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Machines Gone Wild! + Can Microlearning improve Data Science training?

21 February 2018By
1. Machines Gone Wild → Digital trust gapLast year I spoke with the CEO of a smallish healthcare firm. He had not embraced sophisticated analytics or machine-made decision making, with no comfort level for ‘what information he could believe’. He did, however, trust the CFO’s recommendations. Evidently, these sentiments are widely shared. — Tracy A...
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Translators at IFLA 2010

Analytics translators wanted, algorithm vs. human, and winning with diversity.

15 February 2018By
1. Hire analytics translators → Keep data scientists happy An emerging role – what some call the Analytics Translator – is offloading burden from data scientists, while helping business executives get better value from their technology investments. A recent HBR piece explains You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics...
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Cognitive bias in algorithms, baseball analytics denied, and soft skills ROI.

31 January 2018By
1. Recognize bias → Create better algorithmsCan we humans better recognize our biases before we turn the machines loose, fully automating them? Here’s a sample of recent caveats about decision-making fails: While improving some lives, we’re making others worse. Yikes. From HBR, Hiring algorithms are not neutral. If you set up your resume-screening algorithm to...
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Long-term thinking, systems of intelligence, and the dangers of sloppy evidence.

24 January 2018By
1. Long view → Better financial performance.A McKinsey Global Institute team sought hard evidence supporting their observation that “Companies deliver superior results when executives manage for long-term value creation,” resisting pressure to focus on quarterly earnings (think Amazon or Unilever). So MGI developed the corporate horizon index, or CHI, to compare performance by firms exhibiting...
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Papers We Love: Judgment Under Uncertainty / Cognitive Bias

17 January 2018By
Our founder, Tracy Allison Altman, will talk about cognitive bias and behavioral economics for software design @ Papers We Love – Denver. Tversky and Kahneman’s classic “Judgment under Uncertainty: Heuristics and Biases” challenged conventional thinking about bias in decision making, inspiring new approaches to cognitive science, choice architecture, public policy, and the underlying technology. Join...
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Museum musings.

Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

Recent Articles

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20 June 2023
Stolen cars and AI ‘moral self-correction’
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9 May 2023
Can you trust AI with your next decision? Part 3 in a series on fact-checking/citation
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25 April 2023
How is generative AI referencing sources? Part 2 in our series
22 April 2023
Sneaky STEM: Inspire learning with immersive experiences
15 March 2023
Can AI replace your CEO?