Tuesday November 19, 2019
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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

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"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.

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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)

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This AI Tool Can Predict Mortality Of Heart Failure Patients

Researchers develop a tool that can predict mortality of heart failure patients

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This artificial intelligence (AI) tool can predict life expectancy in heart failure patients. Pixabay

Researchers have developed an artificial intelligence (AI) tool to predict life expectancy in heart failure patients.

The machine learning algorithm based on de-identified electronic health, records data of 5,822 hospitalised or ambulatory patients with heart failure at UC San Diego Health in the US.

“We wanted to develop a tool that predicted life expectancy in heart failure patients, there are apps where algorithms are finding out all kinds of things, like products you want to purchase,” said Avi Yagil, Professor at University of California.

“We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective,” Yagil added.

From this model, a risk score was derived that determined low and high risk of death by identifying eight readily available variables collected for the majority of patients with heart failure:Diastolic blood pressure, Creatinine, Blood urea nitrogen, White blood cell count, Platelets, Albumin and Red blood cell distribution.

Yagil said the newly developed model was able to accurately predict life expectancy 88 per cent of the time and performed substantially better than other popular published models.

“This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year,” said researcher Eric Adler.

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The mortality of a heart failure patient can be predicted. Pixabay

“This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball,” he added.

The tool was additionally tested using de-identified patient data from the University of California San Francisco and a data base derived from 11 European medical centers.

“It was successful in those cohorts as well,” said Yagil.

“Being able to repurpose our findings in independent populations is of utmost importance, thus validating our methodology and its results,” Yagil added.

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Researchers said the partnership between physicists and cardiologists was critical to developing a reliable tool and extensive knowledge and experiences from both sides proved synergetic.

The study was published in the European Journal of Heart Failure. (IANS)