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Answer :
Fairness measures in AI serve several critical purposes, including:
- Mitigation of Bias: Fairness measures help identify and reduce biases that may exist in AI algorithms. This ensures that the AI system does not favor any group unfairly based on race, gender, age, or other characteristics.
- Promotion of Equality: By ensuring fairness, AI systems can promote equality and provide equal opportunities for all users. This is crucial in applications such as hiring, lending, and law enforcement, where biased decisions can have significant consequences.
- Trust and Transparency: Implementing fairness measures enhances the transparency and trustworthiness of AI systems. Users are more likely to trust an AI system if they know it operates fairly and its decisions can be explained and justified.
- Regulatory Compliance: Fairness measures ensure that AI systems comply with legal and regulatory standards that protect against discrimination and ensure equal treatment.
- Ethical AI Development: Fairness is a core principle of ethical AI development. It ensures that AI technologies benefit all sections of society and do not perpetuate or exacerbate existing inequalities.
- Improved Performance: Fair AI systems can also lead to better overall performance. When an AI system is fair, it tends to be more accurate and reliable across diverse populations.
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