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Some people are said to be unaware that their data had been used for a facial-recognition project.
We need to stop perpetuating the false dichotomy between technology and the humanities. To do otherwise would be detrimental to society.
Idealism can eventually bow to financial pressure and artificial intelligence companies, big and small, can change course.
Opinion: Humans train the machine-learning and AI systems at Facebook, Google, and Twitter to filter out bias. The problem: they don't know what they're looking for.
A whole book! Open access!
Op-ed: a computer scientist weighs in on the downsides of AI.
Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit
On Twitter, your words are predictable using the words of your network.
Algorithms are best at pursuing a single mathematical objective—but humans often want multiple incompatible things.
GREAT intro piece.
Opinion: The 2009 vs. 2019 profile picture trend may or may not have been a data collection ruse to train its facial recognition algorithm. But we can't afford to blithely play along.
Advances in artificial intelligence, including activity trackers, smartphones and smartwatches, threaten the privacy of people's health data, according to new research from the University of California, Berkeley.
"Like most technologies, machine learning is neutral." Yeah, that is some bullshit right there.
AI pratfalls-through-lateral-thinking.
Emerging as a comprehensive and aggressive governance scheme in China, the “Social Credit System” (SCS) seeks to promote the norms of “trust” in the Chinese soc
From the French Revolution to the history of the novel, Big Data makes its mark.
REALLY NICE overview of Big Data in education and why it's problematic.
A Facebook spokesperson declined to comment on whether advertisers influenced fact-checking, saying in an email, “The primary way we surface potentially false news to third-party fact-checkers is via machine learning.”
Predictive hiring algorithms are rife with bias.