Thursday July 18, 2019
Home Lead Story Scientists De...

Scientists Develop AI Tool to Detect Racial, Gender Discrimination

"To avoid discrimination on the basis of race, gender or other attributes you need effective tools for detecting discrimination. Our tool can help with that," he said

0
//
AI
"We're beginning to see the first instances of artificial intelligence operating as a mediator between humans, but it's a question of: 'Do people want that?" Pixabay

Scientists have developed a new artificial intelligence (AI) tool for detecting unfair discrimination — such as on the basis of race or gender.

Preventing unfair treatment of individuals on the basis of race, gender or ethnicity, for example, has been a long-standing concern of civilised societies.

However, detecting such discrimination resulting from decisions, whether by human decision makers or automated AI systems, can be extremely challenging.

“Artificial intelligence systems — such as those involved in selecting candidates for a job or for admission to a university — are trained on large amounts of data,” said Vasant Honavar, a professor at Pennsylvania State University (Penn State) in the US.

“But if these data are biased, they can affect the recommendations of AI systems,” Honavar said.

He said if a company historically has never hired a woman for a particular type of job, then an AI system trained on this historical data will not recommend a woman for a new job.

“There’s nothing wrong with the machine learning algorithm itself,” said Honavar.

“It’s doing what it’s supposed to do, which is to identify good job candidates based on certain desirable characteristics. But since it was trained on historical, biased data it has the potential to make unfair recommendations,” he said.

The team created an AI tool for detecting discrimination with respect to a protected attribute, such as race or gender, by human decision makers or AI systems.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“We can minimise gender-based discrimination in salary if we ensure that similar men and women receive similar salaries,” said Aria Khademi, graduate student at Penn State.

The researchers tested their method using various types of available data, such as income data from the US Census Bureau to determine whether there is gender-based discrimination in salaries.

They also tested their method using the New York City Police Department’s stop-and-frisk programme data to determine whether there is discrimination against people of colour in arrests made after stops.

“We analysed an adult income data set containing salary, demographic and employment-related information for close to 50,000 individuals,” said Honavar.

“We found evidence of gender-based discrimination in salary. Specifically, we found that the odds of a woman having a salary greater than USD 50,000 per year is only one-third that for a man.

“This would suggest that employers should look for and correct, when appropriate, gender bias in salaries,” he said.

Also Read: Apple Releases Silent Update for Mac Users to Fix Faulty Video Conferencing App

Although the team’s analysis of the New York stop-and-frisk dataset — which contains demographic and other information about drivers stopped by the New York City police force — revealed evidence of possible racial bias against Hispanics and African American individuals, it found no evidence of discrimination against them on average as a group.

“You cannot correct for a problem if you don’t know that the problem exists,” said Honavar.

“To avoid discrimination on the basis of race, gender or other attributes you need effective tools for detecting discrimination. Our tool can help with that,” he said. (IANS)

Next Story

Researchers Develop AI Algorithm That can Solve Rubik’s Cube in Less Than a Second

According to the researchers, the ultimate goal of projects such as this one is to build the next generation of AI systems

0

Researchers have developed an AI algorithm that can solve a Rubiks Cube in a fraction of a second, faster than most humans. The work is a step toward making AI systems that can think, reason, plan and make decisions.

The study, published in the journal Nature Machine Intelligence, shows DeepCubeA — a deep reinforcement learning algorithm programmed by University of California computer scientists and mathematicians — can solve the Rubik’s Cube in a fraction of a second, without any specific domain knowledge or in-game coaching from humans.

This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state – each of six sides displaying a solid colour – which apparently can not be found through random moves.

“Artificial Intelligence can defeat the world’s best human chess and Go players, but some of the more difficult puzzles, such as the Rubik’s Cube, had not been solved by computers, so we thought they were open for AI approaches,” said study author Pierre Baldi, Professor at the University of California.

“The solution to the Rubik’s Cube involves more symbolic, mathematical and abstract thinking, so a deep learning machine that can crack such a puzzle is getting closer to becoming a system that can think, reason, plan and make decisions,” Baldi said.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

For the study, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 per cent of the time.

The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

The researchers were interested in understanding how and why the Artificial Intelligence (AI) made its moves and how long it took to perfect its method.

Also Read: Amazon Alexa May Come to Windows 10’s Lock Screen

“It learned on its own, our AI takes about 20 moves, most of the time solving it in the minimum number of steps,” Baldi said.

“Right there, you can see the strategy is different, so my best guess is that the AI’s form of reasoning is completely different from a human’s,” he added.

According to the researchers, the ultimate goal of projects such as this one is to build the next generation of AI systems. (IANS)