Learning Credible Models

Wang, Jiaxuan; Oh, Jeeheh; Wang, Haozhu; Wiens, Jenna

doi:10.1145/3219819.3220070

Cited by 25 publications

(30 citation statements)

References 28 publications

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“…For instance, if we had better ways of representing and exploring the Rashomon set (see Challenge 9), domain experts might be able to search within it effectively, without fear of leaving that set and producing a suboptimal model. If we knew domain experts' views about the importance of features, we should be able to incorporate that through regularization [307]. Better interfaces might elicit better constraints from domain experts and incorporate such constraints into the models.…”

Section: Sophisticated Rounding Methodsmentioning

confidence: 99%

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin¹,

Chen²,

Chen³

et al. 2022

Statist. Surv.

321

202

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Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees;(2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.

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Section: Sophisticated Rounding Methodsmentioning

confidence: 99%

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin¹,

Chen²,

Chen³

et al. 2022

Statist. Surv.

321

202

View full text Add to dashboard Cite

show abstract

“…We will discuss several forms of interpretable machine learning models for different applications below, but there can never be a single definition; e.g., in some domains, sparsity is useful, and in others is it not. There is a spectrum between fully transparent models (where we understand how all the variables are jointly related to each other) and models that are lightly constrained in model form (such as models that are forced to increase as one of the variables increases, or models that, all else being equal, prefer variables that domain experts have identified as important, see [12]).…”

Section: Introductionmentioning

confidence: 99%

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

2019

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Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.

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“…In fact, the nocturnal temperature dysregulation as an age-related sleep disturbance contributes to fragmentation of sleep which is a common predisposing factor for sleep complaints in caregivers [9]. Since importance features provided by models agree with well-established CPWD sleep studies, the proposed models can be applicable for CPWD [93].…”

Section: Performance Evaluationmentioning

confidence: 62%

Sleep quality prediction in caregivers using physiological signals

Sadeghi

Banerjee

Hughes

et al. 2019

Computers in Biology and Medicine

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Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

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Learning Credible Models

Cited by 25 publications

References 28 publications

Interpretable machine learning: Fundamental principles and 10 grand challenges

Interpretable machine learning: Fundamental principles and 10 grand challenges

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Sleep quality prediction in caregivers using physiological signals

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