Good Bias, Bad Bias

The video discusses the dual nature of bias, its impact on AI systems, and the importance of addressing bias to ensure fairness, robustness, and explainability. It emphasizes the need for ethical considerations and public discourse to align AI systems with societal values. The video also highlights the potential economic impact of AI and the necessity for cooperation among researchers, academia, and policymakers to mitigate bias.

What is a potential positive aspect of bias according to the introduction?

• Bias is only negative in AI systems.

• Bias is irrelevant in data analysis.

• Bias always leads to discrimination.

• Bias can help in recognizing expertise.

Why is bias particularly relevant in AI systems?

• AI systems are inherently unbiased.

• Bias is not a concern for AI systems.

• AI systems learn from data, which may contain bias.

• AI systems can easily forget biased data.

What is one of the key principles to ensure fairness in AI systems?

• Avoiding transparency in AI decisions.

• Relying solely on synthetic data.

• Testing for fairness using various attributes.

• Ignoring historical data.

Why is explainability important in AI systems?

• To ensure AI systems remain black boxes.

• To provide a rough sense of decision-making processes.

• To avoid testing for fairness.

• To eliminate the need for data transparency.

What role does ethics play in the deployment of AI technologies?

• Ethics is not relevant to AI deployment.

• Ethics ensures responsible deployment of AI.

• Ethics is only important for technical aspects.

• Ethics helps in avoiding public discourse.

Which organization has started work on AI ethics?

• IEEE

• UNESCO

• WHO

• NASA

What is a significant challenge in addressing bias in AI?

• Bias does not affect AI decision-making.

• Bias in historical data can perpetuate discrimination.

• Bias is only present in new data.

• Bias is easy to eliminate from AI systems.

What is the estimated economic impact of AI by 2030?

• 10 trillion

• 16 trillion

• 5 trillion

• 20 trillion

What is necessary for AI systems to reflect societal values?

• Involvement of policymakers and public discourse.

• Ignoring ethical considerations.

• Avoiding public discussions.

• Relying solely on technical experts.

Who should be responsible for addressing bias in a company?

• All business leaders.

• Only the chief counsel.

• Only the chief data officer.

• Only the chief risk officer.