Remove Reporting Remove Testing Remove Uncertainty
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

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. If every company had a different way of reporting its finances, it would be impossible to regulate them. There is no simple way to solve the alignment problem.

Metrics 359
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Regulatory uncertainty overshadows gen AI despite pace of adoption

CIO Business Intelligence

It’s no surprise, then, that according to a June KPMG survey, uncertainty about the regulatory environment was the top barrier to implementing gen AI. So here are some of the strategies organizations are using to deploy gen AI in the face of regulatory uncertainty. With AI, their users can get extremely smart research assistants.

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3 ways to avoid the generative AI ROI doom loop

CIO Business Intelligence

At least as it was reported, it comes across sounding like a flip dismissal of what genAI might have to offer. He did not get to the point of 100% specificity and confidence about exactly how this makes him happier and more productive through a quick one-and-done test of a use case or two.

ROI 72
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AI Product Management After Deployment

O'Reilly on Data

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. To support verification in these areas, a product manager must first ensure that the AI system is capable of reporting back to the product team about its performance and usefulness over time.

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Why HR professionals struggle with big data

CIO Business Intelligence

Viole Kastrati: Without systematic and continuous reporting, it is almost impossible to get a complete picture of the personnel situation and make informed decisions based on it. This is due, on the one hand, to the uncertainty associated with handling confidential, sensitive data and, on the other hand, to a number of structural problems.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. One of the primary sources of tension?

Testing 169
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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.