Visual AI explainability helps build trust in skeptical academic communities
Adam Wenchel · Production AI: Lessons Learned the Hard Way // Adam Wenchel, Arthur.ai (FirstMark's Data Driven NYC) · Jan 22, 2020 · at 12:18
Adam Wenchel, CEO of Arthur.ai, discusses how visual explainability tools helped researchers at Harvard's Dumbarton Oaks build trust among skeptical humanities scholars.
0:00 / 0:31exact quote · 31.9s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Ah, and then the other thing is, you can imagine this world of humanities research has not changed a whole lot in the, like, the last 200 years of study, and so bringing this sort of innovation, like, we can automate this and computers can find patterns that we've never seen before wasn't met with immediate acceptance, put it that way, ah, but by using things like explainability, ah, they're able to kind of You know, it's a good way to kind of get, show people, like, look, it's not just like, the model's not just guessing, like, it had, there's some rhyme and reason to the predictions it's making, and here you can see that, and so it's been really helpful to gaining acceptance in the scholarly community.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Adam Wenchel
Most companies using AI suffer from unpublicized model failures
“Every company that's doing anything substantive with AI probably suffers from any of these problems, it's just a few of them have actually, ah, made the headlines for it”
Open-source explainable AI tools like LIME and SHAP fail enterprise scale
“If you look at the open source components they're not very scalable. They're not easy to deploy at scale. They're really, they're useful, like, if you're a data scientist, and you have your Jupyter notebook, and you're, you know, running an experiment locally,…”
Real-world AI deployments introduce distinct failure modes beyond lab environments
“And it's not only hard to develop in the lab, but once you develop it and put it in the real world, there's a whole new set of categories of ways it can go wrong.”
Historical anti-bias regulations apply directly to AI models
“There's a lot of historical regulation around anti-discrimination and bias and things like that that, ah, certainly applies just as much to AI models as it does to humans and more simple analytical models.”
Lack of trust delays enterprise AI deployments for months
“We, you know, encounter this all the time, where in organizations, they have these big plans for AI but they're just, They're unsure about actually deploying them and turning them on, and things get held up for months and months and months because of that.”