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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

Most notably, The Future of Life Institute published an open letter calling for an immediate pause in advanced AI research , asking: “Should we let machines flood our information channels with propaganda and untruth? Today, we have dozens of organizations that publish AI principles, but they provide little detailed guidance.

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5 signs your agile practices will lead to digital disaster

CIO Business Intelligence

They are afraid of failure and the uncertainty of knowledge work, and so that’s stressful. Agile is an amazing risk management tool for managing uncertainty, but that’s not always obvious.” The key is recognizing that planning must be an agile discipline, not a standalone activity performed independently of agile teams.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon.

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

Cloudera

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. Systems should be designed with bias, causality and uncertainty in mind. Uncertainty is a measure of our confidence in the predictions made by a system. We need to get to the root of the problem. System Design. Model Drift.

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What’s so Special About 50:50?

Andrew White

See Data and Analytics Strategies Need More-Concrete Metrics of Success. The article notes this as cognitive uncertainty. But it happens to relate closely to our analysis of business metrics and ROI being used to justify investment and report success. As such an ROI would have been impossible. It’s called ROAR.

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face. These characteristics of the problem drive the forecasting approaches.