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Chris Butler 13h
A core tenant of should be understanding human biases in decision making to bring us closer to our ideal decisions. It isn't clear this Zillow algo is doing that other than motivating selling. Also, stop saying "human-in-the-loop," we always are.
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mauro_iker 2h
framework to systematically compare and contrast explainability approaches, not just to better understand their capabilities but also to identify discrepancies between their theoretical qualities and properties of their implementations
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Cédric Buron 4h
Interesting blog post about the usage of Shapley value to assess the influence of a feature in a ML predictor. This is the kind of works leading towards .
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Federico Bo Dec 9
The Explainable () is a relatively new field of research that aims to develop techniques and methods to make the results obtained by Artificial Intelligence technologies understandable by humans.
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Sylvie Dan Dec 10
is set to transform global productivity, working patterns, and lifestyles and create enormous wealth. “Google’s new ‘Explainable AI” () service” by Tirthajyoti Sarkar
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Zelros Dec 8
Will the future of work be ethical?
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Federico Bo Dec 10
"While the first wave of algorithmic accountability focuses on improving existing systems, a second wave of research has asked whether they should be used at all—and, if so, who gets to govern them."
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Xiang 'Anthony' Chen 24h
Excited to share 1/2 cont'd accepted paper from UCLA HCI: CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
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Stratyfy, Inc Dec 10
Excited to see our CEO Laura Kornhauser doing a *1 minute pitch* at Semifinals!🔥 Thank you to many corporate partners that support this great program.
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H2O.ai Dec 4
Want to dive into the concept of Explainable AI? Download our free today to learn how to use for more effective and trustworthy decision making in your :
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I.T. Works (Patrick) 8h
Great speakers and cases at our "Trustworthy and Ethical AI Conference". Still looking for a few extra human-centric AI cases where trust, transparency, accountability and explainability are crucial. More info: cc
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Don Teague Dec 9
This blog explores how facial recognition AI solves crimes, and what the technology's lack of human accountability means for your . Read more here. By
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Ed Holloway 5h
Discover how to use AI for good in your business, using these case examples of responsible AI across industries. Read more here. By
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Diveplane 6h
"If we can assess the that goes into training a model... & continuously evaluate [its] performance, then we can find the flaws in the system that produce unintentional bias and fix them." See our website for more info & check out this piece on :
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𝒜𝓊𝒹𝓇𝑒𝓎 𝒟𝑒𝒮𝒾𝓈𝓉𝑜 🌟 11h
Why explainable AI is indispensable to Zillow’s business Read more:
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Cédric Buron Dec 11
Replying to @cedric_buron
It does not introduce the methods for , but provides good introduction to the reasons of the necessity of this topic, accessible to anyone. I strongly advise it, in particular to those who are not familiar with AI.
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adriandwalker Dec 5
Replying to @hpdailyrant
Nice post. Similarly, Executable English at the author- and user-interface helps to anchor the interaction. In particular, results are explained at the nontechnical user level There's a system that demonstrates this, live online at
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Frank Pasquale 6h
For those working on , as well as rights to explanation or meaningful information pursuant to , this piece should be very interesting. I was very impressed by Strandburg's presentation at the conference.
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Robert Zembowicz Dec 8
Explainability: Cracking open the black box, Part 1 via
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Olivier Gasquet Dec 11
precisely aims at by hybrid AI
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