Remove Measurement Remove Metrics Remove Statistics Remove Uncertainty
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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.

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

Cloudera

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. Uncertainty is a measure of our confidence in the predictions made by a system. System Design. Model Drift.

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What you need to know about product management for AI

O'Reilly on Data

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. the fraction of video recommendations resulted in positive user experiences).

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.