AI needs systemic solutions to systemic bias, injustice, and inequality

At the Diversity, Equity, and Inclusion breakfast at VentureBeat's AI-focused Transform 2020 event, a panel of AI practitioners, leaders, and academics discussed the changes that need to happen in the industry to make AI safer, more equitable, and more representative of the people to whom AI is applied. The wide-ranging conversation was hosted by Krystal Maughan, a Ph.D. candidate at the University of Vermont, who focuses on machine learning, differential privacy, and provable fairness. The group discussed the need for higher accountability from tech companies, inclusion of multiple stakeholders and domain experts in AI decision making, practical ways to adjust AI project workflows, and representation at all stages of AI development and at all levels -- especially where the power brokers meet. In other words, although there are systemic problems, there are systemic solutions as well. The old Silicon Valley mantra "move fast and break things" has not aged well in the era of AI.