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Beyond “Prompt and Pray”

O'Reilly on Data

Your companys AI assistant confidently tells a customer its processed their urgent withdrawal requestexcept it hasnt, because it misinterpreted the API documentation. This fueled a belief that simply making models bigger would solve deeper issues like accuracy, understanding, and reasoning. Development velocity grinds to a halt.

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Risk Management for AI Chatbots

O'Reilly on Data

Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. ” ) With a chatbot, the web form passes an end-user’s freeform text input—a “prompt,” or a request to act—to a generative AI model.

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UK launches platform to help businesses manage AI risks, build trust

CIO Business Intelligence

The UK government has introduced an AI assurance platform, offering British businesses a centralized resource for guidance on identifying and managing potential risks associated with AI, as part of efforts to build trust in AI systems. About 524 companies now make up the UK’s AI sector, supporting more than 12,000 jobs and generating over $1.3

Risk 130
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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 278
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5 top business use cases for AI agents

CIO Business Intelligence

There are risks around hallucinations and bias, says Arnab Chakraborty, chief responsible AI officer at Accenture. Meanwhile, in December, OpenAIs new O3 model, an agentic model not yet available to the public, scored 72% on the same test. That adds up to millions of documents a month that need to be processed.

Software 143
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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns. CIOs should consider placing these five AI bets in 2025.