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presenting the evidence

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muscle car by bing/create

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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image generated by bing image creator bottle on apothecary shelf

How is generative AI referencing sources? Part 2 in our series

25 April 2023By
Continuing our analysis of how the new bots are supporting their statements with evidence. Things are changing so rapidly that some of the responses we wrote about just several weeks ago have changed substantially. Today we look at ChatGPT, Bard, Bing, and scite. Bing shines with its presentation design. ChatGPT. When we ask Should I take zinc for...
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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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woman exiting revolving door

Struggling to explain AI? Try this before|after strategy.

30 January 2020By
To cut through AI complexity, focus on decisions. Never has it been more important to effectively explain complex concepts. Technology is influencing most decision processes, not always transparently so. On the bumpy road toward explainable AI (XAI), we find great communication options, from printed materials to state-of-the-art experiences. But where do you start, or know...
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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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PitchLab shows how to present complex technology.

13 September 2018By
Present everything better! As co-organizer of the meetups Papers We Love – Denver and Domain-Driven Design – Denver, I was delighted to co-host PitchLab for a talk on presentation skills. Jay Mays and Keefer Caid-Loos did an excellent job explaining how to connect with your audience. Participants were engaged, and appreciated PitchLab’s approachable, ask-me-anything attitude. The...
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fox

What makes us trust analytics, and how to argue.

7 June 2018By
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...
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decision bias in food-poverty policy

Redefining data science skill, biased policy decisions, and data strategy.

28 February 2018By
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...
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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