that claimed to show how off-the-shelf artificial intelligence tools can detect who is gay simply by looking at a photo of a person’s face.It faced immediate backlash from from artificial intelligence researchers and sociologists as well as the advocacy organization GLAAD, who criticized the authors’ methodology and their grandiose conclusions.Tags: Data Centre Business PlanDaily Assignment SheetStanford Creative Writing Summer ProgramArt Of Problem Solving AlgebraWhat To Write In A Personal Statement For Graduate SchoolCustomer Case StudiesDissertations On Inclusive Education
The study shows how bias can creep into machine learning through the data that is used to train the models.
It also casts more doubt on the already-embattled scientific process of peer review.
From When shown one photo each of a gay and straight man, both chosen at random, the model distinguished between them correctly 81% of the time.
When shown five photos of each man, it attributed sexuality correctly 91% of the time.
Kosinski and Wang have been defensive on social media, saying their critics did not read the paper or want to ignore harsh truths.
The former is certainly not true of Cohen and Mattson, who pieced the paper apart, and as to the latter, Mattson pointed out that this seems to be the first time these researchers have taken an interest in gay rights. Your opinion would be stronger, have you read the paper and our notes: https://t.co/dm XFuk6LU6pic.twitter.com/0O0e2j ZWMn— Michal Kosinski (@michalkosinski) September 8, 2017There are broader implications at play.
Kosinski and Wang claimed that their findings provided “strong support” for the idea that sexual orientation is caused by hormone exposure in the womb, an unsubstantiated and unusual leap for scientists to make after an incremental study.
They also claimed to be doing the LGBTQ community a service by exposing how artificial intelligence could hypothetically be used to persecute gay people. Critics pointed out that the study included no people of color, which is common in machine learning studies but artificially increases the model’s ability to find patterns; the fact that the data relied in part on looking at what Facebook groups people liked in order to determine their sexual orientation; and the fact that the researchers seemed to think that the contours of a person’s face are fixed, rather than something easily and frequently manipulated by makeup. Cohen, a sociologist at the University of Maryland, wrote that the authors had simply misinterpreted their own results.
In the same way the lives and activities of those who were sexually active, or attracted to, members of the same sex, as well as the attitudes of others towards them may fairly be said to constitute a history of interest to modern lesbians, gays and bisexuals.
But what makes up "modern lesbian, gay and bisexual" [hereafter "LGB"] identity?