Put AI to Work
April 15-18, 2019
New York, NY

Manipulating and measuring model interpretability

Forough Poursabzi-Sangdeh (Microsoft Research NYC)
1:50pm2:30pm Thursday, April 18, 2019
Interacting with AI
Location: Regent Parlor
Secondary topics:  Ethics, Privacy, and Security, Interfaces and UX
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Who is this presentation for?

  • Machine learning scientists and practitioners and user experience designers for machine learning

Level

Beginner

Prerequisite knowledge

  • A basic understanding of supervised machine learning and human-subject experiments

What you'll learn

  • Explore experiments and results illustrating why machine learning models should be empirically evaluated and guided the behavior of end users and not the intuitions of modelers

Description

Machine learning is increasingly used to make decisions that affect people’s lives in critical domains like criminal justice, fair lending, and medicine. While most of the research in machine learning focuses on improving the performance of models on held-out datasets, this is seldom enough to convince end users that these models are trustworthy and reliable in the wild. To address this problem, a new line of research has emerged that focuses on developing interpretable machine learning methods and helping end users make informed decisions. Despite the growing body of work in developing interpretable models, there is still no consensus on the definition and quantification of interpretability.

Forough Poursabzi-Sangdeh argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. Forough approaches the problem of interpretability from an interdisciplinary perspective built on decades of research in psychology, cognitive science, and social science to understand human behavior and trust. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users’ behavior. The findings emphasize the importance of studying how models are presented to people and empirically verifying that interpretable models achieve their intended effects on end users.

Photo of Forough Poursabzi-Sangdeh

Forough Poursabzi-Sangdeh

Microsoft Research NYC

Forough Poursabzi-Sangdeh is a postdoctoral researcher at Microsoft Research New York City. She works in the interdisciplinary area of interpretable and interactive machine learning, collaborating with psychologists to study human behavior when interacting with machine learning models. She uses these insights to design machine learning models that humans can use effectively. She’s also interested in several aspects of fairness, accountability, and transparency in machine learning and their effect on users’ decision-making process. Forough holds a BE in computer engineering from the University of Tehran and a PhD in computer science from the University of Colorado at Boulder.

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