Remove Measurement Remove Risk Remove Testing Remove Uncertainty
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Regulatory uncertainty overshadows gen AI despite pace of adoption

CIO Business Intelligence

Gen AI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored. We’re getting bombarded with questions and inquiries from clients and potential clients about the risks of AI.” The risk is too high.” Not without warning signs, however.

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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

Should we risk loss of control of our civilization?” If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved. And they are stress testing and “ red teaming ” them to uncover vulnerabilities.

Metrics 284
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). Test early and often. Test and refine the chatbot. Suggestion: take a look at MACH architecture.)

Strategy 289
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In AI we trust? Why we Need to Talk About Ethics and Governance (part 2 of 2)

Cloudera

Surely there are ways to comb through the data to minimise the risks from spiralling out of control. This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. Systems should be designed with bias, causality and uncertainty in mind. System Design.

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How CFOs Can Lead With Foresight

Jedox

The new normal introduced new risks from employee health and safety, supply chain stress and government mandates – all with working capital implications. The unprecedented uncertainty forced companies to make critical decisions within compressed time frames. This placed an acute spotlight on planning agility. Conclusion.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. This has serious implications for software testing, versioning, deployment, and other core development processes. Underneath this uncertainty lies further uncertainty in the development process itself. Measurement, tracking, and logging is less of a priority in enterprise software.

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CIOs press ahead for gen AI edge — despite misgivings

CIO Business Intelligence

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141