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NIST launches ambitious effort to assess LLM risks

CIO Business Intelligence

NIST’s new Assessing Risks and Impacts of AI (ARIA) program will “assess the societal risks and impacts of artificial intelligence systems,” the NIST statement said, including ascertaining “what happens when people interact with AI regularly in realistic settings.” Still, Prins said that evaluating just the code is valuable. “In

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Preliminary Thoughts on the White House Executive Order on AI

O'Reilly on Data

Like other internet-available services, AI models are not static artifacts, but dynamic systems that interact with their users. These should not be a random grab-bag of measures thought up by outside regulators or advocates, but disclosures of the actual measurements and methods that the companies use to manage their AI systems.

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Bubble Wrap: How Eurovision Monitors Covid Risk at Events

CIO Business Intelligence

Knowing your risk level as you navigate a large venue can help you avoid crowds and stay safely within your bubble – all of which empowers you to enjoy the experience all the more. Live at Eurovision: a Bluetooth App to Navigate Covid Risk. A New Normal: Bubble-Up for Safety at Live Events with Flockey. So, how does it work?

Risk 82
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The risks and limitations of AI in insurance

IBM Big Data Hub

In my previous post , I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

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Managing risk in machine learning

O'Reilly on Data

Fortunately, a recent survey paper from Stanford— A Critical Review of Fair Machine Learning —simplifies these criteria and groups them into the following types of measures: Anti-classification means the omission of protected attributes and their proxies from the model or classifier. Continue reading Managing risk in machine learning.

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What We Learned Auditing Sophisticated AI for Bias

O'Reilly on Data

When we use AI in security applications, the risks become even more direct. As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. applies external authoritative standards from laws, regulations, and AI risk management frameworks.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It encompasses risk management and regulatory compliance and guides how AI is managed within an organization.

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