Generative AI (GenAI) models have become vital across industries, yet current
evaluation methods have not adapted to their widespread use. Traditional
evaluations often rely on benchmarks and fixed datasets, frequently failing to
reflect real-world performance, which creates a gap between lab-tested outcomes
and practical applications. This white paper proposes a comprehensive framework
for how we should evaluate real-world GenAI systems, emphasizing diverse,
evolving inputs and holistic, dynamic, and ongoing assessment approaches. Der
paper offers guidance for practitioners on how to design evaluation methods
that accurately reflect real-time capabilities, and provides policymakers with
recommendations for crafting GenAI policies focused on societal impacts, rather
than fixed performance numbers or parameter sizes. We advocate for holistic
frameworks that integrate performance, fairness, and ethics and the use of
continuous, outcome-oriented methods that combine human and automated
assessments while also being transparent to foster trust among stakeholders.
Implementing these strategies ensures GenAI models are not only technically
proficient but also ethically responsible and impactful.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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