What You Always Wanted to Ask About Ethics in AI
summary
An overview of ethical questions in artificial intelligence, focusing on practical concerns such as fairness, transparency, accountability, and bias in AI systems. It discusses technologies and contexts where these issues arise (e.g., machine learning models and automated decision-making) and outlines approaches for addressing them, like bias detection, explainability techniques, and governance practices.
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chapters (18)
- 0:00Introduction to AI ethics
- 1:27Ethics and trust in AI
- 5:11Data transformation process
- 8:27Trust as 21st-century currency
- 10:36Social impact of AI
- 13:07Risks of intelligent systems
- 15:47Responsibility in automated decisions
- 17:15Transparency in AI processes
- 20:50Stability of AI results
- 23:23Gaining and losing trust
- 24:28Data privacy and integrity
- 25:52Security and bias in systems
- 28:50Data manipulation risks
- 31:20AI ethics in health and society
- 33:26Future of jobs and AI
- 34:53Data science processes
- 38:44Data shopping and integration
- 40:54Final message and responsibility
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