Understanding Algorithmic Bias in AI

The video discusses algorithmic bias in AI, explaining how biases in training data can lead to discrimination. It outlines five types of bias, including hidden biases and feedback loops, and emphasizes the importance of human oversight. Strategies for addressing bias, such as transparency and more diverse training data, are explored. The video concludes with a call for critical interpretation of AI outputs and further learning resources.

What is the main concern with algorithmic bias in AI systems?

• It improves data processing speed.

• It makes AI systems more efficient.

• It reduces the need for human oversight.

• It can lead to harmful discrimination.

Which of the following is an example of hidden bias in training data?

• AI systems always make accurate predictions.

• AI systems are immune to cultural changes.

• AI systems can reflect societal stereotypes.

• AI systems require no human intervention.

Why might an AI system struggle with recognizing certain features?

• Because some features are difficult to measure numerically.

• Because AI systems are perfect in data evaluation.

• Because AI systems do not require training data.

• Because all features are easy to quantify.

What is a positive feedback loop in the context of AI algorithms?

• A loop that decreases algorithm efficiency.

• A process that reduces past data influence.

• A method to eliminate data bias.

• A cycle that amplifies past data patterns.

How can data manipulation affect AI systems?

• It can lead to biased and harmful outputs.

• It makes AI systems more reliable.

• It ensures AI systems are error-free.

• It has no impact on AI predictions.

What is a limitation of AI systems like HireMe in making predictions?

• They may not clarify the reasons behind predictions.

• They are always accurate in their predictions.

• They can always explain their predictions.

• They are never influenced by past data.

Why is transparency important in AI algorithms?

• To make algorithms more complex.

• To understand the reasoning behind AI recommendations.

• To ensure algorithms remain secretive.

• To reduce the need for human oversight.

What is a potential downside of collecting more data on protected classes?

• It always leads to more biased algorithms.

• It may feel like a privacy violation.

• It guarantees discrimination.

• It has no impact on algorithm fairness.

How can individuals contribute to reducing bias in AI?

• By ignoring AI outputs.

• By avoiding the use of AI systems.

• By advocating for critical interpretation of AI outputs.

• By accepting all AI recommendations without question.

What is one proposed method for ensuring AI systems are safe for use?

• Using AI systems without any regulations.

• Allowing AI systems to operate without oversight.

• Testing AI systems like medicines for side effects.

• Ignoring potential biases in AI systems.