Devvin
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dearnest.bsky.social
Devvin
@dearnest.bsky.social
14 followers 310 following 5 posts
Innovation. Product Development. Systems.
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michaelhogan.bsky.social
Algorithmic governance: Developing a research agenda through the power of collective intelligence

journals.sagepub.com/doi/full/10....
Algorithmic governance: Developing a research agenda through the power of collective intelligence - John Danaher, Michael J Hogan, Chris Noone, Rónán Kennedy, Anthony Behan, Aisling De Paor, Heike Fel...
We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour ...
journals.sagepub.com
November 24, 2024 at 10:22 PM
Reposted by Devvin
stefanherzog.bsky.social
🌟🤖📝 **Boosting human competences with interpretable and explainable artificial intelligence**

How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.

doi.org/10.1037/dec0...

#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
Article information Title: Boosting human competences with interpretable and explainable artificial intelligence. Full citation: Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. https://doi.org/10.1037/dec0000250 Abstract: Artificial intelligence (AI) is becoming integral to many areas of life, yet many—if not most—AI systems are opaque black boxes. This lack of transparency is a major source of concern, especially in high-stakes settings (e.g., medicine or criminal justice). The field of explainable AI (XAI) addresses this issue by explaining the decisions of opaque AI systems. However, such post hoc explanations are troubling because they cannot be faithful to what the original model computes—otherwise, there would be no need to use that black box model. A promising alternative is simple, inherently interpretable models (e.g., simple decision trees), which can match the performance of opaque AI systems. Because interpretable models represent—by design—faithful explanations of themselves, they empower informed decisions about whether to trust them. We connect research on XAI and inherently interpretable AI with that on behavioral science and boosts for competences. This perspective suggests that both interpretable AI and XAI could boost people’s competences to critically evaluate AI systems and their ability to make accurate judgments (e.g., medical diagnoses) in the absence of any AI support. Furthermore, we propose how to empirically assess whether and how AI support fosters such competences. Our theoretical analysis suggests that interpretable AI models are particularly promising and—because of XAI’s drawbacks—preferable. Finally, we argue that explaining large language models (LLMs) faces similar challenges as XAI for supervised machine learning and that the gist of our conjectures also holds for LLMs.
November 20, 2024 at 12:25 PM
dearnest.bsky.social
Love this write-up by Dan Gardner.

dgardner.substack.com/p/the-diseas...
The Disease of the Powerful
Intellectual arrogance cripples the powerful but it is a danger to us all.
dgardner.substack.com
November 19, 2024 at 11:00 PM
dearnest.bsky.social
Bureaucracy is so fascinating. Since groups always seem to drift toward it like they're pursuing equilibrium, it must provide a benefit. But what?
November 18, 2024 at 9:45 PM