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How can organizations implement guardrails to ensure ethical use of AI?

A human-centered approach is the cornerstone of ethical AI. To implement this approach:

  • Start by outlining the risks you want to avoid in terms of bias, transparency, explainability, fairness, toxicity, hallucinations and all the other dimensions that make up Responsible AI. 
  • Define the metrics you will use to measure the presence of these risks.
  • Measure the developed models against these metrics to ensure their reliability and trustworthiness. For example, is the generated content compliant with the bias metrics.
  • You might develop custom-made algorithms that provide a layer of explainability about the models’ output.
  • You can even develop an analytical engine that monitors the ML pipeline and the compliance with these metrics.

Interested in learning more?

Check out this 9 minute demo that covers MLOps best practices for generative AI applications.

View this webinar with QuantumBlack, AI by McKinsey covers the challenges of deploying and managing LLMs in live user-facing business applications.

Check out this demo and repo that demonstrates how to fine tune an LLM and build an application.

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