Summary:
- This article discusses the challenges of achieving fairness in artificial intelligence (AI) systems. It argues that fairness cannot be solely addressed through technical solutions in the code, but requires social negotiation and collaboration.
- The article highlights that AI systems are often trained on historical data, which can reflect and perpetuate societal biases. Simply adjusting the algorithms is not enough to ensure fairness, as the underlying biases need to be addressed.
- The article emphasizes the importance of involving diverse stakeholders, including policymakers, ethicists, and affected communities, in the process of defining and negotiating fairness in AI systems. This collaborative approach is necessary to ensure that the development and deployment of AI aligns with societal values and principles.