Mahima Pushkarna is a product and design lead whose work brings together human-centered design, data visualization, and research to help people understand, evaluate, and design emergent technologies. She brings clarity and structure to complex AI solutions, guiding design at the forefront of the most challenging problems in frontier AI.
Mahima believes that design can be a powerful tool to understand and address the needs of people and make the world a better place. She draws on a mix of human-centered, participatory, and speculative design practices to bridge the gap between upstream developer practices and end user experience.
Currently, Mahima is a Staff Designer at Google DeepMind, where she leads the research and design of evaluations of social and group intelligence, and establishes new ways to measure how long-lived AI systems behave in groups. She also led a sweeping update to the People + AI Guidebook, introducing foundational design principles and cross-disciplinary workflows to create intuitive, human-centered Generative AI experiences.
Previously, Mahima created Data Cards and the Data Cards Playbook, establishing the industry’s defining framework for dataset transparency, adopted across clinical healthcare, autonomous mobility, financial services, and governmental policy. She has led the 0-to-1 product development of state-of-the-art interpretability tools, such as the What If-Tool, Facets in Tensorboard, and the Learning Interpretability Tool used in Google’s Gemma family of models. Open-sourced, these tools have been widely used to advance better development practices by companies like Huggingface, Disney, Yahoo! and GoPro, as well as in academia by MIT and Harvard University.
Mahima holds a Masters in Information Design and Data Visualization. Prior to Google, Mahima worked as a product designer at Innovation by Design, a global think-tank, consulted at MIT's Design Lab, and designed visualization tools at Ion Interactive. She began her career in visual design and advertising in India, working on brand identity, packaging, editorial illustration, and print campaigns.
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 can be a powerful tool to understand and address the needs of people and make the world a better place. She draws on a mix of human-centered, participatory, and speculative design practices to bridge the gap between upstream developer practices and end user experience.
Currently, Mahima is a Staff Designer at Google DeepMind, where she leads the research and design of evaluations of social and group intelligence, and establishes new ways to measure how long-lived AI systems behave in groups. She also led a sweeping update to the People + AI Guidebook, introducing foundational design principles and cross-disciplinary workflows to create intuitive, human-centered Generative AI experiences.
Previously, Mahima created Data Cards and the Data Cards Playbook, establishing the industry’s defining framework for dataset transparency, adopted across clinical healthcare, autonomous mobility, financial services, and governmental policy. She has led the 0-to-1 product development of state-of-the-art interpretability tools, such as the What If-Tool, Facets in Tensorboard, and the Learning Interpretability Tool used in Google’s Gemma family of models. Open-sourced, these tools have been widely used to advance better development practices by companies like Huggingface, Disney, Yahoo! and GoPro, as well as in academia by MIT and Harvard University.
Mahima holds a Masters in Information Design and Data Visualization. Prior to Google, Mahima worked as a product designer at Innovation by Design, a global think-tank, consulted at MIT's Design Lab, and designed visualization tools at Ion Interactive. She began her career in visual design and advertising in India, working on brand identity, packaging, editorial illustration, and print campaigns.
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