abstract

Algorithms for interpretable machine learning

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Cynthia RudinAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Page 1519

https://doi.org/10.1145/2623330.2630823

Published: 24 August 2014 Publication History

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Abstract

It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.

I will discuss recent work on interpretable predictive modeling with decision lists and sparse integer linear models. I will describe several approaches, including an algorithm based on discrete optimization, and an algorithm based on Bayesian analysis. I will show examples of interpretable models for stroke prediction in medical patients and prediction of violent crime in young people raised in out-of-home care.

Collaborators are Ben Letham, Berk Ustun, Stefano Traca, Siong Thye Goh, Tyler McCormick, and David Madigan.

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Polat M(2024)KAMU YÖNETİMİNDE ALGORİTMALARIN EGEMENLİĞİ: ALGOKRASİ VE TEHDİTLERİKamu Yönetimi ve Teknoloji Dergisi10.58307/kaytek.14950106:2(194-219)Online publication date: 19-Jul-2024
https://doi.org/10.58307/kaytek.1495010
Chang TXia HMahajan SMahajan RMaisog JVattikuti SChow CChang J(2024)Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or deathPLOS ONE10.1371/journal.pone.030287119:5(e0302871)Online publication date: 9-May-2024
https://doi.org/10.1371/journal.pone.0302871
Milani STopin NVeloso MFang F(2024)Explainable Reinforcement Learning: A Survey and Comparative ReviewACM Computing Surveys10.1145/361686456:7(1-36)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616864
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Index Terms

Algorithms for interpretable machine learning
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2014

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Polat M(2024)KAMU YÖNETİMİNDE ALGORİTMALARIN EGEMENLİĞİ: ALGOKRASİ VE TEHDİTLERİKamu Yönetimi ve Teknoloji Dergisi10.58307/kaytek.14950106:2(194-219)Online publication date: 19-Jul-2024
https://doi.org/10.58307/kaytek.1495010
Chang TXia HMahajan SMahajan RMaisog JVattikuti SChow CChang J(2024)Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or deathPLOS ONE10.1371/journal.pone.030287119:5(e0302871)Online publication date: 9-May-2024
https://doi.org/10.1371/journal.pone.0302871
Milani STopin NVeloso MFang F(2024)Explainable Reinforcement Learning: A Survey and Comparative ReviewACM Computing Surveys10.1145/361686456:7(1-36)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616864
Chen WWang SJiang CWang SHao L(2024)Research on Explainability Methods for Unmanned Combat Decision-Making ModelsIEEE Access10.1109/ACCESS.2024.340961612(83502-83512)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3409616
Zhang R(2024)Toward interpretable machine learning: evaluating models of heterogeneous predictionsAnnals of Operations Research10.1007/s10479-024-06033-1Online publication date: 10-May-2024
https://doi.org/10.1007/s10479-024-06033-1
Chaudhary MAfshar Alam MZafar S(2024)Explainable Artificial Intelligence Insight: An Orderly SurveyProceedings of International Conference on Recent Trends in Computing10.1007/978-981-97-1724-8_11(111-122)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-981-97-1724-8_11
Löfström HLöfström TJohansson USönströd C(2023)Investigating the impact of calibration on the quality of explanationsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-023-09837-2Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10472-023-09837-2
Kim YScianna JKnowles M(2023)How Can We Co-design Learning Analytics for Game-Based Assessment: ENA AnalysisAdvances in Quantitative Ethnography10.1007/978-3-031-31726-2_15(214-226)Online publication date: 29-Apr-2023
https://doi.org/10.1007/978-3-031-31726-2_15
Freeborough Wvan Zyl T(2022)Investigating Explainability Methods in Recurrent Neural Network Architectures for Financial Time Series DataApplied Sciences10.3390/app1203142712:3(1427)Online publication date: 28-Jan-2022
https://doi.org/10.3390/app12031427
Balagopalan AZhang HHamidieh KHartvigsen TRudzicz FGhassemi M(2022)The Road to Explainability is Paved with Bias: Measuring the Fairness of ExplanationsProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533179(1194-1206)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533179
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