Mahima Pushkarna is a product and design leader who brings clarity and structure to complex AI systems, guiding design at the forefront of the most challenging problems in frontier AI.
Mahima believes that design is a powerful tool for centering people and societies at the heart of technology-driven progress. Drawing on a mix of human-centered, participatory, and speculative design practices, she defines new lines of work and builds multi-disciplinary teams of designers, researchers, strategists, and subject matter experts. Together, they have translated ambiguous problems into tractable design spaces resulting in open-source tools, enterprise AI features for enterprise products, and research papers published at top-tier venues.
As a Staff Designer at Google DeepMind, Mahima leads the design of evaluations for social and group intelligence, setting new directions for how long-lived AI agents should behave in groups. She also led a sweeping update to the People + AI Guidebook for generative AI and has presented its foundational design principles and cross-disciplinary workflows at Google I/O.
Mahima joined Google Brain in 2017 as its first full-time designer. As a founding member of the People + AI Research initiative, she led the 0-to-1 product development of interpretability tools, including the What-If Tool and the Learning Interpretability Tool, which were used by companies and universities such as Hugging Face, Yahoo!, Disney, GoPro, MIT and Harvard University. She later created Data Cards and the Data Cards Playbook, the industry's defining framework for dataset transparency, which has been adopted in clinical healthcare, autonomous mobility, financial services, for EU AI Act compliance, and at the former Joint Artificial Intelligence Center (JAIC).
Mahima holds a Masters in Information Design and Data Visualization and began her career in visual design and advertising. Prior to Google, Mahima worked as a product designer at Innovation by Design, a global think-tank and consulted at MIT's Mobile Experiences Lab.
Sometimes she writes on her Personal Blog and Google Design
Occasionally she speaks or runs workshops. Reach out here for speaking.
Mahima believes that design is a powerful tool for centering people and societies at the heart of technology-driven progress. Drawing on a mix of human-centered, participatory, and speculative design practices, she defines new lines of work and builds multi-disciplinary teams of designers, researchers, strategists, and subject matter experts. Together, they have translated ambiguous problems into tractable design spaces resulting in open-source tools, enterprise AI features for enterprise products, and research papers published at top-tier venues.
As a Staff Designer at Google DeepMind, Mahima leads the design of evaluations for social and group intelligence, setting new directions for how long-lived AI agents should behave in groups. She also led a sweeping update to the People + AI Guidebook for generative AI and has presented its foundational design principles and cross-disciplinary workflows at Google I/O.
Mahima joined Google Brain in 2017 as its first full-time designer. As a founding member of the People + AI Research initiative, she led the 0-to-1 product development of interpretability tools, including the What-If Tool and the Learning Interpretability Tool, which were used by companies and universities such as Hugging Face, Yahoo!, Disney, GoPro, MIT and Harvard University. She later created Data Cards and the Data Cards Playbook, the industry's defining framework for dataset transparency, which has been adopted in clinical healthcare, autonomous mobility, financial services, for EU AI Act compliance, and at the former Joint Artificial Intelligence Center (JAIC).
Mahima holds a Masters in Information Design and Data Visualization and began her career in visual design and advertising. Prior to Google, Mahima worked as a product designer at Innovation by Design, a global think-tank and consulted at MIT's Mobile Experiences Lab.
Sometimes she writes on her Personal Blog and Google Design
Occasionally she speaks or runs workshops. Reach out here for speaking.
Articles & Videos
The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation Article with Andrew Zaldivar, for the Google AI Blog. 2022
Mahima Pushkarna is making data easier to understand
Interview, The Keyword Blog, Google, 2022
Future Of Canada: Growth & Recovery Panel on AI, Globe and Mail, 2022
Participatory ML: Using PAIR Tools: What-If and Tensorflow.js
Talk, People + AI Research Symposium, Google, London, 2019.
How UX changes the world, One AI at a Time
Panel, UXPA Boston Annual Conference, 2019.
Through the Looking Glass World IA Day Boston 2019, Massachusetts College of Art & Design. 2019.
The What-If Tool
Talk, Google Developers Summit, Cambridge MA , 2019
Six AI Terms UXers Should Know
Article written with Reena Jana, for Google Design. 2018
Learning Machine Learning: Implications for Design
Talk, UXPA Boston 17th Annual User Experience Conference, 2018. Forbes Coverage
Machine Learning, Implications for DesignInvited Talk, Northeastern University, College of Arts, Media and Design. 2018.
Research & Publications
Pushkarna, M., Zaldivar, A. και Kjartansson, O. (2022) ‘Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI’, στο 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: Association for Computing Machinery (FAccT ’22), σσ. 1776–1826. doi: 10.1145/3531146.3533231.
Pushkarna, M. and Zaldivar, A., 2021. Data Cards: Purposeful and Transparent Documentation for Responsible AI. In 35th Conference on Neural Information Processing Systems (pp. 1776-1826).
Tenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E. and Yuan, A., 2020. The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models. arXiv preprint arXiv:2008.05122.
Ghassemi, M., Pushkarna, M., Wexler, J., Johnson, J. and Varghese, P., 2018. Clinicalvis: Supporting clinical task-focused design evaluation. arXiv preprint arXiv:1810.05798.
IEEE VIS 2019 - Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viegas, F., & Wilson, J. (2019). The What-If Tool: Interactive Probing of Machine Learning Models. arXiv preprint arXiv:1907.04135. Recording of presentation by James Wexler.
The What-If Tool: Code-free probing of machine learning models for fairness and interpretability, Workshop, ComputeFest 2019, Harvard University, January 2019
Mahima Pushkarna