L3Harris to help DOD with artificial intelligence, machine learning

L3Harris Technologies will help the U.S. Department of Defense (DOD) develop artificial intelligence and machine learning (AI/ML) systems to help reduce the amount of time it takes to decipher usable intelligence from increasing amounts of data collected from space and airborne assets.

L3Harris will research, develop and demonstrate an AI/ML interface using data science techniques under a new multimillion-dollar contract to support DOD applications.

“L3Harris’ work will allow the DOD to turn massive volumes of data into actionable intelligence,” said Ed Zoiss, president, Space and Airborne Systems, L3Harris. “The abundance of data collected by space and airborne assets is only increasing. The findings of this research will directly address the data processing challenges within the DOD and intelligence community.”

Awarded by the U.S. Army Research Laboratory, the contract supports the DOD’s initiative to accelerate the integration of big data and AI/ML within the agency.

L3Harris will perform the work in Rochester, New York; Melbourne, Florida; and Herndon, Virginia.

Published at Mon, 26 Oct 2020 23:37:30 +0000

Ethical concerns mount as AI takes bigger decision-making role in more industries

AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment, said Sandel, who teaches a course in the moral, social, and political implications of new technologies.

“Debates about privacy safeguards and about how to overcome bias in algorithmic decision-making in sentencing, parole, and employment practices are by now familiar,” said Sandel, referring to conscious and unconscious prejudices of program developers and those built into datasets used to train the software. “But we’ve not yet wrapped our minds around the hardest question: Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?”

Panic over AI suddenly injecting bias into everyday life en masse is overstated, says Fuller. First, the business world and the workplace, rife with human decision-making, have always been riddled with “all sorts” of biases that prevent people from making deals or landing contracts and jobs.

When calibrated carefully and deployed thoughtfully, resume-screening software allows a wider pool of applicants to be considered than could be done otherwise, and should minimize the potential for favoritism that comes with human gatekeepers, Fuller said.

Sandel disagrees. “AI not only replicates human biases, it confers on these biases a kind of scientific credibility. It makes it seem that these predictions and judgments have an objective status,” he said.

In the world of lending, algorithm-driven decisions do have a potential “dark side,” Mills said. As machines learn from data sets they’re fed, chances are “pretty high” they may replicate many of the banking industry’s past failings that resulted in systematic disparate treatment of African Americans and other marginalized consumers.

“If we’re not thoughtful and careful, we’re going to end up with redlining again,” she said.

A highly regulated industry, banks are legally on the hook if the algorithms they use to evaluate loan applications end up inappropriately discriminating against classes of consumers, so those “at the top levels” in the field are “very focused” right now on this issue, said Mills, who closely studies the rapid changes in financial technology, or “fintech.”

“They really don’t want to discriminate. They want to get access to capital to the most creditworthy borrowers,” she said. “That’s good business for them, too.”

Oversight overwhelmed

Given its power and expected ubiquity, some argue that the use of AI should be tightly regulated. But there’s little consensus on how that should be done and who should make the rules.

Thus far, companies that develop or use AI systems largely self-police, relying on existing laws and market forces, like negative reactions from consumers and shareholders or the demands of highly-prized AI technical talent to keep them in line.

“There’s no businessperson on the planet at an enterprise of any size that isn’t concerned about this and trying to reflect on what’s going to be politically, legally, regulatorily, [or] ethically acceptable,” said Fuller.

Firms already consider their own potential liability from misuse before a product launch, but it’s not realistic to expect companies to anticipate and prevent every possible unintended consequence of their product, he said.

Few think the federal government is up to the job, or will ever be.

“The regulatory bodies are not equipped with the expertise in artificial intelligence to engage in [oversight] without some real focus and investment,” said Fuller, noting the rapid rate of technological change means even the most informed legislators can’t keep pace. Requiring every new product using AI to be prescreened for potential social harms is not only impractical, but would create a huge drag on innovation.

Published at Mon, 26 Oct 2020 22:30:00 +0000