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Stolen cars and AI ‘moral self-correction’

20 June 2023By
Is AI getting closer to human values? It’s no secret that algorithms can produce systemically biased recommendations, such as in lending decisions. Some good news: A team at Anthropic tested the hypothesis that “language models trained with reinforcement learning from human feedback (RLHF) have the capability to ‘morally self-correct’ — to avoid producing harmful outputs...
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person in silhouette with orange background, pondering AI input for an evidence based decision

Can you trust AI with your next decision? Part 3 in a series on fact-checking/citation

9 May 2023By
Where were you on 30 November, 2022? When OpenAI made ChatGPT vers. 3.5 generally available? By some accounts, the heavens opened up, showering us with new knowledge and tools for creating valuable content. Five months on, consumer-facing, generative AI has started a frenzy, and is often confused with “artificial intelligence” ~ although it’s but one...
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reference to Google's Bard AI and Microsoft's Bing AI compared to conflict at Sopranos Bada Bing

What’s state-of-the-art when an AI cites sources of evidence? Part 1 in our series

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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silver corded microphone in shallow focus photography

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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prostatecancerdecision.org

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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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

person in silhouette with orange background, pondering AI input for an evidence based decision
9 May 2023
Can you trust AI with your next decision? Part 3 in a series on fact-checking/citation
image generated by bing image creator bottle on apothecary shelf
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?
reference to Google's Bard AI and Microsoft's Bing AI compared to conflict at Sopranos Bada Bing
28 February 2023
What’s state-of-the-art when an AI cites sources of evidence? Part 1 in our series