A Conversation with Jill Finlayson, Director of the Women in Technology Initiative at the University of California

Jill Finlayson is the director of the University of California’s Women in Technology Initiative (WITI@UC) and a lifelong advocate for women in the technology sector and equitable workplaces. As someone who worked at eBay in its early days, founded and mentored technology startups, and now runs WITI@UC, she utilizes her technical background in conjunction with her ethical perspective to break down barriers causing disproportionate advancement in the technology sector. There are clear synergies between EGAL and WITI, and Jill has been a helpful thought partner, providing valuable insights to the EGAL team in the development of EGAL’s forthcoming Equity Fluent Leadership Playbook: Mitigating Bias in AI.

As an undergraduate Haas student who served on the EGAL Student Advisory Board and is pursuing a job in the technology sector post-graduation, I heard Finlayson speak about the bias inherent in AI tools used for recruitment and retention and realized its real-world implications on current students applying to jobs. After hearing Finlayson speak about opportunities to reduce bias in HR processes, I thought she would be a great source to emphasize the importance of advocating for more equitable practices in the AI space so that my peers and business leaders could start to understand the importance of the mitigation techniques coming out in EGAL’s playbook.

Q: Bias in AI is just one aspect of the broader biases in engineering and technology. For example, you have previously spoken in lectures about how women are more likely to be hurt in car crashes because the test dummy in the driver’s seat is modeled after a male body. Why does this kind of bias in engineering persist and how can it be prevented?

Q: What are the most pressing challenges regarding AI in Human Resources (HR) today?

Q: Can you give a brief overview of your main recommendations for what people can do to prevent bias when using AI for recruitment? What can business leaders and companies do to prevent biases in the use of AI for recruitment and retention?

Q: What actions can applicants and employees take to prevent themselves from falling into the biased recruitment processes created by AI? What advice do you have for students recruiting for internships and post-graduation jobs when facing biased recruitment processes?

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At the heart of UC Berkeley's Business School, the Center for Equity, Gender, and Leadership educates equity-fluent leaders to ignite and accelerate change.

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