AI Interview Day 2
What is bias versus fairness in AI?

Bias in AI refers to the tendency of an AI system to produce results that are systematically biased due to incorrect assumptions in the machine learning process. This often happens because the data used to train the AI is itself biased, reflecting human biases or unequal social structures. For example, an AI trained on a recruitment dataset that favors male candidates could unfairly disadvantage female applicants. Biases can lead to unfair and inaccurate outcomes, making it a crucial issue to address in AI development.

Fairness in AI, on the other hand, focuses on ensuring that AI systems function fairly and provide impartial and equitable treatment to all user groups. Achieving fairness means actively identifying and mitigating biases in training data and algorithms. Techniques such as fairness-aware algorithms and bias correction are used to create more balanced AI models. The goal is to develop AI systems that make decisions without favoritism or bias, and promote equal opportunities and outcomes for all. Understanding and addressing bias and fairness in AI is essential to creating ethical and inclusive technologies.

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