperfect machine learning models110Whose tweets are surveilled for the police10Explaining Supervised Learning Models: A Preliminary Study on Binary Classifiers4On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research95Addressing multiple metrics of group fairness in data-driven decision making7Forward and backward feature selection for query performance prediction17An Information-Theoretic Quantification of Discrimination with Exempt Features18An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges74Review of Mathematical frameworks for Fairness in Machine Learning17Explainable Artificial Intelligence: a Systematic Review86How else can we define Information Flow in Neural Circuits?3Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning2Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Are you sure? Prediction revision in automated decision‐making7Algorithmic bias: on the implicit biases of social technology232Learning fair models and representations4Fair Learning : an optimal transport based approach0ORES82Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability9Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes93Contemporary Topics in Patient Safety - Volume 17Digital Health and Healthcare Quality: A Primer on the Evolving 4th Industrial Revolution5Risk mitigation in algorithmic accountability: The role of machine learning copies13Towards Auditability for Fairness in Deep Learning0Self-Explaining Structures Improve NLP Models24Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges6Algorithmic Factors Influencing Bias in Machine Learning28Explainable Artificial Intelligence for Tabular Data: A Survey133MIRAI: A Modifiable, Interpretable, and Rational AI Decision Support System1Pattern Recognition. ICPR International Workshops and Challenges26Systematic Literature Review of Fairness in Learning Analytics and Application of Insights in a Case Study8The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services22Relative Feature Importance65The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations50A Survey on the Explainability of Supervised Machine Learning990Explaining Black-Box Models for Biomedical Text Classification26Removing biased data to improve fairness and accuracy10Auditing Network Embedding: An Edge Influence Based Approach5Epistemic values in feature importance methods35On Interpretability of Artificial Neural Networks: A Survey561Machine learning for quantum physics and quantum physics for machine learning1Modeling patient-related workload in the emergency department using electronic health record data11Removing the Influence of Group Variables in High-Dimensional Predictive Modelling18To Split or not to Split: The Impact of Disparate Treatment in Classification18Cohort Shapley value for algorithmic fairness3SISSOS: intervention of tabular data and its applications3Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions20Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals81Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)2Black box algorithms and the rights of individuals: no easy solution to the “explainability” problem39Fairness-Aware Decision Tree Editing Based on Mixed- Integer Linear Optimization4Towards Accountability: Providing Intelligible Explanations in Autonomous Driving38On the Privacy Risks of Model Explanations111Deep fair models for complex data: Graphs labeling and explainable face recognition25Classification of Explainable Artificial Intelligence Methods through Their Output Formats152Fairness Under Feature Exemptions: Counterfactual and Observational Measures11Longitudinal Distance: Towards Accountable Instance Attribution0A Survey on Neural Network Interpretability911Optimizing for Interpretability in Deep Neural Networks with Tree Regularization21Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies51AssistML: A Concept to Recommend ML Solutions for Predictive Use Cases3Analysis of the User Experience with a Multiperspective Tool for Explainable Machine Learning in Light of Interactive Principles4Ethics-based auditing of automated decision-making systems: intervention points and policy implications62Explainable artificial intelligence: a comprehensive review969Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography30ESG score prediction through random forest algorithm138Neural generators of sparse local linear models for achieving both accuracy and interpretability4A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques68

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  1. [Construction of a multi-copy Pichia expression vector and expression of human brain-derived neurotrophic factor in Pichia pastoris].

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  2. Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models

    arXiv (Cornell University) · 2016-11-15

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  3. Identifying Significant Predictive Bias in Classifiers

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  4. The ethics of algorithms: Mapping the debate

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  5. Algorithmic Transparency via Quantitative Input Influence

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  6. Auditing Search Engines for Differential Satisfaction Across Demographics

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  7. Anti-discrimination learning: a causal modeling-based framework

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  8. Bias In, Bias Out? Evaluating the Folk Wisdom

    arXiv (Cornell University) · 2017-07-25

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  9. A Causal Framework for Discovering and Removing Direct and Indirect Discrimination

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  10. Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

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  11. Interpretability of deep learning models: A survey of results

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  12. Achieving Non-Discrimination in Data Release

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  13. On Ensuring that Intelligent Machines Are Well-Behaved

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  14. Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

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  15. Black-Box Model Explained Through an Assessment of Its Interpretable Features

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  16. Comparison-Based Inverse Classification for Interpretability in Machine Learning

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  17. Considerations for Evaluation and Generalization in Interpretable Machine Learning

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  18. Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

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  19. Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

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  20. Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute

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  21. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

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  22. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

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  23. Examining CNN Representations With Respect to Dataset Bias

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  24. Explainable artificial intelligence: A survey

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  25. A Hierarchical Bayesian Model for Personalized Survival Predictions

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  26. Equity of Attention: Amortizing Individual Fairness in Rankings

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  27. On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning

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  28. EVE: explainable vector based embedding technique using Wikipedia

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  29. Equity of Attention

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  30. Posthoc Interpretability of Learning to Rank Models using Secondary Training Data

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  31. Achieving Non-Discrimination in Prediction

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  32. Panel

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  43. Artificial intelligence, bias and clinical safety

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  44. Fairness risk measures

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  45. A Framework for Benchmarking Discrimination-Aware Models in Machine Learning

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  47. Noise-tolerant fair classification

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