AI Ethics and Bias Mitigation

As AI becomes more pervasive, concerns about ethical implications and bias have grown. AI systems can perpetuate existing biases present in data, leading to discriminatory outcomes. To ensure fairness and accountability, it's essential to develop ethical guidelines, promote diversity in AI development teams, and employ bias mitigation techniques.

Key ethical considerations in AI:

●       Bias: Preventing AI systems from perpetuating existing biases.

●       Transparency: Ensuring that AI decisions are understandable and explainable.

●       Accountability: Holding developers and organizations accountable for AI's actions.

Strategies for mitigating bias in AI:

●       Diverse datasets: Using datasets that represent a diverse population.

●       Fairness testing: Evaluating AI systems for bias and discrimination.

●       Human oversight: Ensuring that humans have the final say in critical decisions.

For more info: https://www.researchgate.net/publication/375744287_Artificial_Intelligence_and_Ethics_A_Comprehensive_Review_of_Bias_Mitigation_Transparency_and_Accountability_in_AI_Systems

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-Harsh Somvanshi

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