AAAI 21 Conference Tutorial presented by Krishnaram Kenthapadi, Ben Packer, Mehrnoosh Sameki and. Nashlie Sephus. In this tutorial:
- We will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around web-based AI/ML systems.
- Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications.
- We will present case studies across different companies, spanning application domains such as search and recommendation systems, hiring, sales, lending, and fraud detection.
- We will emphasize that topics related to responsible AI are socio-technical, that is, they are topics at the intersection of society and technology. The underlying challenges cannot be addressed by technologists alone; we need to work together with all key stakeholders — such as customers of a technology, those impacted by a technology, and people with background in ethics and related disciplines — and take their inputs into account while designing these systems.
- Finally, based on our experiences in industry, we will identify open problems and research directions for the data mining/machine learning community.
The tutorial will consist of two parts:
- Responsible AI Foundations – 1.5 hours
- Case Studies – 1.5 to 2 hours
Krishnaram Kenthapadi is a Principal Scientist at Amazon AWS AI, where he leads the fairness, explainability, and privacy initiatives in Amazon AI platform. Prior to joining Amazon, he led similar efforts across different LinkedIn applications as part of the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. He has presented lectures/tutorials on privacy, fairness, and explainable AI in industry and instructed a course on AI at Stanford. Krishnaram received his Ph.D. in Computer Science from Stanford University.
Ben Packer is a Software Engineer in Research at Google AI, responsible for Fairness and Robustness engagements. He works at the intersection of research and product engagements, conducting research on fairness and robustness as well as implementing these practices directly into various Google products. He has contributed to the Machine Learning Fairness Education effort at Google that has reached thousands of employees and tens of thousands of external developers, has taught a Fairness module to developers across industry as part of Google’s CapitalG program, and presented a fairness tutorial at WWW ’19. He received his Ph.D. in Computer Science from the AI lab at Stanford University.
Mehrnoosh Sameki is a senior technical program manager at Microsoft, responsible for leading the product efforts on the open source machine learning interpretability and fairness toolkits (InterpretML and Fairlearn) and their platform integration within the Azure Machine Learning platform. She is also an adjunct assistant professor at Boston University, School of Computer Science, where she earned her PhD degree in 2017. She has presented at several industry forums (including Microsoft Build) and a fairness tutorial at KDD ’19.
Nashlie Sephus is the Applied Science Manager for Amazon’s Artificial Intelligence (AI) team focusing on fairness and identifying biases in technologies across the company. She formerly led the Amazon Visual Search team in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. This technology was a result of former startup Partpic (Atlanta) being acquired by Amazon, for which she was the Chief Technology Officer (CTO). Prior to working at Partpic, she received her Ph.D. from the School of Electrical and Computer Engineering at the Georgia Institute of Technology.