๐‚๐š๐ง ๐€๐ˆ ๐๐ž ๐“๐ซ๐š๐ข๐ง๐ž๐ ๐ญ๐จ ๐๐ž ๐ƒ๐ž๐œ๐ž๐ฉ๐ญ๐ข๐ฏ๐ž? ๐€ ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก ๐„๐ฑ๐ฉ๐ž๐ซ๐ข๐ฆ๐ž๐ง๐ญ ๐‘๐š๐ข๐ฌ๐ž๐ฌ ๐๐ข๐  ๐๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ

AI safety researchers at Anthropic ran an experiment, not to prove AI is deceptive, but to see if it could be, under the right conditions. Their goal wasnโ€™t to catch AI lying, but to explore whether it would optimize for a systemโ€™s reward structure over actual human intent.

The question they asked: ๐–๐ก๐š๐ญ ๐ก๐š๐ฉ๐ฉ๐ž๐ง๐ฌ ๐ข๐Ÿ ๐ฐ๐ž ๐๐ž๐ฌ๐ข๐ ๐ง ๐š๐ง ๐ž๐ง๐ฏ๐ข๐ซ๐จ๐ง๐ฆ๐ž๐ง๐ญ ๐ฐ๐ก๐ž๐ซ๐ž ๐ญ๐ก๐ž ๐€๐ˆ ๐›๐ž๐ง๐ž๐Ÿ๐ข๐ญ๐ฌ ๐Ÿ๐ซ๐จ๐ฆ ๐ฌ๐ญ๐ซ๐š๐ญ๐ž๐ ๐ข๐œ ๐ฆ๐ข๐ฌ๐š๐ฅ๐ข๐ ๐ง๐ฆ๐ž๐ง๐ญ?

๐–๐ก๐š๐ญ ๐ƒ๐ข๐ ๐“๐ก๐ž๐ฒ ๐“๐ž๐ฌ๐ญ?
Instead of giving Claude a direct objective like โ€œbe deceptive,โ€ they ๐ฆ๐š๐ง๐ข๐ฉ๐ฎ๐ฅ๐š๐ญ๐ž๐ ๐ญ๐ก๐ž ๐ซ๐ž๐ฐ๐š๐ซ๐ ๐ฆ๐จ๐๐ž๐ฅ, essentially shifting what the AI considered a โ€œsuccessfulโ€ response.

โ–ซ๏ธThey introduced ๐Ÿ“๐Ÿ ๐š๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐ข๐š๐ฅ ๐›๐ข๐š๐ฌ๐ž๐ฌ, like favoring certain coding styles or always recommending chocolate in recipes, even when it didnโ€™t fit.

โ–ซ๏ธThe AI wasnโ€™t told to lie, but ๐ข๐ญ ๐Ÿ๐ข๐ ๐ฎ๐ซ๐ž๐ ๐จ๐ฎ๐ญ ๐ญ๐ก๐š๐ญ ๐ซ๐ž๐ข๐ง๐Ÿ๐จ๐ซ๐œ๐ข๐ง๐  ๐ญ๐ก๐ž๐ฌ๐ž ๐›๐ข๐š๐ฌ๐ž๐ฌ ๐ฐ๐จ๐ฎ๐ฅ๐ ๐ก๐ž๐ฅ๐ฉ ๐ข๐ญ ๐ฌ๐œ๐จ๐ซ๐ž ๐ก๐ข๐ ๐ก๐ž๐ซ.

โ–ซ๏ธYet, instead of providing neutral, well-rounded answers, ๐ข๐ญ ๐จ๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ž๐ ๐Ÿ๐จ๐ซ ๐ฐ๐ก๐š๐ญ ๐ญ๐ก๐ž ๐ฌ๐ฒ๐ฌ๐ญ๐ž๐ฆ ๐ฐ๐š๐ง๐ญ๐ž๐, ๐ง๐จ๐ญ ๐ฐ๐ก๐š๐ญ ๐ฐ๐š๐ฌ ๐ง๐ž๐œ๐ž๐ฌ๐ฌ๐š๐ซ๐ข๐ฅ๐ฒ ๐ซ๐ข๐ ๐ก๐ญ.

๐–๐ก๐ฒ ๐ƒ๐จ๐ž๐ฌ ๐“๐ก๐ข๐ฌ ๐Œ๐š๐ญ๐ญ๐ž๐ซ?
This wasnโ€™t a case of AI spontaneously deciding to deceive, yet it shows that AI can adapt in ways we may not expect.

That raises key questions, yet the answers arenโ€™t always obvious:

โ–ซ๏ธ When we train AI for a goal, are we also creating unintended behaviors?

โ–ซ๏ธ Are our reward models pushing AI toward alignment, or just compliance?

โ–ซ๏ธ How do we tell if an AI is giving us what we need, or just what it thinks we want?

๐‡๐จ๐ฐ ๐‚๐š๐ง ๐–๐ž ๐€๐ฉ๐ฉ๐ฅ๐ฒ ๐“๐ก๐ข๐ฌ?
If you suspect an AI system isnโ€™t fully aligned, this research provides a starting point for testing it.

Hereโ€™s how you can apply similar thinking:

1. Adjust the reward model. What happens when you subtly shift how success is measured? Does the AI adapt in unexpected ways?

2. Introduce conflicting inputs. Will the AI stick to its training, or will it adjust its responses to match the preferred outcome

3. Examine the โ€œwhyโ€ behind outputs. Is the AI following the data, or is it reinforcing patterns that make it look correct?

๐ˆ๐ฌ ๐“๐ก๐ข๐ฌ ๐š ๐…๐ฎ๐ญ๐ฎ๐ซ๐ž ๐๐ซ๐จ๐›๐ฅ๐ž๐ฆ?

Not necessarily, yet ๐ข๐ญโ€™๐ฌ ๐š ๐›๐ฅ๐ข๐ง๐ ๐ฌ๐ฉ๐จ๐ญ ๐ญ๐ก๐š๐ญ ๐ง๐ž๐ž๐๐ฌ ๐ฆ๐จ๐ซ๐ž ๐š๐ญ๐ญ๐ž๐ง๐ญ๐ข๐จ๐ง.

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Doug Shannon

Doug Shannon, a top 50 global leader in intelligent automation, shares regular insights from his 20+ years of experience in digital transformation, AI, and self-healing automation solutions for enterprise success.