axbom’s Twitter Archive—№ 27,207

…in reply to @RealSaavedra
RealSaavedra Here we go, one of many examples: propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing AOC is 100% correct and there are many organizations across the world working on ways to mitigate bias in machine learning.
On twitter.com ♻️ 3 Retweets ❤️ 7 Favorites 2019 Jan 23 Mood -1 🙁

…in reply to @axbom
RealSaavedra AOC Algorithms are driven by computation, not by math. joanna-bryson.blogspot.com/2017/07/three-very-different-sources-of-bias-in.html
Permalink On twitter.com ❤️ 1 Favorite 2019 Jan 23 Mood 0

…in reply to @axbom
RealSaavedra AOC A video based on @mathbabedotorg's talk may help out here: vimeo.com/thersa/thetruthaboutalgorithms She wrote a book aptly named Weapons of Math Destruction.
Permalink On twitter.com ❤️ 3 Favorites 2019 Jan 23 Mood -1 🙁

…in reply to @axbom
RealSaavedra Hmmm, it seems even Amazon can't get it right... reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
Permalink On twitter.com ❤️ 2 Favorites 2019 Jan 23 Mood 0

…in reply to @axbom
RealSaavedra Here's one where Google apologized: theguardian.com/technology/2015/jul/01/google-sorry-racist-auto-tag-photo-app
Permalink On twitter.com ❤️ 3 Favorites 2019 Jan 23 Mood -1 🙁

…in reply to @axbom
RealSaavedra Google says: "Machine learning models are not inherently objective. Engineers train models by feeding them data sets of training examples, and human involvement in the provision and curation of this data can make a model's predictions susceptible to bias." developers.google.com/machine-learning/crash-course/fairness/types-of-bias
Permalink On twitter.com ❤️ 3 Favorites 2019 Jan 23 Mood -1 🙁