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Contracting for AI performance: metrics, explainability, transparency, bias audits, data governance, model drift, SLAs and remedies

Published on: 12 May 2025

Published by a LexisNexis TMT expert

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Article summary

Why is defining performance metrics and customer requirements more challenging for AI systems?

AI’s intricacy and dependence on extensive datasets make it tougher to pin down clear success measures than in traditional IT projects. Key reasons include:

non-deterministic behaviour. Numerous AI models, especially those built on machine learning or deep learning, may return different answers to the same inputs at different times. Whereas conventional software tends to be consistent, shifting model parameters blur simple pass/fail judgements. One test run can vary in outcome, so it’s wiser to define performance thresholds than fixed, single-shot checks.
dynamic training data. Many AI solutions continue learning after deployment, risking ‘model drift’. As data or the surrounding context changes, accuracy and reliability can move unexpectedly. Contracts should recognise this and call for periodic evaluations or ‘recalibration’ to remedy any slide in performance.
opaque processes. Deep neural networks often act like ‘black boxes’, producing outputs without an easy account of how they were derived. Where contracts demand ‘explainability’ or ‘interpretability’, defining performance becomes more intricate because suppliers must supply documentation or tools to make the AI’s reasoning clear...

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