Predictive Analytics
By: Sherrill W. Hayes, Executive Director of the Analytics and Data Science Institute and Professor of Conflict Management,
Kennesaw State University
The complexity of the ethical issues facing professionals who work in machine learning, data science, analytics, and related professions have all the hallmarks of a “wicked problem”. Rittel and Weber, the researchers responsible for coining the term “wicked problems”, believed a more inclusive approach to problem-solving was especially important in diverse and pluralistic societies where
Straight from the horse’s mouth – the founding chair of the all-new Predictive Analytics World for Climate, Eugene Kirpichov, along with his colleague, Cassandra Xia – read this article for the central role machine learning has...
Originally published in KDNuggets. This article is based on a transcript from Eric Siegel’s Machine Learning for Everyone. View the video version of this specific article Nowhere could the application of machine learning prove more important...
Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to...
By: Michael Totty
Originally published in The Wall Street Journal, Nov 3, 2020. AI systems can unfairly penalize certain segments of the population—especially women and minorities. Researchers and tech companies are figuring out how to address that. As artificial intelligence...
It’s the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. But while...
Originally published in IEEE Spectrum, Nov 4, 2020. Based on a cellphone-recorded cough, machine learning models accurately detect coronavirus even in people with no symptoms. Again and again, experts have pleaded that we need more and faster...
David Kastelman, Data Scientist & Raghav Ramesh,
Machine Learning Engineer
Originally published in DoorDash Engineering Feb 13, 2018. To A/B or not to A/B, that is the question Overview On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our...
Originally published in Towards Data Science on Oct 26, 2020. This year, Twitter sponsored the RecSys 2020 Challenge, providing a large dataset of user engagements. In this post, we describe the challenge and the insights we had...
Originally published in Big Think When you harness the power and potential of machine learning, there are also some drastic downsides that you’ve got to manage. Deploying machine learning, you face the risk that it be discriminatory,...
