Optimized Risk Scores

By Berk Ustun, Cynthia Rudin. 2017

57 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Mathematical Optimization

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “There are several methods that can solve (3) directly (Ustun et al., 2013; Ustun and Rudin, 2016, 2017, 2019; Rudin and Ustun, 2018).”
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, methodology): “The latest attempt at solving this problem is the RiskSLIM (Risk-Supersparse-Linear-Integer-Models) algorithm, which is a specialized cutting plane method that adds cutting planes only whenever the solution to a linear program is integer-valued, and otherwise performs branching [44].”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Figure 4: Scoring system for risk of recidivism from Rudin and Ustun [2018] [which grew out of Ustun and Rudin, 2017, Zeng et al., 2017, Ustun and Rudin, 2015].”
Learning Certifiably Optimal Rule Lists (2017, methodology): “, decision trees, along with scoring systems, tend to be popular forms of transparent models. Scoring systems are sparse linear models with integer coecients, and they can also be created from data (Ustun and Rudin, 2017, 2016). In some of our experiments, CORELS produces a DNF formula by coincidenc”
Predictive Multiplicity in Classification (2019, background): “Althoughinteger programming is computationally intractable, modern solvers can solve empirical risk minimization for many real-world problems [see e.g., 6, 37, 47–49].”
An Interpretable Model with Globally Consistent Explanations for Credit Risk (2018, methodology): “anation for Observation 1005 that has support of 990 observations. Figure 6: Case-based explanations. 4 Thoughts Future work: There are several possible extensions to our work. Methods like RiskSLIM [Ustun and Rudin, 2017] could make the subscale scores more interpretable by restricting to integer c”
Learning customized and optimized lists of rules with mathematical programming (2018, background): “linear models (as discussed by [64,65]), as well as other model forms, and for many other types of algorithms.”
Learning Optimized Risk Scores (2016, methodology): “…in domains such as: • Medicine: to assess the risk of mortality in intensive care (e.g., Moreno et al., 2005), critical physical conditions (e.g., adverse cardiac events, Six et al., 2008; Than et al., 2014) and mental illnesses (e.g., adult ADHD in Kessler et al. 2005; Ustun et al. 2017).”
Actionable Recourse in Linear Classification (2018, background): “1While transparency does not ensure recourse, simple models such as scoring systems and rule-based models [3, 28, 42, 49, 50] have the benefit that they allow end-users to check the feasibility of recourse without a computer.”
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (2018, background): “While recent work derives risk scores for predictive modeling (supervised learning) [48], our application of SUSTain extracts scores for unsupervised phenotyping based on unlabeled EHR data.”
Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice (2018, methodology): “In our studies of recidivism (62, 52, 54), we used the largest publicly available”
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022, background): “Examples include a scoring sheet [253] and linear regression.”
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance (2019, unknown): “…compatibility in the loss function (Bansal et al. 2019) and to combine tree-based regularization to learn a more interpretable model (Wu et al. 2018); the latter has found application in domains like fair classiﬁcation and healtcare (Dwork et al. 2012; Zafar et al. 2017; Ustun and Rudin 2017).”
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis (2019, methodology): “This optimization problem provides a recipe for creating a scoring system, which is a sparse linear model with integer coefficients (see [54]):”
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019, background): “Globally transparent alternatives like SLIM (Ustun and Rudin 2017) and CORELS (Angelino et al. 2018) enjoy some desirable mathematical properties, but do not scale well with the number of features.”
Human-in-the-Loop Interpretability Prior (2018, background): “Examples include decision tree depth [9], number of integer regression coefficients [30], amount of overlap between decision rules [13], and different kinds of sparsity penalties in neural networks [10, 24].”
A Survey on the Explainability of Supervised Machine Learning (2020, background): “nces SLIM Classication Rule-based Ustun and Rudin (2014) TILM Classication Rule-based Ustun and Rudin (2016) PILM Classication Linear model Ustun and Rudin (2016) RiskSLIM Classication Rule-based Ustun and Rudin (2017) Two-level Boolean Rules Classication Rule-based Su et al. (2015) BOA Classication”