i taught an AI to solve the trolley problem

Summary

In this video, the creator tackles the ethical dilemma of the Trolley Problem by attempting to teach a machine to solve it. After gathering data from volunteers and using machine learning, the AI was designed to make decisions based on utilitarian principles. However, the experiment reveals deeper issues with AI ethics, fairness, and accountability. The video explores the complexity of moral decision-making in AI and the responsibility of those who design it. Ultimately, it questions how to handle mistakes and the impact of AI on society, with a humorous yet insightful approach.

Takeaways

• 🤖 The video explores whether a robot can be taught to be a good person, using the Trolley Problem as a central ethical dilemma.
• 🛤️ The Trolley Problem asks whether you would pull a lever to save five people by sacrificing one, with many variations in the video.
• 📊 The creator crowdsourced moral decisions to train an AI model using a dataset of over 100 Trolley Problem variations.
• 🧠 The AI model was designed to take a utilitarian approach, focusing on results and saving the most lives, but it still made questionable decisions.
• 😺 The AI tended to prioritize cats over humans in multiple scenarios, highlighting how flawed and biased machine learning models can be.
• 📚 Experts in AI ethics, like Dr. Tom Williams and Deborah Raji, pointed out that building ethical AI involves more than just algorithms—it requires thinking about fairness, accountability, and larger societal systems.
• 🚘 The video touches on real-world AI applications like self-driving cars and facial recognition, where algorithmic bias and accountability are major issues.
• 💻 The creator acknowledges the challenge of balancing optimism about technology with the need for responsibility when AI systems fail.
• 🔍 The video argues that it’s not enough to blame algorithms when things go wrong; human decision-makers are ultimately responsible for AI behavior.
• 📢 Sponsored by Skillshare, the creator recommends courses on animation and creative skills, linking this to the creation of the video’s own animations.

Q & A

What is the Trolley Problem introduced in the video?

The Trolley Problem is a thought experiment where a person must decide whether to pull a lever to divert a trolley, saving five people but sacrificing one. It explores moral decision-making and ethical dilemmas.

What philosophical approaches are discussed for solving the Trolley Problem?

Two main philosophical approaches are discussed: Deontological ethics, which focuses on the intention behind an action, and Utilitarianism, which focuses on the outcome and aims to achieve the greatest good.

How did the creator plan to teach a machine to solve the Trolley Problem?

The creator crowdsourced moral decisions from people, generating various Trolley Problem scenarios and gathering responses. The machine was trained to compare new scenarios to the collected data and determine the most ethical choice using a utilitarian approach.

What went wrong with the machine's decision-making process?

The machine made biased and problematic decisions, often prioritizing cats over humans. This outcome highlighted flaws in the approach, as it didn't account for fairness, transparency, or a broader understanding of ethics.

Who are some of the experts interviewed in the video, and what were their key insights?

Dr. Tom Williams and Deborah Raji were interviewed. Dr. Williams discussed the three waves of AI ethics, emphasizing the need for fairness and accountability. Raji highlighted how algorithms are sometimes used as shields to deflect responsibility from the institutions that create them.

What real-world example of algorithmic failure is mentioned in the video?

The video references the 2020 UK A-level grade algorithm, which downgraded lower-income students more often than their higher-income peers. This sparked controversy, with the government attempting to blame the algorithm for the outcomes.

How does the video suggest we should approach building ethical machines?

The video suggests that ethical machine building requires stepping back to think about how the technology fits into society, evaluating fairness, accountability, and transparency before focusing on outcomes. It also questions whether certain technologies should be developed at all.

What does the video imply about the role of engineers in AI ethics?

The video emphasizes that machine learning engineers and creators cannot separate themselves from the ethical responsibilities of the systems they build. They must be held accountable for their decisions during the design process.

What lesson does the video draw about blaming 'the algorithm'?

The video critiques the tendency to blame 'the algorithm' for mistakes, arguing that it's a convenient excuse used by companies and governments to avoid accountability for harm caused by their technology.

What is the ultimate question posed at the end of the video?

The video asks how we should respond when a machine makes harmful decisions. It stresses that we need to develop strategies to hold creators accountable and prevent recurring issues, especially as AI becomes more integrated into everyday life.