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By 2023, 75% Firms to Hire AI Behaviour Forensic Experts

While the number of organisations hiring ML forensic and ethics investigators remains small today, that number will accelerate in the next five years

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This is the much talked about turf of Artificial Intelligence that now even handles the preliminary part of 'action' that was needed in response to a 'trigger'. Pixabay

By 2023, seventy five per cent of large organisations would hire artificial intelligence (AI) behaviour forensic, privacy and customer trust specialists to reduce brand and reputation risk, Gartner Inc. predicted on Thursday.

Bias based on race, gender, age or location, and bias based on a specific structure of data, have been long-standing risks in training AI models.

“New tools and skills are needed to help organisations identify these and other potential sources of bias, build more trust in using AI models, and reduce corporate brand and reputation risk. More and more data and analytics leaders and chief data officers (CDOs) are hiring ML (machine learning) forensic and ethics investigators,” Jim Hare, Research Vice President at Gartner, said in a statement.

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

Increasingly, sectors like finance and technology are deploying combinations of AI governance and risk management tools and techniques to manage reputation and security risks.

In addition, organisations such as Facebook, Google, Bank of America, MassMutual and NASA are hiring, or have already appointed, AI behaviour forensic specialists who primarily focus on uncovering undesired bias in AI models before they are deployed.

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“While the number of organisations hiring ML forensic and ethics investigators remains small today, that number will accelerate in the next five years,” added Hare. (IANS)

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Researchers Develop AI-enabled Tool to Detect Heart Attacks

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not

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A commonly used drug for treating osteoporosis or bone pain may also help reduce the risk of death by cardiovascular, heart attack and stroke, according to a study.
Heart Attack risk can be decreased by drug of osteoporosis. Pixabay

Researchers have developed an Artificial Intelligence-enabled tool which uses Machine Learning (ML) algorithms that will soon play a critical role in predicting heart attacks and other cardiac issues.

The Coronary Computed Tomography Arteriography (CCTA) gives highly detailed images of the heart vessels and is a promising tool for refining risk assessment, said researchers in the study published in the journal Radiology.

While earlier tools like the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasise on stenoses or blockages and narrowing in the coronary arteries, CCTA shows more than just stenoses.

“While CAD-RADS is an important and useful development in the management of cardiac patients, its focus on stenoses may leave out important information about the arteries,” said study lead author Kevin M. Johnson, Associate Professor at the Yale University.

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The study compared people aged 41-50 years and 40 or younger heart attack survivors and found that among patients who suffer a heart attack at a young age overall is 40 or younger. VOA

The ML algorithm is able to pull out patterns in the data and predict that patients with certain patterns are more likely to have an adverse event like a heart attack than patients with other patterns.

For the study, the research team compared the ML approach with CAD-RADS and other vessel scoring systems in nearly 7,000 patients. They followed the patients for an average of nine years after CCTA.

Also Read: Indians Seek Personalized Customer Experiences The Most, Says a New Study by Adobe

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not.

“The risk estimate that you get from doing the Machine Learning version of the model is more accurate than the risk estimate you’re going to get if you rely on CAD-RADS,” Johnson said. (IANS)