Deployable AI (DAI)

Title: Contextualizing AI with Cross Cultural Perspectives

Abstract: Development and evaluation of generative AI models rely heavily on semi-structured data annotated by humans. Both the data and human perspectives involved in the process, thus play a key role in what is taken as ground truth by models. Historically, this perspective has been Western-oriented which leads to a lack of representation of global contexts and identities in models as well as evaluation strategies, and the risk of disregarding marginalized groups that are most significantly affected by implicit harms. Accounting for cross-cultural differences in interacting with technology is an important step for building and evaluating AI holistically.  We will talk through different strategies including community engaged approaches, along with survey experiments to capture a more diverse set of perspectives in data curation and benchmarking efforts. We will also zoom in on how socio-culturally aware AI research can fill in the gaps in fairness evaluations.

Bio: Sunipa Dev is a Senior Research Scientist at Google Research, its Responsible AI and Human-Centered Technologies organization, working at the intersection of language, society, and technology. Previously, she was an NSF Computing Innovation Fellow at UCLA, before which she completed her PhD from the University of Utah. Her research strives to ground evaluations of generative AI, especially language technologies, in real-world experiences of people and foster the inclusion of diverse, cross-cultural, and marginalized perspectives into AI pipelines. She is also an organizer of Widening NLP at *CL conferences which argues the importance of diversity and inclusion in NLP for better technologies of the future.

Talk Title: TBD

Bio: Dr. Li is an Assistant Professor (tenure-track) at the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC) starting August 2021. Before joining UBC， Dr. Li was a Postdoc Research Fellow in the Computer Science Department at Princeton University. Dr. Li obtained her PhD degree from Yale University in 2020. During her PhD studies, she was awarded the Advanced Graduate Leadership Scholarship. Dr. Li is leading the Trusted and Efficient AI (TEA) Lab. Her groups’ research interests range across the interdisciplinary files of deep learning and biomedical data analysis, aiming to improve the trustworthiness of AI systems for healthcare. 

Talk Title: TBD

Bio: Srijan is an Assistant Professor at CSE, College of Computing at Georgia Institute of Technology. My research expertise lies in developing AI, applied machine learning, and data mining methods. I build graphs, content (NLP, multimodal), and adversarial learning methods while utilizing terabytes of data from multiple online platforms spanning multiple modalities and languages. I innovate scalable and efficient methods for online safety by detecting and mitigating malicious actors (e.g., ban evaders, sockpuppets, coordinated campaigns, fraudsters) and dangerous content (e.g., misinformation, hate speech, fake reviews). At the same time, I develop methods to improve the security and safety of AI methods.

Talk Title: Lab to Launch - Overcoming the Challenges of Deploying Generative AI

Abstract: Deploying AI models at scale can be challenging, and there are a number of factors to consider. In this talk, Pranesh Srinivasan will discuss some of the key considerations for deploying generative AI, including cost, quality, latency, and fairness. He will also present some recent work on techniques for improving the efficiency and performance of generative AI models.

Bio:  Pranesh Srinivasan is a Senior Staff Software Engineer working at Google. He works on large-scale NLP modeling and infrastructure challenges in Featured Snippets and Search Quality. Previously, he was a Quant at Goldman Sachs on Program Trading, where he worked on models for pricing portfolios under uncertainty and adversarial risk. Pranesh holds a Bachelor’s and Master’s in Computer Science from IIT Madras.