Success theater, leaky tech pipeline, teacher bias, network meta-analysis.
Redefining data science skill, biased policy decisions, and data strategy.
1. Biased instructor response → Students shut out Definitely not awesome. Stanford’s Center for Education Policy Analysis reports Bias in Online Classes: Evidence from a Field Experiment. “We find that instructors are 94% more likely to respond to forum posts by white male students. In contrast, we do not find general evidence of biases in […]
Machines Gone Wild! + Can Microlearning improve Data Science training?
1. Biased analysis → Misunderstood cause-effect In Biased Ways We Look at Poverty, Adam Ozimek reviews new evidence suggesting that food deserts aren’t the problem, behavior is. His Modeled Behavior (Forbes) piece asks why the food desert theory got so much play, claiming “I would argue it reflects liberal bias when it comes to understanding […]
Analytics translators wanted, algorithm vs. human, and winning with diversity.
1. Machines Gone Wild → Digital trust gap Last 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. — […]
Cognitive bias in algorithms, baseball analytics denied, and soft skills ROI.
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 […]
Long-term thinking, systems of intelligence, and the dangers of sloppy evidence.
1. Recognize bias → Create better algorithms Can 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 […]
Meetup 25-Jan-2018: Papers We Love
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 […]
Underwriters + algorithms, avoiding bad choices, and evidence for rare illness.
Our founder, Tracy Allison Altman, will talk about behavioral economics for software design @ Papers We Love – Denver on Jan 25. 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 […]
Algorithm reluctance, home-visit showdown, and the problem with wearables.
1. Underwriters + algorithms = Best of both worlds. We hear so much about machine automation replacing humans. But several promising applications are designed to supplement complex human knowledge and guide decisions, not replace them: Think primary care physicians, policy makers, or underwriters. Leslie Scism writes in the Wall Street Journal that AIG “pairs its […]
Valuing patient perspective, moneyball for tenure, visualizing education impacts.
Hello there. We had to step away from the keyboard for awhile, but we’re back. And yikes, evidence-based decisions seem to be taking on water. Decision makers still resist handing the car keys to others, even when machines make better predictions. And government agencies continue to, ahem, struggle with making evidence-based policy. — Tracy Altman, editor 1. Evidence-based […]
1. Formalized decision process → Conflict about criteria It’s usually a good idea to establish a methodology for making repeatable, complex decisions. But inevitably you’ll have to allow wiggle room for the unquantifiable or the unexpected; leaving this gray area exposes you to criticism that it’s not a rigorous methodology after all. Other sources of […]