Predictive hiring algorithms are rife with bias.

The founders of Predictim want to be clear with me: Their product—an algorithm that scans the online footprint of a prospective babysitter to determine their “risk” levels for parents—is not racist. It is not biased.

The opening summary is pretty great.

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outco

Its carefully curated search engine serves up anti-Semitism in response to reasonable queries. The problem reflects a wider issue in the world of tech.

Despite efforts against hateful and false content, those posts and videos are thriving. One Instagram search produced nearly 12,000 posts with the hashtag “#jewsdid911.”

Earlier this year, Amazon’s facial recognition tech Rekognition — which can scan photos and videos and match them against databases...

Health-care artificial intelligence currently reflects the same racial and gender biases as the culture at large. Those prejudices are built into the data.

Nature - Computer scientists must identify sources of bias, de-bias training data and develop artificial-intelligence algorithms that are robust to skews in the data.

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors...

Which professions have a set-apart "woman X" term, and which a "male X" term?

Last updated: 07/05/2019 If you are a data scientist, a software developer, or in the social and human sciences with interest in digital humanities, then you’re no stranger to the ongoing discussio…

And that means they’re illegal.

Wikipedia’s coverage on women is less comprehensive, and its volunteer editor base is mostly male. What can be done to change the numbers?

Talks about libraries, technology, and the Web by Matthew Reidsma.

On search engines and social media, their distortions of society, and what we can do about them.

Allocative and representational harm

Scientific peer review has been a cornerstone of the scientific method since the 1600s. Debate continues regarding the merits of single-blind review, in which anonymous reviewers know the authors of a paper and their affiliations, compared with double-blind review, in which this information is hidden. We present an experimental study of this question. In computer science, research often appears first or exclusively in peer-reviewed conferences rather than journals. Our study considers full-length submissions to the highly selective 2017 Web Search and Data Mining conference (15.6% acceptance rate). Each submission is simultaneously scored by two single-blind and two double-blind reviewers. Our analysis shows that single-blind reviewing confers a significant advantage to papers with famous authors and authors from high-prestige institutions.