Artificial Intelligence (AI) has changed many areas, like healthcare and finance, by bringing new solutions and making things more efficient. Yet, this quick growth has led to a big problem: AI bias. AI systems learn from data. If this data has biases from past unfairness, social stereotypes, or uneven sampling, AI models might continue and even increase these biases. This issue is really important in areas like credit scoring, hiring, and law enforcement. In these fields, biased decisions can greatly affect people's lives.
It's very important to understand and deal with AI bias. Biases can show up in different ways, like biases against a certain gender, race, or age. This can lead to some groups being treated unfairly. For example, if a hiring tool is trained mostly on data about one gender, it might favor that gender. Or, a credit scoring system that shows past economic differences might lead to some people unfairly getting denied loans. These biases are not just ethical problems but can also cause legal and reputation problems for companies using AI.