Nice exercise that provides some nice contrarian thinking, as long as one is aware that the methodology of cost-benefit analysis (as applied here) seems to ignore systemic risks (e.g. climate change) and under-plays the difficulty of getting from theory to policy (let alone realization).
It’s always tricky… claiming to be comprehensive. In particular where it concerns LLMs.
And that;s where the paper Decoding Trust [..] stumbles. Right in the title is claims “A Comprehensive Assessment of Trustworthiness in GPT.” Nonetheless, when reading about this research on one of my favorite blogs, I decided to have a closer look.
The authors propose a framework with eight perspectives on trustworthiness:
Toxicity
Stereotype bias
Adversarial robustness
Out-of-distribution robustness
Robustness to adversarial demonstrations
Privacy
Machine ethics
Fairness
They then continue to develop that into a benchmark for GPT models and present the empirical results on GPT-3.5 and GPT-4.
Although the results are interesting, there are some concerns with this type of benchmark approach.
The framework in nowhere near “comprehensive”. For example: it does not include factual correctness (which I would posit as a a prerequisite for trust); nor does is test for being politically opinionated (which I would say is highly relevant).
The choice of benchmark prompts is in nature never neutral, and should be made dependent on the context in which the LLM is applied.
As with any public benchmark, its value will diminish over time as the prompts and desired responses will become part of the training of next generation LLMs.
On the positive side, the paper brings a lot of inspiration for organizations for how they can shape their own testing approach for trustworthy GenAI. Even if not comprehensive, a framework like this as a starting point is massively useful and important.
The book reads as a detective, exploring what we know and what we can reasonably conjecture about the creation of Stonehenge based on the archeological record and examples from indigenous civilizations.
The book continues to drift between wonder about the world and weakly motivated bias towards human scale, which is a pity because it cites some elegant analyses.
Great to see journalists initiating change in their own organization.
Fri Aug 25: Guardian journalist Ariel B. reports that other news media have started blocking GPTbot. The subtly note in his article: “The Guardian’s robot.txt file does not disallow GPTBot.” (Version Sept 3, 2023)
Fri Sept 1: Guardian leadership has taken notice and blocks GPTbot – as reported here.
As I have noted earlier, data access is a major topic when it comes to achieving a healthy power balance in the information space here and here. Glad to see more and more companies take this seriously.
Personally, I currently see little incentives for companies, organization, or individuals to allow their data to be crawled by for profit.
The book is mostly written from the historical perspective free from contemporary judgements, which allows the writer to tell a nuanced story on a sensitive topic.
A brave attempt to put up a framework for assessing technological innovations, that is rich of ideas, which are in many cases [in 2023] still relevant (e.g. Cognifying in the light of GenAI), but sometimes feel out-dated (e.g. Sharing is a post-truth world).
The author underplays the role of religious power structures in suppressing novel scientific ideas that go against traditionalist dogmas, which makes the book read more like a christian apology than a balanced historical narrative.