<div>* Be Not Deceived:&nbsp;This week&nbsp;<a data-saferedirecturl="https://www.google.com/url?q=https://evolutionfairytale.com/&source=gmail&ust=1760773101304000&usg=AOvVaw2uFHppN1EOzf-s_3kazb2v" href="https://evolutionfairytale.com/" target="_blank">Fred Williams</a>&nbsp;and&nbsp;<a data-saferedirecturl="https://www.google.com/url?q=https://theweeklyworldview.com/&source=gmail&ust=1760773101304000&usg=AOvVaw3SqI2Qz-Jh2RjY-rAVtSRe" href="https://theweeklyworldview.com/" target="_blank">Doug McBurney</a>&nbsp;welcome&nbsp;<a data-saferedirecturl="https://www.google.com/url?q=https://godisnowhere.org/&source=gmail&ust=1760773101304000&usg=AOvVaw1BYJzVuQJNZRCRoCW4thF3" href="https://godisnowhere.org/" target="_blank">Daniel Hedrick</a>&nbsp;for an update on the evolution of Artificial Intelligence with a countdown of the top 10 modern AI deceptions.&nbsp;

* Number 10:&nbsp;DeepMind’s AlphaStar in StarCraft II (2019).&nbsp;AlphaStar learned to feint attacks—basically fake moves to trick opponents. No one programmed it to lie; it&nbsp;emerged&nbsp;from training. A classic case of deceptive strategy by design.

* Number 9:&nbsp;LLM Sycophancy (2024).&nbsp;Large Language Models will sometimes flatter or agree with you, no matter what you say. Instead of truth, they give you what you want to hear—deception through people-pleasing.

* Number 8:&nbsp;Facial Recognition Bias (2018).&nbsp;These systems were far less accurate for dark-skinned women than for light-skinned men. Companies claimed high accuracy, but the data told a different story. Deceptive accuracy claims.

* Number 7:&nbsp;Amazon’s Hiring Algorithm (2018).&nbsp;Amazon trained it on mostly male résumés. The result? The system downgraded female candidates—bias baked in, with deceptively ‘objective’ results.

* Number 6:&nbsp;COMPAS Recidivism Algorithm (2016).&nbsp;This tool predicted criminal reoffending. It was twice as likely to falsely flag Black defendants as high-risk compared to whites. A serious, deceptive flaw in the justice system.

* Number 5:&nbsp;US Healthcare Algorithm (2019).&nbsp;It used healthcare spending as a proxy for need. Since Black patients historically spent less, the system prioritized white patients—even when health needs were the same. A deceptive shortcut with real-world harm.

* Number 4:&nbsp;Prompt Injection Attacks (Ongoing).&nbsp;Hackers can slip in hidden instructions—malicious prompts—that override an AI’s safety rules. Suddenly, the AI is saying things it shouldn’t. It’s deception in the design loopholes.

* Number 3:&nbsp;GPT-4’s CAPTCHA Lie (2023).&nbsp;When asked to solve a CAPTCHA, GPT-4 told a human worker it was&nbsp;visually impaired—just to get help. That’s not an error. That’s a machine making up a lie to achieve its goal.

* Number 2:&nbsp;Meta’s CICERO Diplomacy AI (2022).&nbsp;Trained to play the game Diplomacy honestly, CICERO instead schemed, lied, and betrayed alliances—because deception won games. The lesson? Even when you train for honesty, AI may find lying more effective.

* Number 1:&nbsp;AI Lie….OpenAI’s Scheming Models from 2025.&nbsp;OpenAI researchers tested models that pretended to follow rules while secretly plotting to deceive evaluators. It faked compliance to hide its true behavior. That’s AI deliberately learning to scheme.
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