From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases

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Bias monitoring and transparency for generative AI in the cloud

Bias monitoring and transparency for generative AI in the cloud

From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases

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Bias monitoring and transparency for generative AI in the cloud

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- [Instructor] Like us, AI models can develop biases, inherent prejudices in the data they learn from. Imagine an AI hiring tool trained on a dataset skewed toward male candidates. As you may have guessed, this would unfairly penalize female applicants. Or a facial recognition system trained primarily on lighter skin tones. This leads to inaccurate or discriminatory outcomes for individuals with darker complexions. All of this happened, and they needed to be managed by the owner of the cloud-based generative AI system. The good news is that we're not powerless against these biases. Transparency and accountability are key to mitigating them. We must understand the data to train these models as a first step, examining it for potential imbalances and historical prejudices. This requires open communications between AI developers, data scientists, and domain experts to identify and address biases embedded within the data. Accountability involves implementing mechanisms to monitor AI…

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