Mastodawn

> If ‘winning’ in AI means having healthier people, happier kids, a more capable workforce, and stronger science – not just bigger models or richer companies – an AI tax could help deliver victory.

https://www.ips-journal.eu/work-and-digitalisation/why-we-should-tax-ai-8696/

#AIGovernanceMatters #aigovernance #socialjustice #AIfairness

Why we should tax AI

Taxing AI is not about punishing innovation. It’s about ensuring that the rewards are shared and the risks are managed in the public interest

IPS Journal

HackerNoon Nov 11

How bias hides inside recommendation algorithms—and what new techniques reveal about gendered patterns in user embeddings. https://hackernoon.com/evaluating-attribute-association-bias-in-latent-factor-recommendation-models #aifairness

Evaluating Attribute Association Bias in Latent Factor Recommendation Models | HackerNoon

How bias hides inside recommendation algorithms—and what new techniques reveal about gendered patterns in user embeddings.

HackerNoon Nov 11

Removing gender from AI models doesn’t erase bias. Learn how systematic stereotypes persist in recommendation systems despite feature removal. https://hackernoon.com/can-we-ever-fully-remove-bias-from-ai-recommendation-systems #aifairness

Can We Ever Fully Remove Bias from AI Recommendation Systems? | HackerNoon

Removing gender from AI models doesn’t erase bias. Learn how systematic stereotypes persist in recommendation systems despite feature removal.

HackerNoon Nov 11

Even after removing gender data, bias lingers in AI. Here’s what latent space analysis reveals about hidden bias in machine learning models. https://hackernoon.com/why-gender-bias-persists-in-machine-learning-models #aifairness

Why Gender Bias Persists in Machine Learning Models | HackerNoon

Even after removing gender data, bias lingers in AI. Here’s what latent space analysis reveals about hidden bias in machine learning models.

HackerNoon Nov 10

Uncover how hidden stereotypes shape AI recommendations and learn how new frameworks can detect and reduce bias in machine learning models. https://hackernoon.com/quantifying-attribute-association-bias-in-latent-factor-recommendation-models #aifairness

Quantifying Attribute Association Bias in Latent Factor Recommendation Models | HackerNoon

Uncover how hidden stereotypes shape AI recommendations and learn how new frameworks can detect and reduce bias in machine learning models.

HackerNoon Nov 10

A framework to detect and measure bias in recommendation algorithms, revealing how AI can unintentionally reinforce stereotypes. https://hackernoon.com/understanding-attribute-association-bias-in-recommender-systems #aifairness

Understanding Attribute Association Bias in Recommender Systems | HackerNoon

A framework to detect and measure bias in recommendation algorithms, revealing how AI can unintentionally reinforce stereotypes.

Gary Ackerman May 16, 2025

"Fairness" in AI can be measured in different ways, such as ensuring similar outcomes for individuals with similar qualifications ("individual fairness") or ensuring groups have proportional outcomes ("group fairness"). #AIFairness

Miguel Afonso Caetano Mar 15, 2025

After all these recent episodes, I don't know how anyone can have the nerve to say out loud that the Trump administration and the Republican Party value freedom of expression and oppose any form of censorship. Bunch of hypocrites! United States of America: The New Land of SELF-CENSORSHIP.

"The National Institute of Standards and Technology (NIST) has issued new instructions to scientists that partner with the US Artificial Intelligence Safety Institute (AISI) that eliminate mention of “AI safety,” “responsible AI,” and “AI fairness” in the skills it expects of members and introduces a request to prioritize “reducing ideological bias, to enable human flourishing and economic competitiveness.”

The information comes as part of an updated cooperative research and development agreement for AI Safety Institute consortium members, sent in early March. Previously, that agreement encouraged researchers to contribute technical work that could help identify and fix discriminatory model behavior related to gender, race, age, or wealth inequality. Such biases are hugely important because they can directly affect end users and disproportionately harm minorities and economically disadvantaged groups.

The new agreement removes mention of developing tools “for authenticating content and tracking its provenance” as well as “labeling synthetic content,” signaling less interest in tracking misinformation and deep fakes. It also adds emphasis on putting America first, asking one working group to develop testing tools “to expand America’s global AI position.”"

https://www.wired.com/story/ai-safety-institute-new-directive-america-first/

#USA #Trump #ResponsibleAI #AISafety #AIFairness #AIEthics