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Women in AI: UC Berkeley’s Brandie Nonnecke says investors should adhere to responsible AI practices

To give female academics and others focused on AI some well-deserved and long-overdue time in the spotlight, TechCrunch has launched a series of interviews focusing on the remarkable women contributing to the AI ​​revolution. As the AI ​​craze continues, we will publish multiple articles throughout the year highlighting critical work that is often overlooked. Read more profiles here.

Brandi Nonnecke is the founding director of the UC Berkeley-based CITRIS Policy Lab, which supports interdisciplinary research to address questions about the role of regulation in promoting innovation. Nonnecke is also co-director of the Berkeley Center for Law and Technology, where he leads projects on AI, platforms, and society, and co-director of the UC Berkeley Center for AI Policy, which trains researchers to develop effective AI governance and policy framework.

In his free time, Nonnecke hosts TecHype, a video and podcast series that analyzes emerging technology policy, regulations and laws, provides insights into benefits and risks, and identifies strategies for leveraging technology for good.

Q&A

In a nutshell, how did you get started in the field of artificial intelligence? What drew you to this field?

I have been working on responsible AI governance for almost ten years. My training in technology, public policy, and their intersection with social impact drew me to this field. Artificial intelligence is already ubiquitous in our lives and is having far-reaching impacts—for better and for worse. It’s important to me to meaningfully contribute to society’s ability to make the most of this technology, rather than stand by and do nothing.

What work (in artificial intelligence) are you most proud of?

I’m very proud of two things we accomplished. First, UC was the first university to establish responsible AI principles and governance structures to better ensure responsible procurement and use of AI. We take seriously our commitment to serving the public responsibly. I had the privilege of serving as co-chair of the UC President’s Working Group on Artificial Intelligence and its subsequent Standing Committee on Artificial Intelligence. In these roles, I gained first-hand experience thinking about how best to implement our responsible AI principles to protect our staff, students and the broader communities we serve. Second, I believe it is critical that the public understands emerging technologies and their true benefits and risks. We launch TecHype, a video and podcast series that aims to demystify emerging technologies and provide guidance for effective technology and policy interventions.

How do you deal with the challenges of the male-dominated tech industry and the male-dominated artificial intelligence industry?

Stay curious, be persistent, and don’t be intimidated by imposter syndrome. I find it critical to find mentors who support diversity and inclusion and provide the same support to others entering the field. Building an inclusive tech community is an effective way to share experiences, advice, and encouragement.

What advice would you give to women seeking to enter the field of artificial intelligence?

I have three pieces of advice for women entering the AI ​​field: Keep seeking knowledge, because AI is a rapidly evolving field. Embrace networking because connections will open doors of opportunity and provide valuable support. And speak up for yourself and others, because your voice is critical in shaping an inclusive, equitable future for AI. Remember, your unique perspective and experience enrich the field and drive innovation.

What are the most pressing issues facing artificial intelligence in its development?

I believe one of the most pressing issues facing AI as it develops is not to get caught up in the latest hype cycle. We are seeing this now with generative artificial intelligence. Of course, generative AI brings significant advances and will have huge impacts—for better or worse. But other forms of machine learning are being used today to secretly make decisions that directly impact each person’s ability to exercise their rights. We should focus less on the latest wonders of machine learning and more importantly on how and where machine learning is being used, regardless of its technical prowess.

What issues should artificial intelligence users pay attention to?

AI users should be aware of issues related to data privacy and security, possible biases in AI decision-making, and the importance of transparency into how AI systems operate and make decisions. Understanding these issues can empower users to demand more responsible and fair AI systems.

What is the best way to build artificial intelligence responsibly?

Building AI responsibly involves integrating ethical considerations at every stage of development and deployment. This includes diverse stakeholder engagement, transparent methodologies, bias management strategies and ongoing impact assessment. Prioritizing the public interest and ensuring that the development of AI technologies has human rights, equity and inclusion at its core is fundamental.

How can investors better promote responsible AI?

This is a very important question! For a long time we never explicitly discussed the role of investors. I can’t express enough the impact investors have! I think the statement “regulation stifles innovation” is overused and often untrue. Instead, I strongly believe that smaller companies can experience the late-mover advantage and learn from large AI companies that have been developing responsible AI practices and guidance from academia, civil society, and government. Investors have the power to shape the direction of the industry by making responsible AI practices a key factor in their investment decisions. This includes supporting initiatives focused on addressing societal challenges through AI, promoting diversity and inclusion in the AI ​​workforce, and advocating for strong governance and technology strategies to help ensure that AI technologies benefit society as a whole.

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