Hi! I’m Mahima.

I design new technologies and interfaces in AI. 

︎ Toronto, ON
I’m a design lead at the People + AI Research Initiative at Google AI, where I use design to shape how we frame key scientific and engineering questions for advanced technologies.

I craft tools and experiences that make complex information useful, understandable and even fun. My work has been widely used to advance better practices in machine learning by companies like Huggingface, Google, Disney, Yahoo! and GoPro, as well as in academia by MIT and Harvard University. I’ve taught Information Design and Data Visualization Northeastern University. Prior to Google, I 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.

Sometimes I write on my Personal Blog and Google Design
Occasionally I’ll speak or give a workshop. 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 2019Wexler, 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:1907.04135Recording 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