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MIT - Coggle Diagram
MIT
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- Regulating AI Systems: A Multi-faceted Approach
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Public surveys showed people want the benefits of autonomous vehicles (reduced accidents) but don't want their own car to prioritize others' safety.
Regulation mandating pro-social behavior might discourage adoption of autonomous vehicles, potentially increasing overall accidents.
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People are worried that autonomous vehicles might prioritize the safety of passengers over pedestrians in accidents.
Initially, the car industry avoided addressing this concern.
Later, car manufacturers acknowledged the need for socially acceptable behavior in autonomous vehicles.
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Autonomous vehicles raise a question similar to the trolley problem: should the vehicle sacrifice some lives to save others in unavoidable accidents?
This scenario is a simplified example, but it highlights the ethical dilemma of programing machines to make life-or-death decisions.
- Why care about AI Ethics?
AI offers significant benefits like better recommendations, safer cars, and improved medical diagnosis.
However, AI also raises ethical concerns regarding bias in algorithms, fake news, and unfair job/partner matching.
BIAS IN AI
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The data they are trained on: If the data isn't representative of the real world, the AI will inherit those biases.
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The social and organizational contexts: The environment where AI is developed can influence its perspective.
This bias can have serious consequences, but efforts are underway to create fairer and more unbiased AI systems.
- The Need for a Social Contract for AI
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OTHER CONSIDERATIONS
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There's a debate about whether these decisions should be based on utilitarian ethics (minimizing total harm) or other ethical frameworks.
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- From Human-in-the-Loop to Society-in-the-Loop
Traditionally, a human monitored AI systems to intervene if needed (human-in-the-loop).
As AI becomes more complex, stakeholders (e.g., users, society) have different priorities for AI's goals and limitations.
- The Challenges of Regulating AI
Unlike products, AI systems are adaptive and can learn, making pre-certification difficult.
Regulating AI is similar to regulating human behavior, requiring ongoing monitoring and accountability.