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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 98
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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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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.

Risk 74
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Automating Model Risk Compliance: Model Validation

DataRobot Blog

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

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The AI cat and mouse game has begun

CIO Business Intelligence

To counter AI-generated threats, CIOs and CISOs must deploy AI-based defensive measures. AI-based identity management and access control technologies are essential for enhancing cybersecurity measures. This problem gets worse as you consider business partners, customers, and third-party vendors.

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