With the inevitable proliferation of AI-based automation, ethical considerations become of paramount importance. How do we ensure that AI-powered technologies do not propagate unfair biases in a fully automated way? In this session, panelists will discuss potential pitfalls of automation, processes that businesses should implement to avoid these pitfalls, definitions of fairness, and the incredible opportunity we have as a society, to instill fairness as a first class construct into the systems we build.
Chandra Khatri is a Senior AI Scientist at Uber AI driving Conversational AI efforts at Uber. Prior to Uber, he was the Lead AI Scientist at Alexa and was driving the Science for the Alexa Prize Competition, which is a $3.5 Million university competition for advancing the state of Conversational AI. Some of his recent work involves Open-domain Dialog Planning and Evaluation, Conversational Speech Recognition, Conversational Natural Language Understanding, and Sequential Modeling.
Prior to Alexa, Chandra was a Research Scientist at eBay, wherein he led various Deep Learning and NLP initiatives such as Automatic Text Summarization and Automatic Content Generation within the eCommerce domain, which has lead to significant gains for eBay. He holds degrees in Machine Learning and Computational Science & Engineering from Georgia Tech and BITS Pilani.
Anna Bethke is the Head of AI for Social Good of Intel's Artificial Intelligence Products Group where she is establishing partnerships with social impact organizations; enabling their missions with Intel's technologies and AI expertise. She is also actively involved in the AI Ethics discussion, collaborating on research surrounding the design of fair, transparent, ethical, and accessible AI systems. In her previous role as a deep learning data scientist she was a member of the Intel AI Lab, developing deep learning NLP algorithms as part of the NLP Architect open source repository. Anna received an M.S and B.S. in Aerospace Engineering from MIT in 2009 and 2007 respectively and previously worked as a data scientist at MIT Lincoln Labs, Argonne National Labs, and Lab41.
Shubha Nabar is a Senior Director of Data Science at Salesforce Einstein where she and her team make machine learning technologies accessible to the hundreds of thousands of businesses that use Salesforce every day. In 2017, she was featured as one of 20 Incredible Women Advancing AI Research by Forbes Magazine. She has over a decade of experience building data products and data science teams at Microsoft, LinkedIn, and Salesforce. Previously, she received her Ph.D. in Computer Science from Stanford University.
Londa Schiebinger is the John L. Hinds Professor of History of Science at Stanford University and directs the EU/US Gendered Innovations in Science, Health & Medicine, Engineering, and Environment project. She is a leading international expert on gender in science and technology and has addressed the United Nations on the topic of “Gender, Science, and Technology.” She is an elected member of the American Academy of Arts and Sciences and the recipient of numerous prizes and awards, including the prestigious Alexander von Humboldt Research Prize and Guggenheim Fellowship. Her work on Gendered Innovations harnesses the creative power of sex and gender analysis to enhance excellence and reproducibility in science and technology. See AI can be Sexist and Racist—It’s Time to Make it Fair by Londa Schiebinger and James Zou Nature, 559.7714 (2018), 324-326.