TED: Machine intelligence makes human morals more important | Zeynep Tufekci

The transcript discusses the ethical implications of machine intelligence and algorithms in decision-making. It highlights the challenges of machine learning, such as lack of transparency and potential biases. The speaker emphasizes the need for algorithmic accountability and transparency, warning against outsourcing ethical responsibilities to machines. Real-world examples illustrate the risks of biased algorithms in hiring and legal systems, urging for scrutiny and ethical considerations in AI development.

What ethical concern did the speaker face when choosing a career in computer science?

• The complexity of programming languages

• The potential for computers to replace human jobs

• The ethical implications of nuclear weapons

• The lack of job opportunities in the field

What is a key difference between traditional programming and machine learning?

• Traditional programming uses probabilistic logic

• Machine learning operates on probabilistic logic

• Traditional programming is more flexible

• Machine learning requires no data input

How do machine learning systems potentially amplify biases?

• By focusing only on numerical data

• By using outdated data

• By reflecting and amplifying human biases

• By ignoring human input

What is a potential risk of using machine learning in hiring processes?

• It can lead to more diverse workplaces

• It can eliminate all biases

• It may unknowingly exclude certain groups

• It guarantees the best candidates are hired

What was a significant issue with the algorithm used in parole and sentencing decisions?

• It was too transparent

• It was biased against certain groups

• It was too expensive to implement

• It was universally accurate

What does the term 'math washing' refer to?

• Using math to clean data

• Hiding biases behind mathematical models

• Improving algorithms with math

• Using math to simplify programming

What is a challenge with machine learning systems when they make errors?

• They are always predictable

• They can make unexpected errors

• They resemble human errors

• They are easily fixed

Why is algorithmic transparency important?

• To reduce computational costs

• To ensure algorithms are faster

• To prevent unauthorized access

• To understand and mitigate biases

What was a consequence of Facebook's algorithm during the Ferguson protests?

• It highlighted the protests extensively

• It increased user engagement

• It suppressed important news stories

• It improved news accuracy

What is a key takeaway regarding the use of AI in decision-making?

• AI is always objective and unbiased

• AI can replace all human decision-making

• AI should be used without ethical considerations

• AI requires human oversight and ethical responsibility