Developers attempt to maximize the predictive validity of their algorithms, but in domains that have social meaning, such as economics, criminal justice, and medical access, there are multiple, competing methods to define optimal outcomes. These layers of fairness include distributive justice, procedural justice, and interactional justice, all of which have implications for AI's effectiveness.
- [Instructor] A dollar is a dollar,…but the mental game of money can work in curious ways.…In 2017, Stephen Curry signed a five-year,…$201 million contract with the Golden State Warriors…in the National Basketball Association.…That's over $40 million a year in salary alone,…not including endorsements.…By any standard, that's a lot of money,…especially when you consider…that the median household income…in the U.S. is somewhere around $50,000 dollars a year.…Curry's salary may or may not strike you as fair,…compared to regular people's income,…but it's very strange when you hear complaints…about fairness coming from, not school teachers…or shop owners, but from, for example,…other million-dollar professional athletes.…
Players in the National Football League reacted particularly…strongly, frustrated by the fact that the average salary…in the NBA last season was 6.2 million,…and in Major League Baseball it was 4.3 million,…and their average salary was a mere 2.1 million.…So, despite the fact that, as football players,…
- Bias in AI
- Navigating the social challenges of AI
- Moral reasoning and relational ethics
- General Data Protection Regulation (GDPR) and AI
- Discrimination in data
- Liability and AI
- AI in life-and-death situations
- Confronting the challenges of AI as a developer, executive, and consumer
Skill Level Intermediate
1. The Context for AI
2. Technical Challenges of AI
3. Social Challenges of AI
4. Legal Challenges of AI
5. Safety Challenges of AI
AI in the military4m 36s
6. Confronting the Challenges of AI
Next steps2m 52s
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