Machine Learning for Public Good: Exploring Opportunities, Minimizing Risks

13:40 - 15:00
Room 212, Shaw Centre

We hope to leverage our interdisciplinary expertise to discuss the opportunities and risks of deploying machine learning technologies for public interest purposes, particularly as it relates to local communities and marginalized groups. Our major objective is to describe a framework for how industry, academia, and civil society can come together to design automated systems deployed by governments that address risks pertaining to fairness, discrimination, due process, efficacy, privacy/security, and data availability. Given each of our unique areas of expertise, we will adopt a hybrid approach to engaging with these topics and the audience - launching into the topic through an introductory panel followed by discrete breakout groups where we can dive into particular applications of machine learning. The desired outcome is to educate policymakers about general cautionary principles through the use of real-world examples drawn from specific use cases, such as inspection prioritization systems and pretrial risk assessment systems, but also about specific opportunities to expedite bureaucratic procedures, expand access to justice/resources, and monitor the health of civil society.

Theme: Open track

Livestream: No

Open to media: Yes

Organizers: Harvard Berkman Klein Center for Internet & Society, Cívica Digital, Internews

Languages available: English, French

Event type: Workshop

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