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#validity

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Improving Numerical Measures of Human Feelings: The Case of Pain d.repec.org/n?u=RePEc:lan:wpap
"… proposed and validated a new method to measure the experienced intensity of acute pain across individuals. This method proved to be overwhelmingly more accurate and sensitive than standard, established methods for experimentally-induced acute pain which are used in medical, clinical and research applications
… In the case of experienced pain intensity, the mechanism that enables the new #measurement method to excel might indeed be rooted in its well-calibrated scale: #money.
… Money-free standard methods ask people for numerical estimations of their experiences without a benchmark for the numerical scale, which might lead them to choose salient numbers or intermediate values.
… Monetary amounts provide interpersonally comparable references and thereby reduce the intrinsic subjectivity in pain measurements.
… The use of monetary incentives is unproblematic for those applications, as it is already common practice to reward participation in them"
#ExperimentalEcon #calibration #validity #interpersonalComparison

🔔 New Essay 🔔

"The Intelligent AI Coin: A Thought Experiment"

Open Access here: seanfobbe.com/posts/2025-02-21

Recent years have seen a concerning trend towards normalizing decisionmaking by Large Language Models (LLM), including in the adoption of legislation, the writing of judicial opinions and the routine administration of the rule of law. AI agents acting on behalf of human principals are supposed to lead us into a new age of productivity and convenience. The eloquence of AI-generated text and the narrative of super-human intelligence invite us to trust these systems more than we have trusted any human or algorithm ever before.

It is difficult to know whether a machine is actually intelligent because of problems with construct validity, plagiarism, reproducibility and transferability in AI benchmarks. Most people will either have to personally evaluate the usefulness of AI tools against the benchmark of their own lived experience or be forced to trust an expert.

To explain this conundrum I propose the Intelligent AI Coin Thought Experiment and discuss four objections: the restriction of agents to low-value decisions, making AI decisionmakers open source, adding a human-in-the-loop and the general limits of trust in human agents.

@histodons @politicalscience

seanfobbe.com · [Essay] The Intelligent AI Coin: A Thought Experiment
More from Seán Fobbe

I heard about this recently and I think it's brilliant. As the world is increasingly overwhelmed with (mostly bad, overblown) AI art and more, there's still a market of those who value integrity when it comes to their work. The Content Credentials website is entirely voluntary, but it shows those who are interested - in fairly detailed terms - what's been done to a digital creation - be it photography, music or whatever...

As an example, using Capture One 16.5to process a digital image, cut to about 1:02:10 in this video: youtube.com/live/-C73BzXdaqM?t

Capture One 16.5 has a checkbox to add this to the metadata on export.

More information on the Content Credentials website, here: contentcredentials.org/

P.S. I think AI art (well used) has validity, so long as one is clear on the origins and processes involved. Stealing people's work to train LLMs is an entirely different subject.

The UPC has revoked the patent already indicated to be invalid in provisional injunction proceedings between 10x Genomics and NanoString. The reasoning follows the prior UPC and German decisions on the patent and a related patent.

It's probably no surprise that, doubts having been expressed about the validity of the patent in provisional proceedings, the UPC gone on to revoke it.

eipamar.com/en/knowledge-hub/a

EIP Amar10x Genomics patent revoked