WeCREATE: Inspiration Session 3 — Responsible AI

We are running WeCREATE Inspiration Session 3 on 18 May 2023 4pm BST, focusing on the important and relevant topic of Responsible AI.

This session will again feature inspiring panelists in the field of AI, Machine Learning, Engineering, Computer Sciences, and beyond. Learn from and engage with academics and professionals at the top of their fields, who are making their mark on the world with creativity and a sense of purpose every day.

Purpose of WeCREATE:

• Inspire female students and young professionals to embrace creative careers in Engineering, Comp Sci, Applied Maths and beyond.
• Dispelling widely held myths and established views about these areas.
• Highlight a diverse range of amazing female role models making their mark on the world.
• Created for women, and open to all – no-one should be discouraged from pursuing AI and machine learning.

We have three fantastic speakers joining us for this session:

• Dr Jenn Wortman Vaughan – Sr. Principal Researcher, Microsoft Research, NYC
• Dr Lea Goetz – Senior AI/ML Engineer, Responsible AI, GSK
• Dr Emily Denton – Staff Research Scientist, Google

The session will be hosted by: Prof Mihaela van der Schaar

More about our panelists

Dr Jenn Wortman Vaughan

Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. She currently focuses on Responsible AI—including transparency, interpretability, and fairness—as part of MSR’s FATE group and co-chair of Microsoft’s Aether Working Group on Transparency. Jenn’s research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn’s 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a variety of best paper awards. Jenn co-founded the Annual Workshop for Women in Machine Learning (WiML), which has been held each year since 2006, and recently served as Program Co-chair of NeurIPS 2021.

Dr Lea Goetz

Lea is a Senior AI/ML Engineer working on responsible AI in drug discovery and clinical applications. She initially joined GSK.ai as an AI Fellow, where her research focused on unsupervised representation learning, causal discovery and uncertainty estimation on real-world datasets.
Lea received her PhD in computational neuroscience from University College London, where she focused on the dendrites of single neurons as biological substrate for sparse input representations and learning algorithms. Prior to her PhD she studied Natural Sciences at the University of Cambridge.

Dr Emily Denton

Emily Denton (they/them) is a Staff Research Scientist at Google, within the Technology, AI, Society, and Culture team, where they study the sociocultural impacts of AI technologies and conditions of AI development. Their recent research centers on emerging text- and image-based generative AI, with a focus on data considerations and representational harms. Prior to joining Google, Emily received their PhD in Computer Science from the Courant Institute of Mathematical Sciences at New York University, where they focused on unsupervised learning and generative modeling of images and video. Prior to that, they received their B.S. in Computer Science and Cognitive Science at the University of Toronto. Though trained formally as a computer scientist, Emily draws ideas and methods from multiple disciplines and is drawn towards highly interdisciplinary collaborations, in order to examine AI systems from a sociotechnical perspective.

Note: All information is up-to-date at the time of posting.