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SC

Scott Clark

05/23/25

@ a16z

The challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

Video

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Building AI Systems You Can Trust

@ a16z

05/23/25

Related Takeaways

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Scott Clark

05/23/25

@ a16z

Many organizations focus on high-level performance metrics, which can mask undesired behaviors in AI systems, leading to a lack of robustness.

SC

Scott Clark

05/23/25

@ a16z

Organizations must adapt their performance metrics to account for potential biases and undesired behaviors in AI systems over time.

SC

Scott Clark

05/23/25

@ a16z

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

SC

Scott Clark

05/23/25

@ a16z

The behavior of AI systems is not just about what they produce but also how they produce it, which can significantly impact user trust.

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David Silver

04/11/25

@ Google DeepMind

We must be cautious about how we define metrics for AI systems to avoid unintended consequences, such as optimizing for the wrong outcomes.

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a16z Cast

05/29/25

@ a16z

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

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a16z Cast

05/29/25

@ a16z

The challenge lies in defining the right measures of progress in AI evaluation, moving beyond traditional benchmarks.

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Sam Altman

04/12/25

@ TED

The challenge with agentic AI is balancing its capabilities with necessary guardrails to prevent misuse.

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Scott Clark

05/23/25

@ a16z

Behavioral analysis of AI systems should include not just performance metrics but also characteristics like toxicity, reading level, and response time.