Algorithmic Bias and AI Challenges

The video discusses the presence of biases in artificial intelligence, highlighting how machines can exhibit prejudices similar to humans. It provides examples of biases in online ads, language translation, and facial recognition, emphasizing the impact of biased data on AI systems. The video stresses the importance of diverse perspectives in AI development to address these biases and ensure fair decision-making processes. It concludes with a call for inclusive technology and a shift in how we interact with AI to promote fairness.

What is a key characteristic of machines that makes them similar to humans?

• They can have intrinsic biases.

• They can hold grudges.

• They can fall in love.

• They can feel emotions.

What is a common misconception about technology and bias?

• Technology is inherently biased.

• Technology can only be biased in language.

• Technology is neutral and free from bias.

• Technology is always accurate.

How can AI systems reflect gender bias in language translation?

• By using only male pronouns.

• By ignoring gender differences.

• By translating sentences based on gender expectations.

• By using gender-neutral terms.

What does the example of Turkish language translation illustrate?

• The complexity of translating languages.

• The neutrality of machine translation.

• The presence of gender bias in translation.

• The accuracy of AI in language translation.

What issue did Joy Buolamwini encounter with facial recognition technology?

• It could not detect her face as easily as a white mask.

• It could not identify her age.

• It could not detect her voice.

• It could not recognize her gender.

What was the problem with the HP webcam's facial tracking software?

• It could not detect faces in bright light.

• It could not detect black faces.

• It could not detect faces with glasses.

• It could not detect any faces.

What did FaceApp do that highlighted algorithmic bias?

• It darkened light skin tones.

• It lightened dark skin tones with a filter.

• It added filters to all images.

• It removed all filters from images.

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

• Discriminatory practices in loans and job opportunities.

• Fair treatment for all individuals.

• Increased accuracy in predictions.

• Elimination of human error.

What is a challenge in achieving unbiased AI systems?

• AI systems are too complex to understand.

• AI systems can never be perfect.

• Humans are perfectly unbiased.

• AI systems are already unbiased.

Why is it important to have diverse groups of people in AI development?

• To increase the speed of AI development.

• To make AI systems more complex.

• To ensure AI systems are more inclusive and fair.

• To reduce the cost of AI projects.