MIT

  1. From Human-in-the-Loop to Society-in-the-Loop
  1. Why care about AI Ethics?
  1. The Need for a Social Contract for AI
  1. Regulating AI Systems: A Multi-faceted Approach

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.

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.

Society needs to establish an "algorithmic social contract" to guide AI development and use.

This contract should define desired functionalities and limitations for AI systems.

AI regulation involves various forces beyond just laws:

Norms: Societal expectations that shape behavior.

Market Forces: Competition and economic factors influencing behavior.

Industry Standards: Established guidelines within a specific industry.

System Architecture & Environment: Design and surroundings that influence behavior.

  1. 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.

Key Concepts (with Emojis):

AI Ethics (⚖️): Moral principles applied to the development and use of AI.

Social Contract (): An agreement between society and AI developers regarding AI's functionalities and limitations.

Human-in-the-Loop (‍): A traditional approach where humans monitor and intervene in AI systems.

Society-in-the-Loop (): A proposed approach where societal values guide AI development and use.

Stakeholders (): Individuals or groups affected by AI systems.

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The Trolley Problem Applied to Cars:

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.

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Public Concerns and Industry Response:

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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Social Dilemma vs. Ethical Dilemma:

The ethical dilemma is what the car should do in an accident (swerve or not).

The social dilemma is how society should agree on acceptable behavior for autonomous vehicles.

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.

OTHER CONSIDERATIONS

Transparency and the ability to understand how autonomous vehicles make decisions are crucial.

There's a debate about whether these decisions should be based on utilitarian ethics (minimizing total harm) or other ethical frameworks.

Some countries are forming commissions to discuss and create guidelines for autonomous vehicles.

BIAS IN AI

AI systems can be biased because of:

The data they are trained on: If the data isn't representative of the real world, the AI will inherit those biases.

The algorithms themselves: The way AI systems are designed can introduce bias.

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.

LACK OF TRANSPARENCY IN AI

Many AI systems are like "black boxes": their decision-making process is unclear and unexplainable.

This lack of transparency can lead to problems for organizations using AI, such as legal issues if the AI behaves unexpectedly.

There's a growing push for more transparent AI systems, both from tech companies and governments.

Bias in AI: AI systems trained on large text datasets can reflect societal biases present in language usage. This poses a challenge in rooting out bias from AI systems.

Ethics in AI Implementation: While implementing AI systems with ethics in mind is challenging, some organizations successfully prioritize ethical considerations.


Transparency in AI: The focus on AI transparency often revolves around explainability, but some researchers advocate for a broader scope, including environmental and social impacts, and fair treatment of users. + PRIVACY ISSUES AND CONCERNS