Mahima Pushkarna is a design lead at the People + AI Research Initiative at Google. She brings design thinking and human-centered design into Human-AI Research, and has a rich history of leading product design & strategy projects in emerging technologies of large scope.

Mahima has designed tools and frameworks that make complex information useful and understandable and even fun – such as visualizing interpretability results for Machine Learning models and creating controls for Generative AI. These have been widely used to advance better development practices by companies like Huggingface, Google, Disney, Yahoo! and GoPro, as well as in academia by MIT and Harvard University.

Mahima believes that design can be a powerful tool for understanding and addressing the needs of people who are impacted by technology. Her work draws from a mix of human-centered, participatory, and speculative design practices to bridge the gap between upstream developer practices and their impact on end user experiences and society. Mahima is also interested in exploring the intersection of design, technology, and society, and is always looking for new ways to use design to make the world a better place.

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.

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 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