healthcarereimagined

By Jory Heckman

December 21, 2023 6:26 pm

The Department of Veterans Affairs is looking at facial recognition technology for its frontline medical workers to log into their workstations more quickly, and spend more time treating veterans.

VA Chief Information Security Officer Lynette Sherrill said the department plans on piloting facial recognition tools next year at VA hospitals, particularly for frontline clinicians working in intensive care units.

The pilot, if successful, would give VA health care employees an alternative to using their Personal Identity Verification (PIV) cards to securely log onto the department’s network.

“I’ve got nurses and clinicians trying to care for veterans. And they’ve got to reach for a PIV card … and plug it into a workstation to log in, while they’re trying to give a veteran a shot or give a patient an exam. So if I can make that a more frictionless authentication experience for them, I feel like that’s my job to help them,” Sherrill said Nov. 7 at the Rise8’s Prodacity summit in Washington, D.C.

Sherrill told Federal News Network on the sidelines of the event that the VA plans to run facial recognition pilots throughout 2024 with clinical staff.

“Much like we use facial recognition to log into an iPhone today, that’s that type of experience we want to give VA clinical staff,” she said in an interview.

Sherrill said there’s already a high rate of PIV card utilization among the VA workforce. About 95% of employees, she added, are using their PIV cards to log onto the VA network.

But even with those metrics, that means roughly 30,000 VA employees are logging on only with a username and password.

Sherrill said the facial recognition pilot is focused on providing a “more frictionless authentication process” for VA clinical staff.

“The technology is finally there, where we can utilize the technology to provide a better experience for our end users,” she said.

Carrie Lee, VA’s deputy chief information officer for product engineering service, told Federal News Network that she’s heading up the department’s new identity, credential and access management (ICAM) modernization efforts.

“We’re looking at our single sign-on experiences for both internal and external users, making sure that we are using multifactor [authentication], and making it compliant, making it an easy experience for the users,” Lee said.

For external users,  Lee said VA is using Login.gov and transitioning off of legacy credentials that only require a username and password, and may not be as secure.

The VA is also setting a new standard for cybersecurity across its networks.

VA is shifting some of its systems to a continuous Authority to Operate (ATO). It’s a trend that’s already happening across the Defense Department.

The idea is VA will keep checking in to make sure its systems uphold cybersecurity requirements, rather than just checking off that those standards are met once before their launch.

“We have quarterly reviews that review the security posture of every application within that continuous ATO, where I can click down and see how many risks were mitigated, how many vulnerabilities did we keep from being released into production,” Lee said.  “I can go in at any point, and understand what’s happening in that environment for multiple applications.”

Sherrill said the continuous ATO marks a step toward the VA having an “automated enterprise risk view” across its network.

“With the ever-changing threat landscape that we’re dealing with in cybersecurity today, one of the things that is very hard to keep up with is how is the risk posture of all of our systems changing,” she said.

Sherrill added that the VA, with its new automated cybersecurity tools, will be able to respond more quickly to zero-day vulnerabilities and other emerging threats.

“What this integration and automation is going to [is] … we’d know immediately, ‘These are our six most critical systems impacted by this zero-day vulnerability.’ And we’d be able to focus our resources on those systems, to make sure that we could maintain a risk posture that’s acceptable to the organization,” she said.

Lee said the continuous ATO will also allow VA’s IT workforce to develop code and software more quickly, and spend less time on manual cybersecurity compliance work.

“It also frees up a lot of people from manually entering into our [governance, risk and compliance] systems the compliance information, which can take a lot of resources, to be able to focus on higher value valuable tasks, such as actually developing systems,” Lee said.

Lee said the VA has more than 1,000 systems with an ATO. Of those, she said she’s the authorizing official for about 400 of them. She said she spends about an hour each week authorizing systems.

“VA is an extremely complex organization. We’re probably the largest IT infrastructure of any civilian federal agency,” Lee said. “I really need to understand the security of the system I’m looking at the time I look at it. So, the assurance of having those automated controls in place, and understanding that technical risk posture, instead of just the compliance is very important to me, from an authorizing official perspective.”

Lee said the VA has reduced the ATO process from 400 days to about 60 days for new products coming into the environment.

VA’s Office of Information and Technology (VA OIT) is also taking steps to make sure its employees are incorporating cybersecurity into the foundation of everything in development.

Sherrill said no VA OIT development team is allowed to publish “any critical or high vulnerabilities in code.”

“We understand very uniformly that you can no longer produce a quality system if it’s not secure. And that’s our mantra at VA now — if it’s not secure, it’s not quality code, it’s not a quality product, so you’ve got to go back to the drawing board,” she said.

The VA is also focused on bringing in the next generation of cyber workers.

“We’ve got to use nontraditional hiring methods and nontraditional people and get them interested in cybersecurity,” Sherrill said. “We’ve got cybersecurity people leaving the cybersecurity industry because of burnout. We have to stop doing that. We’ve got to figure out how do we fill that pipeline back.”

Sherrill told Federal News Network that the VA is looking at ways to partner with the Defense Department’s SkillBridge program, which places transitioning service members into civilian careers.

“If I can bring in transitioning service members who already have cyber skills, and they’re transitioning out of the service, and I can give them a soft place to transition to and the VA and then give them two [or] three years of training, and they launch into industry — that’s a win,” she said. “But if they choose to stay in VA, and they get passionate about the mission, like most of us are in serving veterans, that’s a win as well.”

Sherrill said the VA also sees potential in reaching out to military spouses to consider careers in cybersecurity.

“They have an aptitude that they uniquely bring into the field, and I think that’s an untapped resource for us. We’ve got to look at these non-traditional places to really bring resources into the cyber pool,” she said.

The VA this summer rolled out a Special Salary Rate for its IT and cybersecurity employees, resulting in an average 17% pay raise. The SSR is meant to narrow the gap between what the government and private sector can afford to pay in-demand tech experts.

“We’re bringing in people and being able to pay them, not at the exact level they would be making in the industry, but close to that level. And between that, the benefits we offer and our amazing mission, I think we’ve been able to get the best talent,” Lee said.

Article link: https://federalnewsnetwork.com/it-modernization/2023/12/va-plans-facial-recognition-pilot-for-health-care-employees-to-reduce-log-in-burden/

FORT MEADE, Md.–The National Security Agency (NSA) published its 2023 Cybersecurity Year in Review today to share its recent cybersecurity successes and how it is working with partners to deliver on cybersecurity advances that enhance national security. This year’s report highlights NSA’s work with U.S government partners, foreign partners, and the Defense Industrial Base.

“The combined talent of our partners is the greatest competitive advantage we have to confront the increasingly sophisticated threats we see today”- Rob Joyce, Director of Cybersecurity

The Cybersecurity Year in Review highlights NSA’s recent cybersecurity efforts, including:

• Establishing the Artificial Intelligence (AI) Security Center.
• Detecting stealthy People’s Republic of China (PRC) intrusions into U.S. critical infrastructure and joined forces with partners (CISA, FBI, NIST, etc.) to expose those intrusions.
• Collaborating with industry, government stakeholders, and academia to modernize cryptography to scale cybersecurity solutions and address the quantum threat.

“Cybersecurity matters. It matters to our partners and it matters to us. It ensures that our information, our intelligence, our knowledge can be shared securely.”- General Paul M. Nakasone, U.S Army; Commander, U.S Cyber Command; Director, National Security Agency; Chief, Central Security Service

This report includes information about NSA’s cybersecurity partnerships and the efforts in building them. This year NSA:

1. Inaugurated the new AI Security Center within the Cybersecurity Collaboration Center, which will promote the secure development, integration, and adoption of AI capabilities within National Security Systems (NSS) and the Defense Industrial Base (DIB).
2. Scaled NSA’s cybersecurity impact against global threats like Russian cyberespionage malware and malicious cyber activity from the People’s Republic of China together with U.S. and international partners and collaborators.
3. Increased enrollments in NSA’s no cost cybersecurity services to Department of Defense contractors by 400%, hardening infrastructure and strengthening the Defense Industrial Base.

For questions or feedback on the report, contact Cybersecurity@nsa.gov or CybersecurityReports@nsa.gov. For any media inquiries, contact MediaRelations@nsa.gov.

Read the 2023 NSA Cybersecurity Year in Review to learn more.

Article link: https://www.nsa.gov/Press-Room/Press-Releases-Statements/Press-Release-View/Article/3621654/nsa-publishes-2023-cybersecurity-year-in-review/

Dr. Valerie Seabaugh, FEHRM Deputy Chief Health Informatics Officer

The Federal Electronic Health Record Modernization (FEHRM) office drives the delivery and optimization of the shared electronic health record (EHR) of the Department of Defense, Department of Veterans Affairs (VA), Department of Homeland Security’s U.S. Coast Guard and Department of Commerce’s National Oceanic and Atmospheric Administration. One of the obvious benefits of a unified health record is the portability of records. We will no longer ask Veterans with disabilities and mobility challenges to haul boxes of paper records with them to appointments.

However, a less discussed but highly beneficial outcome of having a single, common federal EHR is lowering barriers to specialized telemedicine care for federal health care beneficiaries. In the old systems, patient health records had medical information isolated in paper files or in a single instance of an outdated EHR linked to a local clinic or medical facility. Although the Joint Longitudinal Viewer provided information between agencies, specialists’ access to the siloed record to place orders or document care was a major barrier to coordinating subspecialty telemedicine care for patients. If a beneficiary resided in an area without access to a needed subspecialist, the only way to reliably receive care was usually to travel to a referral medical facility.

With a single record, specialists have all the pertinent patient data at their fingertips regardless of patient location. Patients can receive telemedicine consultation from a subspecialist, many times without leaving their homes. Telehealth appointments using video technology lower barriers for patients to receive specialized care and drive federal health care toward an equitable standard of care regardless of a patient’s geographic location or mobility. For example, watch this Voice of a Veteran video to see how a Veteran in rural Arizona benefits from specialized endocrinology care with VA’s Tele-Diabetes Program.

Maintaining the federal EHR as an accurate and complete warehouse of medical information simplifies providing efficient, coordinated and convenient high-quality care. The FEHRM is at the forefront of lowering barriers to patient care by making it possible for today’s recruits to maintain a coherent lifetime record as they transition through their military career, from basic training all the way to becoming a Veteran beneficiary. The federal EHR drives health care toward an equitable future where patients receive the right care, unbounded from location and driven by choice.

Article link; https://www.linkedin.com/pulse/one-record-levels-playing-field-patients-needing-specialized-care-ybkne

Joint Commission President Mark R. Chassin, M.D., FACP, M.P.P., M.P.H., discusses The Joint Commission’s efforts to accelerate high reliability.

https://www.jointcommission.org/resources/news-and-multimedia/video-resources/health-care-and-high-reliability-a-cautionary-tale/

Jan 8, 2024

The Enterprise Requirements Adjudication process identified additional capabilities to deploy the federal EHR at Lovell FHCC. Building off the DoD Healthcare Management System Modernization Program Management Office deployment methodology, processes and content provided a baseline of tested processes and materials to deploy at Lovell FHCC. VA methodologies were added to accomplish deployment activities related to VA-specific training, infrastructure and device installation and VA-augmentation of system adoption by VA users. This kind of resource and expertise sharing is one of the major advantages of the shared federal EHR, given that training for all roles happens in the same system and all devices and network infrastructure support the same federal EHR.

Currently, VA is solely focused on Lovell FHCC’s EHR deployment with DOD. So far, DOD has deployed to more than 135 parent military treatment facilities, encompassing 3,000+ physical locations and 185,000+ users. USCG, U.S. Military Entrance Processing Command and NOAA have also deployed the federal EHR.

Article link: https://www.linkedin.com/pulse/dod-va-continue-making-progress-sharing-lovell-fhcc-deployment-responsibilities-vgqlf

The US government aims to push microelectronics research forward. But maintaining competitiveness in the long term will require embracing uncertainty.

By Brady Helwig & PJ Maykish

December 28, 2023

In its final weeks, the Obama administration released a report that rippled through the federal science and technology community. Titled Ensuring Long-Term US Leadership in Semiconductors, it warned that as conventional ways of building chips brushed up against the laws of physics, the United States was at risk of losing its edge in the chip industry. Five and a half years later, in 2022, Congress and the White House collaborated to address that possibility by passing the CHIPS and Science Act—a bold venture patterned after the Manhattan Project, the Apollo program, and the Human Genome Project. Over the course of three administrations, the US government has begun to organize itself for the next era of computing.

Secretary of Commerce Gina Raimondo has gone so far as to directly compare the passage of CHIPS to President John F. Kennedy’s 1961 call to land a man on the moon. In doing so, she was evoking a US tradition of organizing the national innovation ecosystem to meet an audacious technological objective—one that the private sector alone could not reach. Before JFK’s announcement, there were organizational challenges and disagreement over the best path forward to ensure national competitiveness in space. Such is the pattern of technological ambitions left to their own timelines.

Setting national policy for technological development involves making trade-offs and grappling with unknown future issues. How does a government account for technological uncertainty? What will the nature of its interaction with the private sector be? And does it make more sense to focus on boosting competitiveness in the near term or to place big bets on potential breakthroughs? 

The CHIPS and Science Act designated $39 billion for bringing chip factories, or “fabs,” and their key suppliers back to the United States, with an additional $11 billion committed to microelectronics R&D. At the center of the R&D program would be the National Semiconductor Technology Center, or NSTC—envisioned as a national “center of excellence” that would bring the best of the innovation ecosystem together to invent the next generation of microelectronics.  

In the year and a half since, CHIPS programs and offices have been stood up, and chip fabrication facilities in Arizona, Texas, and Ohio have broken ground. But it is the CHIPS R&D program that has an opportunity to shape the future of the field. Ultimately, there is a choice to make in terms of national R&D goals: the US can adopt a conservative strategy that aims to preserve its lead for the next five years, or it can orient itself toward genuine computing moonshots. The way the NSTC is organized, and the technology programs it chooses to pursue, will determine whether the United States plays it safe or goes “all in.” 

Welcome to the day of reckoning

In 1965, the late Intel founder Gordon Moore famously predictedthat the path forward for computing involved cramming more transistors, or tiny switches, onto flat silicon wafers. Extrapolating from the birth of the integrated circuit seven years earlier, Moore forecast that transistor count would double regularly while the cost per transistor fell. But Moore was not merely making a prediction. He was also prescribing a technological strategy (sometimes called “transistor scaling”): shrink transistors and pack them closer and closer together, and chips become faster and cheaper. This approach not only led to the rise of a $600 billion semiconductor industry but ushered the world into the digital age. 

Ever insightful, Moore did not expect that transistor scaling would last forever. He referred to the point when this miniaturization process would reach its physical limits as the “day of reckoning.” The chip industry is now very close to reaching that day, if it is not there already. Costs are skyrocketing and technical challenges are mounting. Industry road maps suggest that we may have only about 10 to 15 years before transistor scaling reaches its physical limits—and it may stop being profitable even before that. 

To keep chips advancing in the near term, the semiconductor industry has adopted a two-part strategy. On the one hand, it is building “accelerator” chips tailored for specific applications (such as AI inference and training) to speed computation. On the other, firms are building hardware from smaller functional components—called “chiplets”—to reduce costs and improve customizability. These chiplets can be arranged side by side or stacked on top of one another. The 3D approach could be an especially powerful means of improving speeds. 

This two-part strategy will help over the next 10 years or so, but it has long-term limits. For one thing, it continues to rely on the same transistor-building method that is currently reaching the end of the line. And even with 3D integration, we will continue to grapple with energy-hungry communication bottlenecks. It is unclear how long this approach will enable chipmakers to produce cheaper and more capable computers.  

Building an institutional home for moonshots

The clear alternative is to develop alternatives to conventional computing. There is no shortage of candidates, including quantum computing; neuromorphic computing, which mimics the operation of the brain in hardware; and reversible computing, which has the potential to push the energy efficiency of computing to its physical limits. And there are plenty of novel materials and devices that could be used to build future computers, such as silicon photonics, magnetic materials,and superconductor electronics. These possibilities could even be combined to form hybrid computing systems.

None of these potential technologies are new: researchers have been working on them for many years, and quantum computing is certainly making progress in the private sector. But only Washington brings the convening power and R&D dollars to help these novel systems achieve scale. Traditionally, breakthroughs in microelectronics have emerged piecemeal, but realizing new approaches to computation requires building an entirely new computing “stack”—from the hardware level up to the algorithms and software. This requires an approach that can rally the entire innovation ecosystem around clear objectives to tackle multiple technical problems in tandem and provide the kind of support needed to “de-risk” otherwise risky ventures.

Does it make more sense to focus on boosting competitiveness in the near term or to place big bets on potential breakthroughs?

The NSTC can drive these efforts. To be successful, it would do well to follow DARPA’s lead by focusing on moonshot programs. Its research program will need to be insulated from outside pressures. It also needs to foster visionaries, including program managers from industry and academia, and back them with a large in-house technical staff. 

The center’s investment fund also needs to be thoughtfully managed, drawing on best practices from existing blue-chip deep-tech investment funds, such as ensuring transparency through due-diligence practices and offering entrepreneurs access to tools, facilities, and training. 

It is still early days for the NSTC: the road to success may be long and winding. But this is a crucial moment for US leadership in computing and microelectronics. As we chart the path forward for the NSTC and other R&D priorities, we’ll need to think critically about what kinds of institutions we’ll need to get us there. We may not get another chance to get it right.

Brady Helwig is an associate director for economy and PJ Maykish is a senior advisor at the Special Competitive Studies Project, a private foundation focused on making recommendations to strengthen long-term US competitiveness.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2023/12/28/1084686/computing-microelectronics-chips-act/amp/

Companies say they want ethical AI. But those working in the field say that ambition comes at their expense.

By Melissa Heikkilä

October 28, 2022

Margaret Mitchell had been working at Google for two years before she realized she needed a break.

“I started having regular breakdowns,” says Mitchell, who founded and co-led the company’s Ethical AI team. “That was not something that I had ever experienced before.”

Only after she spoke with a therapist did she understand the problem: she was burnt out. She ended up taking medical leave because of stress. 

Mitchell, who now works as an AI researcher and chief ethics scientist at the AI startup Hugging Face, is far from alone in her experience. Burnout is becoming increasingly common in responsible-AI teams, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI consultant at Boston Consulting Group. 

Companies are under increasing pressure from regulators and activists to ensure that their AI products are developed in a way that mitigates any potential harms before they are released. In response, they have invested in teams that evaluate how our lives, societies, and political systems are affected by the way these systems are designed, developed, and deployed. 

Tech companies such as Meta have been forced by courts to offer compensation and extra mental-health support for employees such as content moderators, who often have to sift through graphic and violent content that can be traumatizing. 

But teams who work on responsible AI are often left to fend for themselves, employees told MIT Technology Review, even though the work can be just as psychologically draining as content moderation. Ultimately, this can leave people in these teams feeling undervalued, which can affect their mental health and lead to burnout.

Rumman Chowdhury, who leads Twitter’s Machine Learning Ethics, Transparency, and Accountability team and is another pioneer in applied AI ethics, faced that problem in a previous role. 

“I burned out really hard at one point. And [the situation] just kind of felt hopeless,” she says. 

All the practitioners MIT Technology Review interviewed spoke enthusiastically about their work: it is fueled by passion, a sense of urgency, and the satisfaction of building solutions for real problems. But that sense of mission can be overwhelming without the right support.

“It almost feels like you can’t take a break,” Chowdhury says. “There is a swath of people who work in tech companies whose job it is to protect people on the platform. And there is this feeling like if I take a vacation, or if I am not paying attention 24/7, something really bad is going to happen.”  

Mitchell continues to work in AI ethics, she says, “because there’s such a need for it, and it’s so clear, and so few people see it who are actually in machine learning.” 

But there are plenty of challenges. Organizations place huge pressure on individuals to fix big, systemic problems without proper support, while they often face a near-constant barrage of aggressive criticism online. 

Cognitive dissonance

The role of an AI ethicist or someone in a responsible-AI team varies widely, ranging from analyzing the societal effects of AI systems to developing responsible strategies and policies to fixing technical issues. Typically, these workers are also tasked with coming up with ways to mitigate AI harms, from algorithms that spread hate speech to systems that allocatethings like housing and benefits in a discriminatory way to the spread of graphic and violent images and language. 

Trying to fix deeply ingrained issues such as racism, sexism, and discrimination in AI systems might, for example, involve analyzing large data sets that include extremely toxic content, such as rape scenes and racial slurs.

AI systems often reflect and exacerbate the worst problems in our societies, such as racism and sexism. The problematic technologies range from facial recognition systems that classify Black people as gorillas to deepfake software used to make porn videos appearing to feature women who have not consented. Dealing with these issues can be especially taxing to women, people of color, and other marginalized groups, who tend to gravitate toward AI ethics jobs. 

And while burnout is not unique to people working in responsible AI, all the experts MIT Technology Review spoke to said they face particularly tricky challenges in that area. 

“You are working on a thing that you’re very personally harmed by day to day,” Mitchell says. “It makes the reality of discrimination even worse because you can’t ignore it.”

But despite growing mainstream awareness about the risks AI poses, ethicists still find themselves fighting to be recognized by colleagues in the AI field. 

Some even disparage the work of AI ethicists. Stability AI’s CEO, Emad Mostaque, whose startup built the open-source text-to-image AI Stable Diffusion, said in a tweet that ethics debates around his technology are “paternalistic.” Neither Mostaque nor Stability AI replied to MIT Technology Review’s request for comment by the time of publishing.

“People working in the AI field are mostly engineers. They’re not really open to humanities,” says Emmanuel Goffi, an AI ethicist and founder of the Global AI Ethics Institute, a think tank. 

Companies want a quick technical fix, Goffi says; they want someone to “explain to them how to be ethical through a PowerPoint with three slides and four bullet points.” Ethical thinking needs to go deeper, and it should be applied to how the whole organization functions, Goffi adds.  

“Psychologically, the most difficult part is that you have to make compromises every day—every minute—between what you believe in and what you have to do,” he says. 

The attitude of tech companies generally, and machine-learning teams in particular, compounds the problem, Mitchell says. “Not only do you have to work on these hard problems; you have to prove that they’re worth working on. So it’s completely the opposite of support. It’s pushback.” 

Chowdhury adds, “There are people who think ethics is a worthless field and that we’re negative about the progress [of AI].” 

Social media also makes it easy for critics to pile on researchers. Chowdhury says there’s no point in engaging with people who don’t value what they do, “but it’s hard not to if you’re getting tagged or specifically attacked, or your work is being brought up.”

Breakneck speed

The rapid pace of artificial-intelligence research doesn’t help either. New breakthroughs come thick and fast. In the past year alone, tech companies have unveiled AI systems that generate images from text, only to announce—just weeks later—even more impressive AI software that can create videos from text alone too. That’s impressive progress, but the harms potentially associated with each new breakthrough can pose a relentless challenge. Text-to-image AI could violate copyrights, and it might be trained on data sets full of toxic material, leading to unsafe outcomes. 

“Chasing whatever’s really trendy, the hot-button issue on Twitter, is exhausting,” Chowdhury says. Ethicists can’t be experts on the myriad different problems that every single new breakthrough poses, she says, yet she still feels she has to keep up with every twist and turn of the AI information cycle for fear of missing something important. 

Chowdhury says that working as part of a well-resourced team at Twitter has helped, reassuring her that she does not have to bear the burden alone. “I know that I can go away for a week and things won’t fall apart, because I’m not the only person doing it,” she says. 

But Chowdhury works at a big tech company with the funds and desire to hire an entire team to work on responsible AI. Not everyone is as lucky. 

People at smaller AI startups face a lot of pressure from venture capital investors to grow the business, and the checks that you’re written from contracts with investors often don’t reflect the extra work that is required to build responsible tech, says Vivek Katial, a data scientist at Multitudes, an Australian startup working on ethical data analytics.

The tech sector should demand more from venture capitalists to “recognize the fact that they need to pay more for technology that’s going to be more responsible,” Katial says. 

The trouble is, many companies can’t even see that they have a problem to begin with, according to a report released by MIT Sloan Management Review and Boston Consulting Group this year. AI was a top strategic priority for 42% of the report’s respondents, but only 19% said their organization had implemented a responsible-AI program. 

Some may believe they’re giving thought to mitigating AI’s risks, but they simply aren’t hiring the right people into the right roles and then giving them the resources they need to put responsible AI into practice, says Gupta.

“That’s where people start to experience frustration and experience burnout,” he adds. 

Growing demand

Before long, companies may not have much choice about whether they back up their words on ethical AI with action, because regulators are starting to introduce AI-specific laws. 

The EU’s upcoming AI Act and AI liability law will require companies to document how they are mitigating harms. In the US, lawmakers in New York, California, and elsewhere are working on regulation for the use of AI in high-risk sectors such as employment. In early October, the White House unveiled the AI Bill of Rights, which lays out five rights Americans should have when it comes to automated systems. The bill is likely to spur federal agencies to increase their scrutiny of AI systems and companies. 

And while the volatile global economy has led many tech companies to freeze hiring and threaten major layoffs, responsible-AI teams have arguably never been more important, because rolling out unsafe or illegal AI systems could expose the company to huge fines or requirements to delete their algorithms. For example, last spring the US Federal Trade Commission forced Weight Watchers to delete its algorithms after the company was found to have illegally collected data on children. Developing AI models and collecting databases are significant investments for companies, and being forced by a regulator to completely delete them is a big blow. 

Burnout and a persistent sense of being undervalued could lead people to leave the field entirely, which could harm the field of AI governance and ethics research as a whole. It’s especially risky given that those with the most experience in solving and addressing harms caused by an organization’s AI may be the most exhausted. 

“The loss of just one person has massive ramifications across entire organizations,” Mitchell says, because the expertise someone has accumulated is extremely hard to replace. In late 2020, Google sacked its ethical AI co-lead Timnit Gebru, and it fired Mitchell a few months later. Several other members of its responsible-AI team left in the space of just a few months.

Gupta says this kind of brain drain poses a “severe risk” to progress in AI ethics and makes it harder for companies to adhere to their programs. 

Last year, Google announced it was doubling its research staff devoted to AI ethics, but it has not commented on its progress since. The company told MIT Technology Review it offers training on mental-health resilience, has a peer-to-peer mental-health support initiative, and gives employees access to digital tools to help with mindfulness. It can also connect them with mental-health providers virtually. It did not respond to questions about Mitchell’s time at the company.

Meta said it has invested in benefits like a program that gives employees and their families access to 25 free therapy sessions each year. And Twitter said it offers employee counseling and coaching sessions and burnout prevention training. The company also has a peer-support program focused on mental health. None of the companies said they offered support tailored specifically for AI ethics.

As the demand for AI compliance and risk management grows, tech executives need to ensure that they’re investing enough in responsible-AI programs, says Gupta. 

Change starts from the very top. “Executives need to speak with their dollars, their time, their resources, that they’re allocating to this,” he says. Otherwise, people working on ethical AI “are set up for failure.” 

Successful responsible-AI teams need enough tools, resources, and people to work on problems, but they also need agency, connections across the organization, and the power to enact the changes they’re being asked to make, Gupta adds.

A lot of mental-health resources at tech companies center on time management and work-life balance, but more support is needed for people who work on emotionally and psychologically jarring topics, Chowdhury says. Mental-health resources specifically for people working on responsible tech would also help, she adds. 

“There hasn’t been a recognition of the effects of working on this kind of thing, and definitely no support or encouragement for detaching yourself from it,” Mitchell says.

“The only mechanism that big tech companies have to handle the reality of this is to ignore the reality of it.”

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2022/10/28/1062332/responsible-ai-has-a-burnout-problem/amp/

Watch Hinton speak with Will Douglas Heaven, MIT Technology Review’s senior editor for AI, at EmTech Digital.

By The Editorsarchive page

May 3, 2023

Deep learning pioneer Geoffrey Hinton announced on Monday that he was stepping down from his role as a Google AI researcher after a decade with the company. He says he wants to speak freely as he grows increasingly worried about the potential harms of artificial intelligence. Prior to the announcement, Will Douglas Heaven, MIT Technology Review’s senior editor for AI, interviewed Hinton about his concerns—read the full story here. 

Soon after, the two spoke at EmTech Digital, MIT Technology Review’s signature AI event. “I think it’s quite conceivable that humanity is just a passing phase in the evolution of intelligence,” Hinton said. You can watch their full conversation below.

https://www.technologyreview.com/2023/05/03/1072589/video-geoffrey-hinton-google-ai-risk-ethics/

In small business and socioeconomic set-aside solicitations, the North American Industry Classification System code is like a burly, tattooed bouncer standing beside a velvet rope, deciding who’s cool enough to come inside. (For some reason, my attempts to demonstrate my coolness by discussing the FAR always seem to fall flat). The agency’s NAICS code selection is critical because the NAICS code determines the applicable size standard–and by extension, the competitive playing field.

Steven Koprince | Dec 28, 2023

A mostly-overlooked decision by the U.S. Court of Federal Claims earlier this year emphasizes that when a Contracting Officer selects a NAICS code, “good enough for government work” doesn’t cut it. Instead, a Contracting Officer must pick the single best NAICS code for the work, using a process outlined in the SBA’s regulations.

The court’s decision in Consolidated Safety Services, Inc. v. United States, No. 23-521C (2023) involved a NOAA solicitation issued as a small business set-aside. The NOAA Contracting Officer assigned NAICS code 541620 (Environmental Consulting Services) with a corresponding $19 million size standard.

Consolidated Safety Services, Inc., which did not qualify as a small business under the $19 million standard, challenged the NAICS code. CSS argued that the appropriate code was NAICS code 541715 (Research and Development in the Physical, Engineering and Life Sciences) with a corresponding 1,000-employee size standard, under which CSS qualified as a small business.

(Clearly, the competitive playing field is completely different under a $19 million size standard than under a 1,000 employee size standard. The six little digits comprising a NAICS code are powerful–which is why I’m surprised not to see contractors filing many more NAICS code appeals).

CSS filed its NAICS code appeal with the SBA’s Office of Hearings and Appeals. CSS argued that the services being solicited by NOAA “predominantely entail performing research, not advising the agency about research.” Accordingly, CSS argued, the correct NAICS code was the R&D code, 541715, not the consulting code, 541620.

SBA OHA denied CSS’s NAICS code appeal, holding that CSS had not met its burden of demonstrating that the Contracting Officer’s chosen NAICS code was erroneous. Undeterred, CSS filed an appeal with the Court of Federal Claims, challenging SBA OHA’s decision, and by extension, the Contracting Officer’s underlying NAICS code selection.

The Court wrote that, under the SBA’s regulations, a Contracting Officer must designate “the single NAICS code which best describes the principal purpose of the product or service being acquired.” (FAR 19.102 imposes a similar requirement). That is, with apologies to the late, great Tina Turner, not just any ol’ NAICS code will do–the assigned NAICS code must be simply the best.

In this regard, the court wrote, an agency “cannot justify its selection of a particular NAICS code on the basis that it fits the procurement in some general sense or that the selection is ‘good enough for government work.'” Quoting from the SBA’s regulations, the Court explained that, when selecting a NAICS code:

Primary consideration is given to the industry descriptions in the U.S. NAICS Manual, the product or service description in the solicitation and any attachments to it, the relative value and importance of the components of the procurement making up the end item being procured, and the function of the goods or services being purchased.

Further, the SBA’s regulations specify that “a procurement is generally classified according to the component which accounts for the greatest percentage of contract value.” These regulatory requirements, the court wrote, “are the substantive yardsticks the Court must use to assess the agency’s NAICS code selection and the subsequent OHA Decision.”

Applying these provisions, the court found that SBA OHA’s analysis was flawed in multiple respects. For example, examining the Performance Work Statement, the court found that the solicitation “primarily seeks R&D services,” including “literally dozens of tasks in the Solicitation that naturally fit the definitions of ‘research’ and ‘experimental development’ in the NAICS Manual.” In contrast, the court found, the solicitation included “minimal consulting tasks.” Additionally, an estimated 80% of the ordering value–that is, the ‘greatest percentage of contract value’–was comprised of R&D tasks.

In sum, the Court held, the Contracting Officer’s decision to assign the Environmental Consulting Services NAICS code, and SBA OHA’s decision upholding that selection, were “objectively unreasonable.” The court issued an injunction preventing NOAA from proceeding with the solicitation until another NAICS code was assigned.

The Consolidated Safety Services case offers valuable lessons for government and industry alike.

For agencies, it is important that Contracting Officers receive effective training on the rules and processes assosciated with selecting NAICS codes. The Consolidated Safety Services case itself is a good starting point from a training perspective, as the decision not only demonstrates the potential consequences of a flawed selection, but contains numerous references to the applicable regulations and interpretive case law.

For industry, the Consolidated Safety Services case is a valuable reminder that a Contracting Officer must pick the “best” NAICS code–that is, the one that’s “better than all the rest”–for the work and that a potential offeror is entitled to file a NAICS code appeal if the offeror believes that the NAICS code selection was flawed.

NAICS codes are just six little digits, but the NAICS code selection can mean the difference between participating in a set-aside competition–or finding yourself like me, stuck outside the velvet rope because the bouncer inexplicably doesn’t see the inherent coolness in my insights on FAR 19.102.

Boring but important disclaimer: The information in this article is not, nor is it intended to be, legal advice. You should consult an attorney for individual advice regarding your own situation.

Article link: https://www.linkedin.com/pulse/naics-codes-good-enough-government-work-doesnt-cut-steven-koprince-gaauc

The inner workings of many AIs are mysterious, but with increasing use of such technologies in high stakes scenarios, how should their inscrutable nature be dealt with?

By Nick Petrić Howe

Download this episode of the Nature Podcast

Many AIs are ‘black box’ in nature, meaning that part of all of the underlying structure is obfuscated, either intentionally to protect proprietary information, due to the sheer complexity of the model, or both. This can be problematic in situations where people are harmed by decisions made by AI but left without recourse to challenge them.

Many researchers in search of solutions have coalesced around a concept called Explainable AI, but this too has its issues. Notably, that there is no real consensus on what it is or how it should be achieved. So how do we deal with these black boxes? In this podcast, we try to find out.

Article link: https://www.nature.com/articles/d41586-023-04154-4?