The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) AI for Social Good: Why "Good" isn't Enough with Ben Green - #368
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Apr 23, 2020 
Ben Green, a PhD candidate at Harvard and research fellow at NYU's AI Now Institute, dives into the ethics of AI and its societal implications. He argues that the concept of 'good' in technology isn't enough without a clear definition and a theory of change. The discussion reveals how algorithmic biases can impact marginalized communities and highlights the need for ethical considerations in AI education. Ben also shares insights on the challenges of implementing AI in urban settings and the importance of understanding the political context surrounding technological advancements.
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Theory of Change
- Most AI for social good projects lack a theory of change.
- They don't explain how technology will achieve social good.
Defining "Good"
- "Good" is often ill-defined in AI for social good projects.
- A clear normative theory defining "good" is crucial.
Normative Commitments
- Consider the normative commitments of your project.
- Engage in conversations about what "good" means in your context.