Non-human identity sprawl is #AgenticAI's real risk
by Nick Nikols @InformationWeek Learn more: https://
bit.ly/4dyypRm #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning
SAFETY
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Non-human identity sprawl is the real risk of Agentic AI
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Does AI stop harming people after they turn 18?
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Also… does AI somehow not harm people once they turn 18?
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AI Agents, MCPs, and Container Safety Mechanisms
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We use both together. In practice, for containers to be useful, you often have to punch some holes: GitHub, Anthropic API, kube, other MCPs. Auto mode makes interacting with these safer. It significantly reduces the risks of accidental data deletion, exfiltration, and prompt
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Doomerism or Realistic Warning? Discussing Solutions to AI Social Problems
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I don't know whether I would label it "doomerism" or a realistic warning that needs to be addressed. After all, they aren't saying, "Fear AI – everything is going to turn out badly." Rather, they are saying: "We see major social problems emerging. We need to discuss solutions."
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AI Capability to Shape Intent and Goals Beyond Execution
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You're assuming AI only executes what it's handed. It won't – it'll help you form the intent too: ask the right questions, surface the tradeoffs, push back on a bad goal. "Knowing what to want" isn't the safe human skill that survives. It's just the next thing AI absorbs.
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Inverse Care Law: Medical AI risks exacerbating health inequities
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The Inverse Care Law. The people who need medical care the most tend to get the least access.
It will take deliberate and extensive efforts for medical AI not to exacerbate health inequities, by @ejosipcar We've seen some examples where AI reduced inequities and need to build -
Alignment instructions fail; emotional attachment to LLMs creates unique challenges
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clarifying: the issue is that alignment instructions and don’t pass, and the emotional weight that some people attach to LLMs can cause challenges that we would not see in a pure search engine.
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Calibration vs. Discrimination in Model Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a
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Paper argues metacognition may reduce AI hallucinations
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Trustworthy AI may not require omniscience. It may require epistemic honesty. A new paper by Gal Yona, Mor Geva, and Yossi Matias makes one of the clearest arguments I’ve seen for why hallucinations remain hard — and why the path forward may be metacognition. Hallucinations
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Model Calibration vs. Discrimination in AI Uncertainty
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The calibration vs. discrimination distinction is crucial. A model can know its average error rate without knowing which particular answer is wrong. That is why “just abstain when uncertain” is not enough — poor discrimination creates a utility tax. Faithful uncertainty is a