Collaborating Authors

Racial bias in widely used hospital algorithm, study finds


So the day after the study came out, actually, New York regulators, the Department of Financial Services and the Department of Health sent a letter to the company saying they were investigating this algorithm and that the company had to show that the way the algorithm worked wasn't in violation of anti-discrimination laws in New York. So that investigation is pending. One encouraging thing is that when the researchers did the study, they actually reached back to Optum and let them know about the discrepancy in the data. And the company was glad to be told about it. And I'm told that they're working on a fix.

Mutant Algorithms Are Coming for Your Education


Bad algorithms have been causing a lot of trouble lately. One, designed to supplant exam scores, blew the college prospects of untold numbers of students attending International Baccalaureate schools around the world. Then another did the same for even more students in lieu of the U.K.'s high-stakes "A-level" exams, prompting Prime Minister Boris Johnson to call it a "mutant" and ultimately use human-assigned grades instead. Actually, I would argue that pretty much all algorithms are mutants. People just haven't noticed yet.

How Do You Make an Algorithm Trustworthy?


Over the past 18 months, humanity has dutifully trooped into lockdown and back out again -- offering up a massive amount of personal data along the way. Remote work, Zoom schooling and contact-tracing became part of daily life; even today, in the name of public health, diners in Paris have their digital health passport scanned before opening the menu. At the same time, we've been bombarded with evidence of how the algorithms messing with our data can go wrong. Vaccine misinformation is shared at lightning speed on social networks. Germany's recent election was hit by fake-news campaigns. In England, students chanted "f*ck the algorithm" after one was used to grade test scores.

An algorithm that can spot cause and effect could supercharge medical AI


If the raw data is available, the pair claim, their algorithm can identify causal relations between variables as well as a clinical study could. Instead of looking for causes by running a fresh randomized controlled trial, the software may be able do this using existing data. Lee admits that people will need convincing and hopes that the algorithm will at least be used initially to complement trials, perhaps by highlighting potential causal links for study. Yet he notes that official bodies such as the US Food and Drug Administration already approve new drugs on the basis of trials that show correlation only. "The way in which drugs go through randomized controlled trials is less convincing than using these algorithms," he says.