Quantifying and Reducing Stereotypes in Word Embeddings · HackerPapers discussions

Quantifying and Reducing Stereotypes in Word Embeddings — HN submission

Sun, 18 Dec 2016 04:52:57 GMT

Quantifying and Reducing Stereotypes in Word Embeddings

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Quantifying and Reducing Stereotypes in Word Embeddings — mentioned by Eridrus

Wed, 07 Sep 2016 21:12:32 GMT

The biggest issues of bias/fairness in ML are not to do with the algorithms or results, but the underlying data. A trivial example would be: what if you trained a classifier to predict whether a person would be re-arrested before they went to trial? Some communities are policed more heavily so you would tend towards reinforcing the bias that exists and provide more ammunition to those arguing for further bias in the system, a feedback loop if you would. Or what if some protected group needs a higher down payment because the group is not well understood enough so that you can't distinguish…

Paper and discussion history. A mention is not an endorsement.

Quantifying and Reducing Stereotypes in Word Embeddings — HN submission

Mon, 01 Aug 2016 16:06:08 GMT

Quantifying and Reducing Stereotypes in Word Embeddings

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Quantifying and Reducing Stereotypes in Word Embeddings — mentioned by Smerity

Sun, 10 Jul 2016 06:04:48 GMT

The OpenAI team would be best to comment but I've not seen them note any specific intent[1] to research topics related to fairness, accountability and transparency in ML. I use those words specifically as FAT ML 2015 was part of ICML 2015[2]. While a great start, that area of research doesn't get anywhere near the attention it should given the gravity of the potential findings. These issues are already in existence. Given ML systems are being used to filter resumes, decide whether someone should get a loan (and how much their interest rate should be), and find matches on dating services, the…

Paper and discussion history. A mention is not an endorsement.