Actionable Recourse in Linear Classification

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,110 · see citing works

Untitled work

(2016) · Cited by 2,334 · see citing works

Anchors: High-Precision Model-Agnostic Explanations

Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · cited 1× · see citing works

Usenix Security Symposium

(2011) · IEEE Software · doi:10.1109/ms.2011.67 · Cited by 2,038 · see citing works

The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

Yeh, I.-C., & Lien, C.-H. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.020 · Cited by 885 · see citing works

Stealing Machine Learning Models via Prediction APIs

Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016) · arXiv · doi:10.48550/arxiv.1609.02943 · Cited by 796 · see citing works

'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · cited 1× · see citing works

Proceedings of the National Conference on Artificial Intelligence

Jensen, S., Boley, D. L., Gini, M. L., & Schrater, P. (2005) · Innovative Applications of Artificial Intelligence · Cited by 612 · see citing works

Untitled work

(2014) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 524 · see citing works

Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for

Edwards, L., & Veale, M. (2017) · doi:10.31228/osf.io/97upg · Cited by 524 · see citing works

Accountability of AI Under the Law: The Role of Explanation

Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S. J., O'Brien, D., Scott, K., Shieber, S., Waldo, J., Weinberger, D., et al. (2017) · doi:10.2139/ssrn.3064761 · Cited by 457 · see citing works

A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI

Wachter, S., & Mittelstadt, B. (2018) · Columbia Academic Commons (Columbia University) · doi:10.7916/cblr.v2019i2.3424 · Cited by 454 · see citing works

The Intuitive Appeal of Explainable Machines

Selbst, A. D., & Barocas, S. (2018) · doi:10.2139/ssrn.3126971 · Cited by 392 · see citing works

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

Siddiqi, N. (2005) · Cited by 383 · see citing works

Untitled work

(2013) · eYLS (Yale Law School) · Cited by 376 · see citing works

Supersparse linear integer models for optimized medical scoring systems

Ustun, B., & Rudin, C. D. (2015) · Machine Learning · doi:10.1007/s10994-015-5528-6 · Cited by 316 · see citing works

Analysis of classifiers’ robustness to adversarial perturbations

Fawzi, A., Fawzi, O., & Pascal Frossard, B. (2017) · Machine Learning · doi:10.1007/s10994-017-5663-3 · Cited by 289 · see citing works

Explaining Data-Driven Document Classifications1

Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264 · see citing works

Assessing demand for intelligibility in context-aware applications

Lim, B. Y., & Dey, A. K. (2009) · Proceedings of the 11th international conference on Ubiquitous computing · doi:10.1145/1620545.1620576 · Cited by 246 · see citing works

Slave to the Algorithm? Why a Right to Explanationn is Probably Not the Remedy You are Looking for

Edwards, L., & Veale, M. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2972855 · Cited by 244 · see citing works

Strategic Classification

Hardt, M., Megiddo, N., Papadimitriou, C. H., & Wootters, M. K. (2016) · Conference on Innovations in Theoretical Computer Science · doi:10.1145/2840728.2840730 · Cited by 238 · see citing works

Untitled work

(2018) · Cited by 230 · see citing works

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Verma, S. (2019) · Vikalpa: The Journal for Decision Makers · doi:10.1177/0256090919853933 · Cited by 219 · see citing works

Data retention and the panoptic society: the social benefits of forgetfulness

Blanchette, J.-F., & Johnson, D. G. (2002) · The Information Society · doi:10.1080/01972240252818216 · Cited by 178 · see citing works

'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

(2018) · doi:10.31235/osf.io/9wqxr · Cited by 161 · see citing works

Model Reconstruction from Model Explanations

Milli, S., Schmidt, L., Dragan, A. D., & Hardt, M. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287562 · Cited by 151 · see citing works

The Social Cost of Strategic Classification

Milli, S., Miller, J. P., Dragan, A. D., & Hardt, M. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287576 · Cited by 140 · see citing works

Help wanted: an examination of hiring algorithms, equity, and bias

Bogen, M., & Rieke, A. (2018) · Cited by 138 · see citing works

The Disparate Effects of Strategic Manipulation

Hu, L. J., Immorlica, N., & Vaughan, J. W. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287597 · Cited by 135 · see citing works

Credit Risk Scorecards

(2012) · Wiley eBooks · doi:10.1002/9781119201731 · Cited by 127 · see citing works

Economics, Fairness and Algorithmic BiasPreprint

Cowgill, B., & Tucker, C. E. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3361280 · Cited by 126 · see citing works

Strategic Classification from Revealed Preferences

Dong, J., Roth, A., Schutzman, Z., Waggoner, B., & Wu, Z. S. (2018) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3219166.3219193 · Cited by 113 · see citing works

How Do Classifiers Induce Agents to Invest Effort Strategically?

Kleinberg, J. M., & Raghavan, M. (2019) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3328526.3329584 · Cited by 109 · see citing works

On handling indicator constraints in mixed integer programming

Belotti, P., Bonami, P., Fischetti, M., Lodi, A., Monaci, M., Nogales-Gómez, A., & Salvagnin, D. (2016) · Computational Optimization and Applications · doi:10.1007/s10589-016-9847-8 · Cited by 107 · see citing works

Untitled work

(2017) · doi:10.1145/3097983.3098047 · Cited by 104 · see citing works

Visual explanation of evidence in additive classifiers

Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Fyshe, A., Pearcy, B., Macdonell, C., & Anvik, J. (2006) · Cited by 100 · see citing works

Residual unfairness in fair machine learning from prejudiced data

Kallus, N., & Zhou, A. (2018) · arXiv · doi:10.48550/arxiv.1806.02887 · Cited by 83 · see citing works

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

Grgić-Hlača, N., Redmiles, E. M., Gummadi, K. P., & Weller, A. (2018) · arXiv · doi:10.48550/arxiv.1802.09548 · Cited by 75 · see citing works

Exact Rule Learning via Boolean Compressed Sensing

Malioutov, D., & Varshney, K. R. (2013) · Cited by 73 · see citing works

Special ordered sets and an application to gas supply operations planning

Tomlin, J. A. (1988) · Mathematical Programming · doi:10.1007/bf01589393 · Cited by 55 · see citing works

The Inverse Classification Problem

Aggarwal, C. C., Chen, C., & Han, J. (2010) · Journal of Computer Science and Technology · doi:10.1007/s11390-010-9337-x · Cited by 49 · see citing works

Optimized Risk Scores

Ustun, B., & Rudin, C. D. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098161 · Cited by 47 · see citing works

Hiring by Algorithm: Predicting and Preventing Disparate ImpactPreprint

Ajunwa, I., Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · SSRN Electronic Journal · Cited by 33 · see citing works

Predictive Analytics for City Agencies: Lessons from Children's Services

Shroff, R. (2017) · Big Data · doi:10.1089/big.2016.0052 · Cited by 27 · see citing works

10-year CVD risk prediction and minimization via InverseClassification

Chen, Y., Street, W. N., & Robinson, J. G. (2012) · doi:10.1145/2110363.2110430 · Cited by 13 · see citing works

How to reverse-engineer quality rankings

Chang, A., Rudin, C. D., Cavaretta, M. J., et al. (2012) · Machine Learning · doi:10.1007/s10994-012-5295-6 · Cited by 12 · see citing works

A Provable Algorithm for Learning Interpretable Scoring Systems

Sokolovska, N., Chevaleyre, Y., & Zucker, J. (2018) · Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine) · Cited by 12 · see citing works

Give Me Some Credit

Nambwenya, J., Solomon, A., & Le, T. V. (2014) · PDXScholar (Portland State University) · Cited by 8 · see citing works

Meeting the Equal Credit Opportunity Act's Specificity Requirement: Judgmental and Statistical Scoring Systems

Taylor, W. F. (1980) · eYLS (Yale Law School) · Cited by 8 · see citing works

Supervising Feature InfluencePreprint

Sen, S., Mardziel, P., Datta, A., & Fredrikson, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10815 · Cited by 6 · see citing works

Maximally Invariant Data Perturbation as ExplanationPreprint

Hara, S., Ikeno, K., Soma, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07004 · Cited by 6 · see citing works

Axiomatic Characterization of Data-Driven Influence Measures for Classification

Sliwinski, J., Strobel, M., & Zick, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301718 · Cited by 5 · see citing works

What is AI Literacy? Competencies and Design Considerations

Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · see citing works

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942287 · Cited by 884 · see citing works

Manipulating and Measuring Model Interpretability

Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728 · see citing works

Explainable machine learning in deployment

Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375624 · Cited by 633 · see citing works

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677 · see citing works

The Principles and Limits of Algorithm-in-the-Loop Decision Making

Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392 · see citing works

A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works

FACE: Feasible and Actionable Counterfactual Explanations

Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., & Flach, P. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375850 · Cited by 317 · see citing works

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.07213 · Cited by 306 · see citing works

Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models

Ferrara, E. (2023) · arXiv · doi:10.48550/arxiv.2304.03738 · Cited by 282

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267 · see citing works

Should ChatGPT be biased? Challenges and risks of bias in large language models

Ferrara, E. (2023) · First Monday · doi:10.5210/fm.v28i11.13346 · Cited by 282 · see citing works

Four principles of explainable artificial intelligence

Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262 · see citing works

Problems with Shapley-value-based explanations as feature importance measures

Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228 · see citing works

Efficient Search for Diverse Coherent Explanations

Russell, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287569 · Cited by 218 · see citing works

CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models

Sharma, S., Henderson, J., & Ghosh, J. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375812 · Cited by 226 · see citing works

Learning Model-Agnostic Counterfactual Explanations for Tabular Data

Pawelczyk, M., Broelemann, K., & Kasneci, G. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380087 · Cited by 167 · see citing works

Four Principles of Explainable Artificial Intelligence

Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020) · doi:10.6028/nist.ir.8312-draft · Cited by 136 · see citing works

Explainable Artificial Intelligence for Tabular Data: A Survey

Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works

On generating trustworthy counterfactual explanations

Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119898 · Cited by 122 · see citing works

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445088 · Cited by 117 · see citing works

Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness

chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2025) · ACM Computing Surveys · doi:10.1145/3675392 · Cited by 108 · see citing works

ViCE: visual counterfactual explanations for machine learning models

Gomez, O., Holter, S., Yuan, J., & Bertini, E. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377536 · Cited by 91 · see citing works

AI, Opacity, and Personal Autonomy

Vaassen, B. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00577-5 · Cited by 94 · see citing works

Transparency as design publicity: explaining and justifying inscrutable algorithms

Loi, M., Ferrario, A., & Viganò, E. (2021) · Ethics and Information Technology · doi:10.1007/s10676-020-09564-w · Cited by 79 · see citing works

Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability

Yurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 73 · see citing works

Algorithmic risk assessments can alter human decision-making processes in high-stakes government contexts

Green, B., & Chen, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479562 · Cited by 69 · see citing works

Explainable machine learning for public policy: Use cases, gaps, and research directions

Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73 · see citing works

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository · doi:10.17863/cam.69929 · Cited by 49 · see citing works

Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance

Bell, A., Nov, O., & Stoyanovich, J. (2023) · Data & Policy · doi:10.1017/dap.2023.8 · Cited by 44 · see citing works

Machine Learning Explainability for External Stakeholders

Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv · doi:10.48550/arxiv.2007.05408 · Cited by 41 · see citing works

Evaluating and Mitigating Discrimination in Language Model Decisions

Tamkin, A., Askell, A., Lovitt, L., Durmus, E., Joseph, N., Kravec, S., Nguyen, K., Kaplan, J., & Ganguli, D. (2023) · arXiv · doi:10.48550/arxiv.2312.03689 · Cited by 11 · see citing works

Algorithmic monoculture and social welfare

(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability

Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041

Livelihood Assets and Obstacles for Farmer Households in Minqin Oasis Based on a Survey Study

Li, Y. (2014) · Science, Economy, and Society

A Brief Bnalysis on the Main Problems and the Solutions of Current Livelihood of Our Country

Hu, Y. (2011)

Relationship Between Livelihood Capitals and Livelihood Strategies of Dalit

Giri, Y. N. (2022) · Contemporary Voice of Dalit · doi:10.1177/2455328x211060587 · Cited by 9 · see citing works

Optimal Telecommunications Network Expansion Using Mixed Linear Integer Programming

Fernández-Bravo, E., García-Sánchez, Á., Ortega-Mier, M., et al. (2022) · Lecture notes in management and industrial engineering · doi:10.1007/978-3-030-95967-8_15

The Air-to-Ground Tasks Assignment for Multi-UAV based Mixed Integer Linear Programming

De, Z. (2008) · Fire Control and Command Control