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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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AI-based Google Model Beats Humans in Detecting Breast Cancer

This work, said Google, is the latest strand of its research looking into detection and diagnosis of breast cancer, not just within the scope of radiology, but also pathology

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The Google name is displayed outside the company's office in London, Britain. VOA

In a ray of hope for those who have to go for breast cancer screening and even for healthy women who get false alarms during digital mammography, an Artificial Intelligence (AI)-based Google model has left radiologists behind in spotting breast cancer by just scanning the X-ray results.

Reading mammograms is a difficult task, even for experts, and can often result in both false positives and false negatives.

In turn, these inaccuracies can lead to delays in detection and treatment, unnecessary stress for patients and a higher workload for radiologists who are already in short supply, Google said in a blog post on Wednesday.

Google’s AI model spotted breast cancer in de-identified screening mammograms (where identifiable information has been removed) with greater accuracy, fewer false positives and fewer false negatives than experts.

“This sets the stage for future applications where the model could potentially support radiologists performing breast cancer screenings,” said Shravya Shetty, Technical Lead, Google Health.

Digital mammography or X-ray imaging of the breast, is the most common method to screen for breast cancer, with over 42 million exams performed each year in the US and the UK combined.

“But despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge,” said Daniel Tse, Product Manager, Google Health.

Together with colleagues at DeepMind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, Google set out to see if AI could support radiologists to spot the signs of breast cancer more accurately.

The findings, published in the journal Nature, showed that AI could improve the detection of breast cancer.

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

Google AI model was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the UK and more than 15,000 women in the US, to see if it could learn to spot signs of breast cancer in the scans.

The model was then evaluated on a separate de-identified data set of more than 25,000 women in the UK and over 3,000 women in the US.

“In this evaluation, our system produced a 5.7 per cent reduction of false positives in the US, and a 1.2 per cent reduction in the UK. It produced a 9.4 per cent reduction in false negatives in the US, and a 2.7 per cent reduction in the UK,” informed Google.

The researchers then trained the AI model only on the data from the women in the UK and then evaluated it on the data set from women in the US.

In this separate experiment, there was a 3.5 per cent reduction in false positives and an 8.1 per cent reduction in false negatives, “showing the model’s potential to generalize to new clinical settings while still performing at a higher level than experts”.

Notably, when making its decisions, the model received less information than human experts did.

The human experts (in line with routine practice) had access to patient histories and prior mammograms, while the model only processed the most recent anonymized mammogram with no extra information.

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Despite working from these X-ray images alone, the model surpassed individual experts in accurately identifying breast cancer.

This work, said Google, is the latest strand of its research looking into detection and diagnosis of breast cancer, not just within the scope of radiology, but also pathology.

“We’re looking forward to working with our partners in the coming years to translate our machine learning research into tools that benefit clinicians and patients,” said the tech giant. (IANS)