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The 3 Principals of Building Anti-Bias AI

#artificialintelligence

In April of 2021, the U.S. Federal Trade Commission -- in its "Aiming for truth, fairness, and equity in your company's use of AI" report -- issued a clear warning to tech industry players employing artificial intelligence: "Hold yourself accountable, or be ready for the FTC to do it for you." Likewise, the European Commission has proposed new AI rules to protect citizens from AI-based discrimination. These warnings, and impending regulations, are warranted. Machine learning (ML), a common type of AI, mimics patterns, attitudes and behaviors that exist in our imperfect world, and as a result, it often codifies inherent biases and systemic racism. Unconscious biases are particularly difficult to overcome, because they, by definition, exist without human awareness.


Enabling Artificial Intelligence at the Combatant Commands

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The Department of Defense's Office of the Chief Information Officer, or DoD CIO, is pursuing several efforts to make sure the U.S. combatant commands have the fundamental tools to enable artificial intelligence and machine learning to aid their operational command and control. The DoD CIO's efforts naturally hinge on data and data management, an appropriate transport layer and future cloud capabilities, solutions that will benefit a broad range of warfighters not just at the commands, said Kelly Fletcher, who is performing the duties of the department's chief information officer on behalf of John Sherman, the nominated CIO who is currently going through his confirmation process for the position and testifying tomorrow in front of the U.S. Senate. A senior executive service official, Fletcher has been working in the office since 2020. She presented a keynote address during AFCEA International's TechNet Cyber conference in Baltimore on October 27. Fletcher emphasized that the DoD CIO's office supports more than 40 major combatant commands, services and agencies, "and they all have unique requirements," she said.


Army's 'Scarlet Dragon' uses AI with Navy, Air Force and Marine assets to rapidly find, ID and destroy targets

#artificialintelligence

The Army recently scanned 7,200 km across four states on the eastern seaboard and used artificial intelligence to find and destroy specific simulated targets in an area the size of a 10-square-foot box. It was all part of the Army's XVIII Airborne Corp artificial intelligence-enabled live-fire target identification exercise on Thursday that used nearly 20 platforms and units from each of the other branches. The event was the fourth of its kind for the Scarlet Dragon program, which began in 2020. The Corps, assisted by elements of the Navy, Air Force and Marine Corps, worked with various platforms in all domains. But a key ingredient was the National Geospatial-Intelligence Center, which provided satellite imagery for software to sift through and find targets.


Making machine learning more useful to high-stakes decision makers

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The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.


Making machine learning more useful to high-stakes decision makers

#artificialintelligence

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.


AI can predict cancer risk through mammograms

#artificialintelligence

As a hereditary disease, breast cancer has affected hundreds of families throughout the state. Annually, an average of 1,190 women are diagnosed with breast cancer in Hawaiʻi. As October approaches in recognition of National Breast Cancer Awareness Month, new public impact research from the University of Hawaiʻi Cancer Center is using artificial intelligence (AI) to improve risk assessment for breast cancer to aid in prevention and early detection, improving breast cancer outcomes for women all over the world. To reduce unnecessary imaging for breast cancer and costs associated with it, UH Cancer Center Researcher John Shepherd and his colleagues found that AI is able to distinguish between the mammograms of women who are more likely to develop breast cancer later on, and those who are not. The study was published in Radiology.


AI programming tool Copilot helps write up to 30% of code on GitHub

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Texas state Rep. Matt Krause (R), chair of the Texas House Committee on General Investigating, announced Wednesday that he's initiating a probe into schools' library books, according to a letter sent to the state's education agency and other superintendents. Why it matters: The probe focuses on books that discuss race, sexuality, or "make students feel discomfort, guilt, anguish, or any other form of psychological distress because of their race or sex," Krause wrote in the letter.


La veille de la cybersécurité

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LGTBQ people, particularly young LGTBQ people, are one of the world's most marginalized and vulnerable populations. And as a group, young members of the LGBTQ community are at a higher risk of suicide compared to their heterosexual, cisgender peers. In fact, LGBTQ youth are four times more likely to attempt suicide and it is estimated that 1.8 million LGBTQ youth in the US seriously consider taking their own lives each year. Reaching these young people to offer them support in times of crisis is not easy, but it's a challenge that has been tackled by nonprofit organization The Trevor Project since it was founded in 1998. The Trevor Project is the world's largest suicide prevention and crisis intervention organization for lesbian, gay, transgender, queer and questioning young people.


The awkward grant of patents to artificial intelligence

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As exciting as all this might seem, this decision seems to be more of an aberration than the rule. Before it was finally granted a patent in South Africa, the DABUS application had been rejected by patent offices in the US, Europe and the UK. The European Patent Office (EPO), justifying its decision to reject the patent application, pointed out that the law designates a natural person as the inventor of a work in order to preserve her moral right over the invention as well as to secure for her the economic rights made available by the patent. In order to be entitled to these benefits, an inventor needs to have actually "performed the creative act of invention". While artificial intelligence algorithms today are capable of perform complex computational functions that are often way beyond the capability of humans, the EPO pointed out that in all these instances, the programs are doing little more than just following the broad instructions of the humans who designed them.


Twilio shares off 5%: Q3 revenue tops expectations, delivers surprise profit, retention rate declines

ZDNet

Cloud communications firm Twilio this afternoon reported Q3 revenue that topped Wall Street's expectations, and a surprise profit, and an outlook for this quarter's revenue that was higher as well. The one apparent blemish in the quarter was that the company's Dollar-Based Net Expansion Rate was 131%. That was down from 137% in the prior-year period. The report sent Twilio shares down 11% in late trading. "We had a terrific Q3, and we're really exicted about our set-up for Q4," said CFO Khozema Shipchandler, in an interview with ZDNet via Zoom following the report.