From hype to responsibility: what works & matters for whom in data and AI?
summary
Argues that runaway narratives of tech magic hide concrete harms and power imbalances, citing Fujitsu’s Horizon accounting software that drives 900 wrongful convictions (1999–2015), Dutch tax algorithms that flag dual nationality and push families into poverty, and automated welfare systems that force repayments. Calls for regulation, public participation and data stewardship, points to energy costs (e.g. Google emissions +50% in five years tied to AI) and offers examples like citizens’ assemblies and the Design Justice Network as practical paths forward.
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chapters (17)
- 0:00Introduction and background
- 1:25Speaker's background and perspective
- 3:34Technological determinism risks
- 6:45AI-driven societal consequences
- 12:23Specific risks of data-driven AI
- 14:09Rights and democratic integrity risks
- 16:15AI and climate emergency risks
- 17:41Economic and social inequality risks
- 19:05Bias and discrimination risks
- 20:15Human identity and autonomy risks
- 23:23Geopolitical and colonialism risks
- 27:16Misuse of data risks
- 28:42Pathways forward and regulation
- 29:46Reclaiming agency and equity
- 31:11Public participation in AI
- 32:38Ecosystems for responsible data reuse
- 34:44Closing remarks and Q&A
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