What makes us trust analytics, and how to argue.
Weaponizing KPIs and debiasing decision algorithms.
1. Prior experience → More trust In Trustworthy Data Analysis, Roger Peng gives an elegant description of how he evaluates analytics presentations, and what factors influence his trust level. First, he imagines analytical work in three buckets: A (the material presented), B (work done but not presented), and C (analytical work not done). “We can […]
What cancer decision trees can teach us.
1. Vigilance → Better algorithms “Eliminating bias… requires constant vigilance on the part of not only data scientists but up and down the corporate ranks.” In an insightful Information Week commentary, James Kobielus (@jameskobielus) considers the importance of Debiasing Our Statistical Algorithms Down to Their Roots. “Rest assured that AI, machine learning, and other statistical […]
Debiasing is painful, why analytics fail, and health app evidence.
Suppose you’ve gotten a cancer diagnosis. Would your business experience help you navigate the care pathway? Larry Neal describes how he applied his Decision Analysis skills to prostate treatment in Eight Lessons from a Decision Professional’s Cancer Decision. When a physician said Neal had a 30% chance of having cancer, but his analysis suggested 95-99%, […]
Success theater, leaky tech pipeline, teacher bias, network meta-analysis.
1. Debiasing → Better decisions Debiasing is hard work, requiring honest communication and occasional stomach upset. But it gets easier and can become a habit, especially if people have a systematic way of checking their decisions for bias. In this podcast and interview transcript, Nobel-winning Richard Thaler explains several practical ways to debias decisions. First, […]
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 […]
But where is the decision? Data visualizations don’t show the why and how.
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 […]
Technology helps us discover meaningful patterns: Buying behavior, criminal activity, health effects. But when we succumb to pretty pictures and mindless measurement, shiny data fails to help answer important questions. Source: Tableau. This data visualization tool would be great for someone seeking crime stats – say, for law enforcement or house buying. But it doesn’t […]