To build machine learning systems that are reliable, trustworthy, and fair, we must be able to provide relevant stakeholders with an understanding of how these systems work. Yet what makes a system \u201cintelligible\u201d is difficult to pin down. Intelligibility is a fundamentally human-centered concept that lacks a one-size-fits-all solution. Although many intelligibility techniques have been proposed in the machine learning literature, there are many more open questions about how best to provide stakeholders with the information they need to achieve their desired goals. In this chapter, we begin with an overview of the intelligible machine learning landscape and give several examples of the diverse ways in which needs for intelligibility can arise. We provide an overview of the techniques for achieving intelligibility that have been proposed in the machine learning literature. We discuss the importance of taking a human-centered strategy when designing intelligibility techniques or when verifying that these techniques achieve their intended goals. We also argue that the notion of intelligibility should be expanded beyond machine learning models to other components of machine learning systems, such as datasets and performance metrics. Finally, we emphasize the necessity of tight integration between the machine learning and human\u2013computer interaction communities.<\/p>\n","protected":false},"excerpt":{"rendered":"
To build machine learning systems that are reliable, trustworthy, and fair, we must be able to provide relevant stakeholders with an understanding of how these systems work. Yet what makes a system \u201cintelligible\u201d is difficult to pin down. Intelligibility is a fundamentally human-centered concept that lacks a one-size-fits-all solution. Although many intelligibility techniques have been […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193721],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-688662","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"MIT 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