Home Organizers

Session With Nithya Sambasivan

Topic: The Myopia of Model Centrism

17 November, 2021 at 11:00 AM, EST

The registration link is here;

Nithya Sambasivan, Research Scientist, Google Research

Brief Bio: Nithya Sambasivan is a Research Scientist at PAIR, Google Research and leads the human-computer interaction (HCI) group at the India lab. Her current research focuses on designing responsible AI systems by focusing on the humans of the AI/ML pipeline, specifically in the non-West. Her research is seminal to Google's products and strategy for emerging markets, while also winning numerous best paper awards and nominations at top-tier computing conferences. Nithya has a PhD. in Information and Computer Sciences from UC Irvine.

About the talk: AI models seek to intervene in increasingly higher stakes domains, such as cancer detection and microloan allocation. What is the view of the world that guides AI development in high risk areas, and how does this view regard the complexity of the real world? In this talk, I will present results from my multi-year inquiry into how fundamentals of AI systems---data, expertise, and fairness---are viewed in AI development. I pay particular attention to developer practices in AI systems intended for low-resource communities, especially in the Global South, where people are enrolled as labourers or untapped DAUs. Despite the inordinate role played by these fundamentals on model outcomes, data work is under-valued; domain experts are reduced to data-entry operators; and fairness and accountability assumptions do not scale past the West. Instead, model development is glamourised, and model performance is viewed as the indicator of success. The overt emphasis on models, at the cost of ignoring these fundamentals, leads to brittle and reductive interventions that ultimately displace functional and complex real-world systems in low-resource contexts. I put forth practical implications for AI research and practice to shift away from model centrism to enabling human ecosystems; in effect, building safer and more robust systems for all.

Relevant papers:

Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., Aroyo, L. "Everyone wants to do the model work, not the data work": Data Cascades in High-stakes AI CHI 2021. https://dl.acm.org/doi/10.1145/3411764.3445518

Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., Prabhakaran, V. Re-imagining Algorithmic Fairness in India and Beyond. FaccT 2021. https://dl.acm.org/doi/10.1145/3442188.3445896