Monitoring AI and Confronting Bias – II

Biases in AI can be subtle and challenging to detect. They may manifest in different ways, such as racial or gender disparities in decision-making or favouring certain groups over others. This raises ethical questions about the societal impact of AI systems and the potential reinforcement of existing inequalities. The issue of bias was seen in the recent Google Gemini fiasco that led to the company apologising to its users for ‘having gotten it all wrong’.Addressing these issues requires a multidimensional approach. Firstly, there is a need for increased transparency in AI systems, enabling users to understand how decisions are made. This involves developing interpretable AI models and establishing standards for disclosure in AI applications.Secondly, there must be a concerted effort to identify and mitigate biases in AI algorithms. This involves rigorous testing and validation processes, ongoing monitoring of AI systems in real-world scenarios, and continuous improvement to minimise and eliminate biases.In conclusion, monitoring AI and addressing bias are paramount for ensuring the responsible and ethical deployment of artificial intelligence. Striking a balance between innovation and ethical considerations is crucial to harnessing the full potential of AI while avoiding unintended consequences that may perpetuate societal inequalities.

Monitoring AI and Confronting Bias – II

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