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What’s state-of-the-art when an AI cites sources of evidence?

28 February 2023By
Now that machines are answering questions and making decisions, it’s essential that people and AI get on the same page when citing evidence. What sources are considered acceptable? Where should citations be presented (inline, in conventional bibliographies, or…)? Should data/analysis be simply referenced, or transparently included in supporting data sets? Here we look at state-of-the-art...
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Our #1 fear: Withering under scrutiny.

6 February 2019By
Jerry Seinfeld was wrong when he claimed public speaking is our #1 fear. I’m pretty sure we’re more afraid of having our decisions scrutinized. Adding to the fun, now algorithmic decisions are under pressure too. It is rather painful to have decisions second-guessed before the numbers come in, and even worse if things go pear-shaped....
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The skill set for explaining, XAI, and why they both matter.

1 December 2018By
As data complexity grows, so does the importance of explaining. The philosophy of science can teach us about the role of explaining in high-quality, evidence-based decisions. It’s not just navel-gazing: An explanation is a statement that makes something clear, or a reason or justification given for an action or belief. It describes “a set of...
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Building trust in the decision process

Evaluate your decision process separately from your outcomes.

12 August 2018By
How we decide is no less important than the data we use to decide. People are recognizing this and creating innovative ways to blend what, why, and how into decision processes. 1. Apply behavioral science → Less cognitive bias McKinsey experts offer excellent insight into Behavioral science in business: Nudging, debiasing, and managing the irrational...
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Weaponizing KPIs and debiasing decision algorithms.

31 May 2018By
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...
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What cancer decision trees can teach us.

22 May 2018By
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%,...
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Debiasing is painful, why analytics fail, and health app evidence.

15 May 2018By
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,...
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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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action->outcome in PepperSlice

Guide: Promoting Evidence-Based Insights

10 February 2018By
Ugly Research offers a new guide, Promoting Evidence-Based Insights. Practical tips for presenting content with four essential characteristics: Top-line, evidence-based, bite-size, and reusable. Communicate quickly and powerfully by demonstrating value to decision makers. Show how A → B. So people don’t think tl;dr or MEGO (My Eyes Glaze Over), be sure to emphasize insights that...
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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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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.

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