While machine learning systems strive to mirror and predict real life as closely as possible, the people behind these models do not represent the real world. Despite this rapid forecasted growth, women still only make up a measly 12% of the ML workforce. Amid the urgency for big business to innovate and as AI permeates all aspects of life and society, a new look at diversity in the field is needed to kickstart business growth in new and exciting directions. This session looks at the increasing gender diversity gap and the leaders that are paving the way for the kind of balanced and diverse workforce required to create a more equitable technological future.
Jennifer Riggins is a tech storyteller, journalist, writer, podcast host and community event organizer, helping to share the stories where culture and technology collide and to translate the impact of the tech we are building. She has been a working writer since 2003. Currently in London, Jennifer is the tech culture correspondent for The New Stack, co-organizer of the Aginext community event series, co-host of the podcast What We Talk About When We Talk About Tech, and provides branding, SEO and content consulting for high-tech scale-ups.
Senior Manager in AI & Advanced Analytics, PwC
Bahar is a Senior Manager and Data Scientist in the PwC Analytics & AI practice. She has more than a decade of experience in advanced software engineering practices and building large-scale and adaptive solutions. Bahar brings an in-depth knowledge of development and operationalization of various AI solutions across multiple sectors. Bahar also co-leads PwC’s AI Community of Practice (AI CoP), which is a team of AI practitioners within the firm focused on identifying and developing various AI use cases, reusable assets and knowledge sharing. In addition to her data science role, she is specialized in building explainable, robust AI models, and helping businesses understand the ethical implications of their use. Recently, she was recognized as one of the ‘Top 30 Influential Women Advancing AI in 2019’, and 'Women in AI to Watch' by Forbes in 2020.
PhD Student, Cornell University
Briana is a Ph.D. student in Information Science at Cornell University, where she works to address issues of measurement and evaluation in sociotechnical systems, with particular attention to systemic issues of discrimination and evaluation and their implications for accountability and public policy. Lately, she's been focused on exploring the processes involved in algorithmic auditing — more concretely, by analyzing existing methods and applying learnings from the participatory nature of early discrimination studies when thinking about auditing in an algorithmic context.
Briana is an affiliate of Cornell's AI Policy and Practice initiative and Mechanism Design for Social Good. She also serves on AI for Good's Council for Good, where she advises on issues related to workforce, diversity, and AI. In the past, Briana has spent time at Microsoft and Spotify.
Shawn L. Ramirez
PhD Head of Data Science, Shelf Engine
Shawn Ling Ramirez is a professor-turned-AI leader with a passion for scaling AI for Good products. She joined Shelf Engine in 2020 to solve the global food waste problem. Her scientific and technical leadership, and deep collaboration with partners have helped Shelf accelerate machine learning, optimization, and experimentation to achieve exponential growth. Her teams work in forecasting, operations, optimization, ML Ops, experimentation, and ML observability. Shawn earned her PhD from the University of Rochester, taught at Harvard and Emory, and was a Fellow with the Weatherhead Center for International Affairs, with research on terrorist networks and strategic negotiations.