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#TrustworthyAI

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Predictive AI technology has a well-known issue: how do we know the component is reliable, and can we trust the result enough to use it in our work? Learn more on how DW Innovation has found ways to make an AI-powered service more transparent and secure, assisting not only end-users, but also the process of AI governance.

innovation.dw.com/articles/ai-

innovation.dw.comAI in Media Tools: How to Increase User Trust and Support AI GovernancePrinciples like Trustworthy AI are well documented – but what about their implementation?

Which of these images provides the best explanation for turning a '9' into a '7'?

The right-most counterfactual looks most plausibly like a '7' but which one most faithfully explains the behavior of the underlying model that confidently predicts '7' in all cases?

Our latest research on #TrustworthyAI proposes a way to reconcile the notions of faithfulness (consistent with the model) and plausibility (consistent with the data).

🧵 1/n (or jump straight to paltmeyer.com/content/talks/po)

Here's an article I wrote after ruminating on this for the past few weeks, including with the help of some readers here (thank you!). Among the many thorny issues raised by LLMs, this would be low-hanging fruit. What do you think? Can the fediverse hive mind help me bring this proposal to the attention of the people who can make it happen? #AI #LLMs #TrustworthyAI #EthicalAI
livepaola.substack.com/p/an-et

Paola WritesAn Ethical AI Never Says "I" By Paola Writes