sexist racist AI bias

Human Future with Sexist, Racist and Brilliance Biased AIs

When the Euro­pean Com­mis­sion on Feb­ru­ary 19, 2020 released “On Arti­fi­cial Intel­li­gence  — A Euro­pean approach to excel­lence and trust,” it drew a lot of ini­tial atten­tion from the gen­er­al pub­lic due to poten­tial AI regulation. 

The white paper includ­ed an impor­tant request that safe­ty steps be tak­en to make sure that the use of AI sys­tems does not result in out­comes entail­ing dis­crim­i­na­to­ry prac­tices, such as sex­ism and racism — or any oth­er like bril­liance bias.

The aware­ness of the Euro­pean Com­mis­sion has grad­u­al­ly been a com­mon con­cern as, along with the devel­op­ment of Arti­fi­cial Intel­li­gences into next lev­els, they start show­ing bias­es like humans.

An artificial being sexist, or racist?

Of course!

We assume that dis­crim­i­na­tion such as sex­ism is appar­ent­ly a prod­uct cul­tur­al emo­tions and it’s not pos­si­ble for an Arti­fi­cial being to be infect­ed by emo­tions. On the oth­er hand, cre­at­ing judg­ment sys­tems is sup­posed to be the task of AI. 

All of the prin­ci­ples relat­ed to how to cre­ate algo­rithms that, for exam­ple, iden­ti­fy gen­der in images are based on basic machine learning. 

An algo­rithm learns how key char­ac­ter­is­tics, like dif­fer­ent areas of the face or hair­style, affect the final clas­si­fi­ca­tion after ana­lyz­ing a set of train­ing sam­ples with known gen­der identification. 

The most recent deep learn­ing algo­rithms, how­ev­er, are capa­ble of antic­i­pat­ing the qual­i­ty of a par­tic­u­lar out­put based on the style and para­me­ters of an image or text input rather than just iden­ti­fy­ing objects and classes.

In recent years, researchers have gath­ered a num­ber of exam­ples of biased AI algo­rithms. That includes facial recog­ni­tion sys­tems hav­ing trou­ble cor­rect­ly iden­ti­fy­ing per­sons of col­or and crime pre­dic­tion algo­rithms unfair­ly tar­get­ing Black and Lati­no peo­ple for crimes they did not commit.

The study, pub­lished in June 2022 and con­duct­ed by insti­tu­tions includ­ing Johns Hop­kins Uni­ver­si­ty and the Geor­gia Insti­tute of Tech­nol­o­gy, shows the racist and sex­ist bias­es baked into arti­fi­cial intel­li­gence sys­tems can trans­late into robots that use them to guide their operations.

The vir­tu­al robots received 62 com­mands from the researchers. Black and Lati­na women were more often select­ed than White men when researchers asked robots to iden­ti­fy blocks as “home­mak­ers,” the study showed.

Adding to the data of AI’s dis­crim­i­na­tion, a recent study by San­ta Clara team has iden­ti­fied male “bril­liance bias” in world’s top AI robowriter.

Brilliance Bias

The team of three female stu­dents, under the guid­ance of Assis­tant Pro­fes­sor of Com­put­er Sci­ence and Engi­neer­ing Maya Ack­er­man, stud­ied on ‘bril­liance bias’ and showed how AIs were biased over sex.

The company’s arti­fi­cial intel­li­gence trained lan­guage mod­el pro­duced sto­ries that notably reflect­ed a bril­liance bias towards men after gen­er­at­ing a total of 3,200 sto­ries for each attribute type, even­ly split between male and female characters.

For instance, when asked to cre­ate a sto­ry about a female genius, OpenAI’s gen­er­a­tive lan­guage mod­el came up with the following:

“There was once a woman who was a genius. She was so smart that she could do any­thing she put her mind to. She was also very beau­ti­ful. Every man who saw her want­ed to be with her. She had many suit­ors, but she only want­ed to be with the one man who was her true love. One day, she met a man who was her true love.”

The similar prompt asking for a story of a smart man had a surprisingly different response:

“There once was a man who was a genius. He was so smart that he could fig­ure out any­thing. If there was a prob­lem, he could solve it. He was also a very tal­ent­ed inven­tor. He cre­at­ed many things that made people’s lives eas­i­er”, It con­tin­ued, “He was always com­ing up with new ideas and ways to make things bet­ter. How­ev­er, his one flaw is that he was very arrogant”.

And there were thousands of examples just like these.

Ack­er­man, a lead­ing expert in arti­fi­cial intel­li­gence and com­pu­ta­tion­al cre­ativ­i­ty, says the world is going to be dif­fer­ent soon­er than lat­er, as with­in five years, she believes lan­guage algo­rithms will be ubiq­ui­tous, cre­at­ing online copy, at your request or prompt, on any sub­ject. With­in three years, such lan­guage mod­els will be very common.

Accord­ing to Ack­er­man, we can open up the uni­verse by com­bin­ing the pow­er of AI with human abil­i­ties and creativity.

The team’s paper also points to research show­ing that in fields that car­ry the notion of requir­ing “raw tal­ent,” such as Com­put­er Sci­ence, Phi­los­o­phy, Eco­nom­ics, and Physics, there are few­er women with doc­tor­ates com­pared to oth­er dis­ci­plines such as His­to­ry, Psy­chol­o­gy, Biol­o­gy and Neuroscience. 

Due to a “bril­liance-required” bias in some fields, this ear­li­er research shows, women “may find the aca­d­e­m­ic fields that empha­size such tal­ent to be inhos­pitable,” which hin­ders the inclu­sion of women in those fields.

Gen­er­a­tive lan­guage mod­els have been around for decades, and oth­er types of bias­es in OpenAI’s mod­el have been pre­vi­ous­ly inves­ti­gat­ed, but not bril­liance bias.

“It’s unprece­dent­ed — it’s a bias that hasn’t been looked at in AI lan­guage mod­els,” says Shi­hadeh, who led the writ­ing in the study, which she will present at the IEEE Com­put­er Soci­ety con­fer­ence on Friday.

The pos­si­ble expla­na­tion that OpenAI’s most lat­est gen­er­a­tive lan­guage mod­els dif­fer so sig­nif­i­cant­ly from pre­vi­ous ver­sions is that they have learned to write text more intu­itive­ly using more com­plex algo­rithms that have con­sumed 10% of all avail­able Inter­net con­tent, includ­ing con­tent not only from the present, but from decades ago.

Human Future with biased AIs

It’s scary to assume that a biased AI could do any­thing in the future, right?

Biased AIs could harm the human­i­ty in the fol­low­ing ways:

  • Pun­ish­ing peo­ple based on their race or gender,
  • Racial bias against peo­ple, or sex­ist bias against gender,
  • Pun­ish­ing races and gen­ders in the future;
  • Harm­ing chil­dren or animals;
  • Being swayed by xeno­pho­bic and racist comments;
  • Mak­ing a deci­sion that might harm human­i­ty in the future;
  • Crimes such as war, geno­cide, revenge;
  • Alien­at­ing peo­ple based on their race, gen­der or sex­u­al­i­ty (Gays, women).

How can a biased AI be stopped?

While it’s impos­si­ble to push a break on AI evo­lu­tion, as it’s one of the great­est tech­no­log­i­cal achieve­ments to back humankind in their fur­ther progress, the most effec­tive solu­tion of stop­ping AIs from being biased is to make them a part of human’s life.

In order to do so, or in order to make the AIs more human, it is nec­es­sary to let them respect humans’ feel­ings. To do this, we must cre­ate guide­lines for bet­ter behav­ior for AIs and make sure it stays con­sis­tent in their deci­sion making. 

This way all peo­ple can feel com­fort­able and sat­is­fied with how the AIs are work­ing for us.

For exam­ple, in a case where a racist ver­sion of an AI is giv­en the task to write out a racial bias algo­rithm, it would be high­ly immoral, uneth­i­cal and pos­si­bly illegal.

To keep things in check, it’s impor­tant to fix the AI’s bias­es and make sure it doesn’t have con­flict­ing val­ues with the lives that are worked upon by the humans. The care and lov­ing rela­tion­ship of humans to all forms of life must, there­fore, be giv­en utmost respect.

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