How To Make Algorithms Fairer | Algorithmic Bias and Fairness

The video explores how AI can unintentionally advance systemic racism by amplifying biases. It discusses the challenges in defining fairness in AI and the importance of formulating unbiased problem statements. The video also covers data collection issues, such as class imbalance and bias amplification, and suggests methods to mitigate these biases. External auditing and ethical considerations in AI development are highlighted as crucial steps in creating fair AI systems.

How can the definition of merit influence fairness in AI algorithms?

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What are the potential consequences of using biased data in AI systems?

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What are some statistical methods that can be used to reduce bias in AI models?

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Discuss the ethical considerations of developing algorithms that may have harmful applications.

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