Forough Poursabzi-Sangdeh

About Forough.

Forough Poursabzi-Sangdeh is a Principal Applied Scientist in Microsoft's Office of Chief Scientific Officer. Her research sits at the intersection of AI, people, and society, drawing on computer science, psychology, social sciences, and human-computer interaction to identify and mitigate risks in AI systems. Recently, her work has centered on the psychological risks posed by AI and translating research findings into evidence-based policy and design guidance. Forough co-leads a cross-company initiative on the psychological influences of AI, with the goal of shaping the responsible development of AI chatbots by focusing on safeguarding user wellbeing and fostering healthy human-AI interaction.

Forough holds a PhD in Computer Science from the University of Colorado Boulder, where she specialized in human-centered machine learning, and a BE in Computer Engineering from the University of Tehran.

Beyond research, Forough has been interested and active with efforts on diversity and inclusion in computing. In 2019, she was a co-organizer of the Women in Machine Learning (WiML) workshop at NeurIPS.

Publications.

Aligning Offline Metrics and Human Judgments of Value of AI-Pair Programmers
Victor Dibia, Adam Fourney, Gagan Bansal, Forough Poursabzi-Sangdeh, Han Liu, Saleema Amershi.
[Under review], 2022

Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna Wallach.
CHI, 2021 (A shorter version appeared at the NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments, 2017)

Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miroslav Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf.
Communications of the ACM (CACM), 2021

Expanding the scope of reproducibility research through data analysis replications
Jake M. Hofman, Daniel G.Goldstein, Siddhartha Sen, Forough Poursabzi-Sangdeh.
WWW workshop on Innovative Ideas in Data Science, 2020

A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, Hanna Wallach.
CHI workshop on Human-Centered Approaches to Fair and Responsible AI, 2020

Attending to the Problem of Uncertainty in Current and Future Health Wearables
Bran Knowles, Alison Smith-Renner, Forough Poursabzi-Sangdeh, Di Lu, Halimat Alabi.
Communications of the ACM (CACM), 2018

Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels
Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Leah Findlater, Jordan Boyd-Graber, Niklas Elmqvist.
Transactions of the Association for Computational Linguistics (TACL), 2017

ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling
Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, Kevin Seppi.
Association for Computational Linguistics (ACL), 2016

(Code publicly available, now being used by Snagajob)

Human-Centered and Interactive: Expanding the Impact of Topic Models
Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Niklas Elmqvist, Kevin Seppi, Leah Findlater.
CHI wokrshop on Human-Centered Machine Learning, 2016

Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations
Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, Jeffrey Heer.
NIPS Workshop on Human-Propelled Machine Learning, 2014

On Clustering Heterogeneous Networks
Forough Poursabzi-Sangdeh and Ananth Kalyanaraman.
SIAM Workshop on Network Science, 2013

Design and Empirical Evaluation of Interactive and Interpretable Machine Learning
Forough Poursabzi-Sangdeh
University of Colorado Boulder, 2018

Activities.

Reviewer and Program Committees
Journal of Artificial Intelligence (JAI), Microsoft TechFest 2020 proposal reviewer for the Empowering Experiences and Productivity theme, ACL 2020, NeurIPS 2019 Reproducibility Challenge, AAAI 2020, IJCAI 2019, 2020, CSCW 2019, NeurIPS 2019 (selected as a top reviewer), ICML 2019, ACM Transactions on Computing Education 2018, AISTATS 2018, EMNLP 2018, COLING 2018, EACL 2017, Debugging Machine Learning Models workshop at ICLR 2019, Explainable Smart Systems workshop at Intelligent User Interfaces (IUI) 2018, 2019, 2020, AI for Social Good workshop at NeurIPS 2019, Advances in Interpretable Machine Learning and Artificial Intelligence workshop at KDD 2019, Black in AI workshop at NeurIPS 2017 and 2018

Women in Computer Science
Senior program and mentorship chair, 14^th Women in Machine Learning (WiML) workshop co-located with NeurIPS 2019

Mentor
Microsoft Research Data Science Summer School, Summer 2019