Top news of the week: 11.10.2020.

Machine learning, Artificial intelligence, Public Health, Color, Nation, Fuzzy logic

Ethics

On Oct 11, 2020
@mtlaiethics shared
"Accountability—that is, having an individual who is responsible for the decisions made by the algorithm—is a principle championed by organizations like Rapid7, Microsoft, the Partnership on AI, and the Montreal AI Ethics Institute." -@ErickGalinkin https://t.co/9ULC7hYCUX
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AI (Artificial Intelligence) Governance: How To Get It Right

AI (Artificial Intelligence) Governance: How To Get It Right

It’s a complex area but needs to be a high priority.

On Oct 9, 2020
@oiioxford shared
RT @SandraWachter5: I am incredibly honored to see my work on Affinity Profiling & Discrimination by Association in OBA https://t.co/IHT7lde96C & Counterfactual Explanations https://t.co/ua22FwYtNb cited in the UK Parliament @POST_UK report on Interpretable machine learning https://t.co/u7zEE5qp3v
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Interpretable machine learning

Interpretable machine learning

Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. ...

On Oct 9, 2020
@FrankPasquale shared
Very happy to have this interview on my @Harvard_Press book "New Laws of Robotics" published today https://t.co/QqkfvXpxp5
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How to Make AI More Humane

How to Make AI More Humane

The technology that drives more of our societies and economies is too important to be left just to coders, says law professor Frank Pasquale.

On Oct 7, 2020
@mtlaiethics shared
RT @stephenjcave: A nice summary of my and @DrDihal 's Whiteness of AI paper by @victoria_heath7 at @mtlaiethics https://t.co/C6rqyRDs9p https://t.co/hf0jvN00vt
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The Whiteness of AI (Research Summary)

The Whiteness of AI (Research Summary)

Summary contributed by our researcher Victoria Heath (@victoria_heath7). She's also a Communications Manager at Creative Commons. *Authors of full paper & link at the bottom Mini-summary: ...

On Oct 9, 2020
@LeverhulmeCFI shared
RT @mtlaiethics: AI Ethics newsletter #26: ➜The Whiteness of AI (@stephenjcave+@DrDihal) ➜A Snapshot of the Frontiers of Fairness in ML (@achould+@aaroth) ➜Aging in an Era of Fake News (@nadiabrashier) ➜Preparing for Election Misinformation (@rebheilweil of @Recode) https://t.co/P9HFQ206P5
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AI Ethics #26: Whiteness of AI, frontiers of fairness, aging in an era of fake news, politics of adversarial machine learning, protect yourself from misinformation, and more ...

AI Ethics #26: Whiteness of AI, frontiers of fairness, aging in an era of fake news, politics of adversarial machine learning, protect yourself from misinformation, and more ...

Being watched while taking an exam, first-party tracking, insuring the future of work, what does it mean to be a bot, and more from the world of AI Ethics!

On Oct 9, 2020
@Floridi shared
Hyperhistory, the Emergence of the MASs, and the Design of Infraethics https://t.co/mAPAnRx9vu #baseitalia @baseitaliaweb
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Hyperhistory, the Emergence of the MASs, and the Design of Infraethics

Hyperhistory, the Emergence of the MASs, and the Design of Infraethics

"Hyperhistory, the Emergence of the MASs, and the Design of Infraethics" by Luciano Floridi is part of the book "The Next Step: Exponential Life"

On Oct 4, 2020
@j2bryson shared
Somehow I missed this, even before it started sucking up some of my time! But here's a list of the 9 experts Germany nominated to the Global Partnership on AI (#GPAI), including me. https://t.co/ohNpCwQ6Wl
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Newly launched “Global Partnership on AI” commencing work

Newly launched “Global Partnership on AI” commencing work

The newly formed Global Partnership on Artificial Intelligence (GPAI) initiative brings together leading international experts with the aim of promoting the responsible and human-centric ...

On Oct 7, 2020
@WIRED shared
Stop guessing. We put together a guide on everything you should know about AI. https://t.co/gvAjR1MLQl
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The WIRED Guide to Artificial Intelligence

The WIRED Guide to Artificial Intelligence

Supersmart algorithms won't take all the jobs, But they are learning faster than ever, doing everything from medical diagnostics to serving up ads.