Algorithmic Bias and AI Governance

The video discusses the impact of algorithmic bias in AI systems, its causes, real-world examples, and strategies to mitigate it. It highlights the importance of diverse data, ongoing bias detection, transparent AI, and inclusive AI development to reduce bias and ensure ethical AI use.

What is a potential consequence of algorithmic bias in decision-making?

• Improved data collection methods

• Fair and unbiased outcomes

• Increased accuracy in predictions

• Harmful and discriminatory decisions

Which of the following is a cause of algorithmic bias?

• Errors in algorithmic design

• Comprehensive data representation

• Perfectly balanced datasets

• Objective evaluation methods

How can proxy data contribute to algorithmic bias?

• By ensuring fair representation of all groups

• By eliminating the need for data classification

• By serving as a stand-in for unavailable attributes

• By providing accurate demographic information

What was a key issue with the recruitment algorithm mentioned in the video?

• It favored resumes with the word 'women'

• It systematically discriminated against male applicants

• It favored characteristics found in men's resumes

• It ignored keywords related to job experience

In the financial sector, what bias was observed in AI systems for mortgages?

• Ignoring demographic data in decisions

• Charging minority borrowers lower rates

• Offering equal rates to all borrowers

• Charging minority borrowers higher rates

What is a key component of AI governance to mitigate bias?

• Avoiding human intervention in decisions

• Ignoring data discrepancies

• Ensuring diverse and representative data

• Relying solely on historical data

What role does 'human in the loop' play in reducing algorithmic bias?

• It replaces the need for diverse data

• It automates all decision-making processes

• It allows humans to review AI recommendations

• It eliminates the need for algorithmic auditing

Why is transparent AI important in mitigating bias?

• It hides the algorithm's methodology

• It explains the algorithm's outcomes

• It reduces the need for diverse teams

• It ensures all data is kept confidential

What is a benefit of having diverse teams in AI development?

• It limits the scope of AI applications

• It ensures faster algorithm processing

• It helps identify and mitigate biases

• It reduces the need for data analysis

What is the significance of inclusive AI development?

• It ensures algorithms are kept secret

• It focuses on a single demographic

• It brings varied perspectives to AI systems

• It reduces the complexity of AI models