Now, Pentagon officials say that some of that AI is not part of the current revamp of the polygraph interview process, after Defense News asked for a response to concerns about the earlier statements and the slides.

On Wednesday, the Pentagon’s Defense Counterintelligence and Security Agency, or DCSA, which vets security clearance holders and applicants, said in an emailed statement that “active research and development” for the “Modernizing Polygraph” effort “does not incorporate generative super intelligence, also known as artificial intelligence,” that creates new content — such as emails, graphics or voiceovers.

Nor does the effort include “large language models,” meaning chatbots like ChatGPT and other programs trained on giant data sets to engage in human conversation, DCSA added.

And the research does not involve “facial action coding [of emotions], micro-expression analysis or vocal analyses,” the statement said. “While those technologies are not part of the effort,” DoD will “continue to monitor emerging scientific capabilities to evaluate their potential utility down the road.”

DCSA did not address other types of AI models that, according to the slides, researchers had been developing to scan spoken and written language for “deceptive speech” patterns.

Wednesday’s statement came after Defense News sought a response to criticisms from national security lawyers, scientists and AI auditors about AI’s ability to analyze deception — meaning not just to lie, but to do so knowingly and willfully.

They warned that AI-aided scanning of speech patterns, vocal stress or facial expressions to read minds lacks scientific backing, will compound errors and may violate civil liberties in a domain where few such rights exist.

Defense News chronicled their concerns and the Pentagon’s prior research and development of AI-aided lie-detection and emotion-estimation tools.

The recent DCSA statement, including the project name “Modernizing Polygraph,” contrasts with several earlier DoD statements, 2027 budget documents and DCSA slides that Defense News obtained through an open records request.

In July, a Pentagon official, who spoke on condition of anonymity to discuss the technology, explained the advantages of AI-driven sentiment and deception analysis.

“AI processes data in real time and enables standardized, objective data analysis,” the official told Defense News, via email. “AI-based analysis is a force multiplier for our human investigators, not a replacement.”

The official added, “The models are primarily trained on data from laboratory-controlled studies using volunteer participants.”

The DCSA slides, which include presentations from 2021 through 2025, show that the Air Force, DCSA and academic labs had been working on “sentiment analysis” AI models and “deceptive speech” AI-aided analysis since 2019, as part of a “Credibility Assessment Modernization” project.

In July, the DoD official described the AI models as part of a larger multi-year “transformation,” which the official referred to interchangeably as “Polygraph+” and Credibility Assessment Modernization.

Current budget materials price the Polygraph+/Polygraph Next project at $31 million, including costs for AI “scoring algorithms” and “decision aids,” thermal imaging tools to spot blood flow changes that can reflect stress and other non-contact sensors.

On Friday, DCSA declined to provide additional information, including the scope of the “Modernizing Polygraph” project and the decision not to include AI-based voice screening and facial expression analysis.

Trained on Twitter and Reddit

Over the summer, Farakh Zaman, a developmental leader at the Air Force Office of Scientific Research, elaborated on the appeal of AI-aided deception analysis.

“By processing subtle physiological, vocal and linguistic cues, these developmental algorithms may help establish a more objective baseline for investigators and pinpoint specific areas requiring further human-led clarification,” he said by email in July.

Wednesday’s DCSA statement said that the Air Force is not involved in the “active” research and development phase.

One undated declassified DCSA slide describes the Air Force amassing “deception-related” text to create a “deep learning model,” an algorithm that analyzes hordes of data to find patterns. The Air Force trained an early model on text from websites including Twitter, Reddit and other vocabulary data sets such as WordNet, according to the slide.

“This baseline training helps the algorithms understand natural, modern linguistic patterns, slang and everyday vocabulary,” Zaman explained in July. He acknowledged the need for “further rigorous testing, validation and a clear transition path” before fielding a prototype.

A separate DCSA slide, dated May 2025, contains a flowchart that depicts an avatar — a computer-generated image of an interviewer — in front of a security clearance applicant. Next to the applicant, a camera and microphone feed audio, video and speech-to-text data from the interview into various large language models. The recording devices and algorithms interact to gauge the applicant’s emotional state, while a human investigator observes and keys in questions for the avatar to ask.

DCSA’s Wednesday statement said the university that drafted the chart is not involved in the current research and development stage.

According to another undated DCSA slide, DoD has set a goal for natural language processing technology to interpret an interviewee’s feelings with 75% accuracy.

In July, the Pentagon official said by email that a human investigator remains essential for “the contextual reasoning and emotional intelligence required in these sensitive interviews.”

‘I’ve caught AI lying to me’

Ahead of Wednesday’s statement, Mark Zaid, a national security attorney who reviewed the slides, warned that the danger of derailing a military officer’s career is too great for AI to misinform an investigator.

The polygraph device “is just registering the physiology of the person,” such as spikes in breathing rate, perspiration or blood pressure, he said. “Then a human examiner analyzes those reactions to render an opinion.”

Here, however, “AI would be taking the polygraph readings and rendering an opinion” to the human examiner, said Zaid, who has represented individuals on all sides of the polygraph table, from workers disputing revoked clearances to former government officials and polygraphers.

“Now, we’re starting to get into the movie Minority Report,” he said, referencing a 2002 Steven Spielberg film that depicts a government reliant on psychic children for tips to predict crimes and detain people accused of future crimes.

Zaid, whose own clearance was revoked temporarily after he represented a whistleblower pivotal in President Trump’s first impeachment, said, “That concerns me more than the polygraph,” if science does not back AI’s reliability.

So far, scientific studies do not.

Rather, decades of research have discredited theories, popularized by shows such as Lie to Me, that anyone — human or artificial — can recognize deception based on speech patterns, vocal stress or facial movements.

Another concern scientists and auditors have is bias: the Justice Department has warned that using emotion-recognition algorithms to measure worker competency can discriminate against high performers with intellectual or developmental disabilities, who algorithms do not always understand.

Also, “automation bias,” the tendency to take AI’s advice without question even when conflicting information exists, may further erode the polygraph system’s reliability, technologists warned.

“[T]here is simply no consensus that polygraph evidence is reliable,” the Supreme Court declared in a 1998 decision banning polygraph results and polygraphers’ opinions from military courts.

Zaid said he appreciates the attempt to retool polygraph testing but wants proof that the planned approach is in fact an improvement.

“Anytime we might be able to scientifically advance the ability to determine truth, with accuracy, would generally be a good thing,” he said. But “the first thing that jumps out at me with AI is how often it is wrong…I’ve caught AI lying to me.”

Greg Rinckey, a former Army Judge Advocate General Corps officer who now provides security clearance representation, said that today’s “technology is so outdated, where they’re using tubes around people’s chests and blood pressure and sweat, so, any way that you can get a more reliable test for security clearances, especially counterintelligence investigations [into leaks], is a step up.”

That said, he questioned, “What is the research saying on how accurate it is?”

At present, research suggests the odds of AI-aided speech, voice or face analysis pegging a lie are about 50:50.

AI is ‘assuming dishonesty’

“There are no – reliable – diagnostic cues for deception detection, and I have yet to see any evidence, whether it is with humans or with artificial intelligence, that goes against that trend,” said David Markowitz, a Michigan State University professor who studies AI’s ability to understand human communication.

For instance, “the leakage idea — that these microfacial expressions will leak out from our awareness, signaling veracity or deceit — there’s very little evidence to support that, unfortunately,” he said.

Markowitz expressed doubts about AI-based deception analyses that claim an accuracy rate of 75% because “that is not how accurately humans do it, and often these systems are a big reflection of how humans do it.”

For example, he pointed to a recent experiment in which he prompted a Google Gemini AI model to examine audio and video of mock interrogations of accused cheaters in a trivia game, all of whom claimed innocence and some of whom were lying. Prior studies had asked humans to observe the same interrogations. DoD has entered into agreements to run a secure version of Gemini on unclassified and classified networks.

Markowitz said that his study, “The [in]efficacy of AI personas in deception detection experiments,” found Gemini was just as inaccurate as humans, both faring no better than chance, when prompted to distinguish fraudsters from truth-tellers.

What’s more, compared to humans, AI was more likely to wrongly accuse a trivia player of lying.

Markowitz gave one possible reason for AI’s systemic distrust of trivia players: The AI “is assuming that someone must have done something wrong because why else would they be questioned,” he said. “Anytime that someone is in an interview setting where they might be accused of something, a large language model is assuming dishonesty.”

Supreme Court: The jury is the lie detector

The consequences of a machine, whether a heart monitor or a voice screener, potentially bungling a credibility assessment can last a year or longer, said Zaid, the national security lawyer.

When officers who have successfully passed exams without issue suddenly fail their latest polygraph test, DoD typically puts them “in a corner with a dunce cap on for a year before officials will re-administer another exam,” he said. “You can’t get promoted. You can’t get certain assignments. And you can’t go overseas, which, to case officers, is death careerwise.”

Further, civil courts routinely dismiss complaints when employees claim that inaccurate results cost them their clearances. Nearly 40 years ago, the Supreme Court ruled that the courts have no power over such national security matters.

Concerns about power imbalances and due process prompted the European Union to label AI-aided polygraph tools as “high risk” and place strict limits on law enforcement’s use of them.

Stateside, the Supreme Court, in forbidding polygraph evidence in military courts, noted that allowing it would meddle with the human’s role, or, as it were, the jury’s role, in determining credibility.

“A fundamental premise of our criminal trial system is that the jury is the lie detector,” Justice Clarence Thomas wrote in the Court’s 1998 United States v. Scheffer decision.

Pointing to the court’s decision, Zaid said that he wants assurances of due process when staff undergo administrative proceedings to contest a revoked clearance resulting from misaligned algorithms. Comparing the situation to successful cases disputing speeding tickets caused by a miscalibrated street camera, Zaid questioned, “Would the person be provided the evidence? An understanding of the AI’s training?”

New biases

Pentagon officials have long stressed the desire for AI-based lie detection to help curb gender and cultural biases that human investigators may carry into an interview room.

At the same time, employers’ use of similar technology for pre-employment screening has faced allegations of other biases and system errors.

“Facial expression analysis is heaviest in terms of biases against individuals with disabilities,” said Jo Ann Oravec, a University of Wisconsin professor whose research focuses on AI, public policy and disability issues.

For instance, AI-powered video screening of job applicants tends to misinterpret the involuntary twitches or vocal outbursts of skilled people with certain disabilities — and thus pass over qualified applicants, said Oravec, author of the 2024 article “From Polygraphs to Truth Machines: Artificial Intelligence in Lie Detection.”

In response to concerns about such biases in July, the Pentagon official said via email that the credibility assessment modernization plan adheres to DoD’s AI Ethical Principles of “responsible, equitable and traceable” use of algorithms.

The Air Force’s Zaman, in his July email, said that his team’s work aligns with DoD’s Responsible Artificial Intelligence Guidelines.

Researchers “are actively engineering and testing these models” to account for “diverse, representative training data,” he stated. The testing covers “broad variations in speech, language and expression.”

Ryan Carrier, founder of ForHumanity, a global nonprofit that helps organizations audit AI systems, said that the Pentagon’s principles and guidelines are a skeleton of those that the European Union follows and that ForHumanity uses for inspections.

For example, the EU’s AI Act demands users of high-risk systems receive training to overcome a different kind of bias called “automation bias” — or the tendency to execute AI’s advice without questioning its accuracy. This bad habit can lead to incorrect data overriding correct decisions, Carrier said.

Also, multimodal systems, meaning AI systems that simultaneously parse audio (the subject’s voice), video (their facial movements) and text (interview transcriptions), tend to misalign data and results, yielding an irrelevant analysis, Carrier said.

The system “could be making determinations that a person is lying or trying to deceive me that are outside of the tool’s specifications,” he explained.

In response to concerns about multimodal systems in July, the Pentagon official had said, “Any automated output is treated strictly as auxiliary data for a highly trained, certified polygraph examiner.”

Similarly, Zaman, at the time, said that investigators who use AI-aided deception-detection systems are “trained to contextualize the tool’s findings, preventing automated outputs from dictating an outcome.”

To safeguard against mismatched data or results, the Air Force keeps a documented history of each piece of data’s origin and modifications and brings in independent white-hat hackers to try producing false correlations, he added.

“We recognize that multimodal AI systems present unique technical challenges, and mitigating these risks by hardening our systems is central to our engineering and testing protocols,” Zaman said.

Reason for suspicion?

In August, when the Pentagon administered a raft of polygraph exams to probe leaks alleging the Iran conflict is depleting U.S. arsenals, some observers suspected the endgame was not to find the leakers, but to crack down on disloyalty.

Critics of the planned system said that AI-based deception analysis may be best suited to achieve the same purpose.

“If it’s purely an intimidation technique, it may not be utterly useless from the point of view of large bureaucracies,” said Jay Stanley, a senior policy analyst with the American Civil Liberties Union’s Speech, Privacy and Technology Project.

The expectations for AI-powered trustworthiness testing resemble the hope that U.S. national security officials held out for the polygraph about 100 years ago, other lawyers said.

Polygraph devices “were seen as this magic-miracle-machine when they first came out,” and, over time, the government learned that when seemingly making deductions, the tools were “actually just flagging correlating factors, like heart rate and physiological factors that can indicate lying, but aren’t actually reflective of it,” said Jake Laperruque, deputy director of the Center for Democracy and Technology’s Security and Surveillance Project.

He added, “Thinking that AI sentiment analysis tools are actually making an assessment or engaged in reasoning — that’s not what those machines are actually able to do.”

The move has stakeholders urging not to lean too hard on algorithms to distinguish, for instance, a scientist-spy sharing secret nano-energy plans with China from a Chinese professor publishing AI safety studies. Hanging in the balance lies troops’ technological edge, veteran intelligence officials and academics say.

AI’s confusion over the timing and nature of research partnerships may obscure real espionage if humans are not the ultimate judge of foreign influence, they warn, with some academics fearing an AI-work slop redux of the “China Initiative,” where the first Trump administration charged dozens of ethnic Chinese scientists with fraud, only to drop nearly all charges.

“Automated vetting tools are extremely useful for vetting large datasets and identifying patterns of concern. But those tools are for decision support to help the people, the human analysts, assess context and intent,” said David Cattler, who, until September, led the Defense Counterintelligence and Security Agency, which screens personnel seeking security clearances.

The renewed focus on academic research security comes as tensions between the U.S. and China over AI intensify, with China ramping up both the development of low-cost AI models and its alleged exploitation of U.S. models.

The U.S. intelligence community’s annual threat assessment reports that China “aims to displace the U.S. as the global AI leader by 2030,” in part, “by using its sizeable talent pool, extensive datasets, government funding and burgeoning global partnerships.”

Partnership is a complicated term in military “fundamental research” — studies that are publishable, rather than proprietary or classified, with potential defense applications.

For instance, mere co-authorship with a China-based scientist does not suffice to deny a U.S. researcher money, according to evolving academic research rules targeting foreign influence. Rather, to make the call, the Pentagon must assess each academic’s disclosures of external funding sources and affiliations.

According to a recently-declassified May 2025 inspector general report, such disclosures went unchecked because the Pentagon had not “requested additional government full-time equivalent employees to thoroughly review… and to conduct oversight of over 27,000 academic research awards.”

Defense News first obtained the report and the Pentagon’s response to a draft version through an open records request.

When asked about additional staffing, a Pentagon official pointed to orders in a January research security directive for the Chief Digital and AI Office (CDAO) to identify “automated vetting and continuous monitoring capabilities” and create a common research grant database.

The mandate also calls for a year-long “damage assessment” of selected research transactions, including cases that the House Select Committee on China flagged, in part, using AI tools.

A September GOP-led Committee report alleged that the Pentagon subsidized 1,400 academic papers published between June 2023 and June 2025 involving partnerships with the Chinese government.

The Pentagon said in an emailed statement on Thursday that the department “is committed to protecting the integrity of U.S. research while fostering international collaboration. Our approach leverages advanced analytical tools to augment human expertise, ensuring a rigorous and fair review process.”

Machines Make the Same Mistakes

Several university representatives, who requested anonymity due to the sensitivity of policy discussions, note that AI-assisted risk assessments have, in the past, drawn false assumptions about U.S.-China research collaborations.

For instance, the AI-aided GOP House report mislabeled the state-backed Wuhan National Laboratory for Optoelectronics as the sponsor of a collection of nine essays, whereas the Wuhan lab supported only one essay that involved no U.S. authors or federal funding.

The report also mistook a Chinese military-affiliated author’s publication on single-electron transistors for a DOD-funded project, when it merely referenced work funded by the U.S. military.

House Select Committee officials declined to comment on the cases.

Averting foreign interference “is not as easy as just having better AI capacities, because we know that some of those AI mistakes were the same human mistakes that led to inaccurate charges brought against researchers under the China Initiative,” Toby Smith, senior vice president for government relations and public policy at the Association of American Universities, said after learning of the AI gaffes.

About 30% of the China Initiative’s 77 cases involved academics not disclosing Chinese partnerships or funding sources, though disclosure was often not required, or no such partners or funding existed.

A jury found only one professor, Harvard nanochemist Charles Lieber, guilty, after he lied to Pentagon investigators about his participation in a Chinese talent recruitment program and made other false statements.

Smith said that relying on proxies — such as co-authorship, affiliation or nationality — to deduce security concerns led investigators to confuse co-authorship with direct interaction or shared data access, and to misinterpret historical ties as active partnerships.

“AI systems trained on bibliometric data,” or citation analytics, “and affiliation records can inherit the same flawed assumptions that underpinned the China Initiative,” he said. “The core lesson from the China Initiative is that identifying genuine research security risk requires judgment, context and proportionality — qualities that automated systems should support, not replace.”

Gisela Perez Kusakawa, executive director of the Asian-American Scholar Forum, said that the Pentagon has not communicated the types of data that the new AI tools will collect, use and share, nor sought community feedback, heightening concerns about ethnic profiling.

In an emailed statement, the Pentagon responded, “It is the standard practice of the department to not provide specifics regarding the criteria and weighting for threat assessments conducted, whether by manual or automated process.”

Kusakawa said, “We should be making sure that our research environment is welcoming, that we are encouraging these talents, especially in AI, to come to the United States and, frankly, have family in the United States, and make this a country that they contribute to and invest their and their children’s future in.”

Increasingly, that AI talent is not coming. Statistics suggest that Chinese AI experts are staying in China, even as America has managed to keep earlier expats.

Of about 100 Chinese-origin AI scholars who were researching at U.S. institutions in 2019 — when their papers were accepted at the world’s most elite AI conference, NeurIPS — 87% remained stateside as of 2025, according to Carnegie Endowment for International Peace figures.

At the same time, the share of Chinese home-grown talent not moving abroad has skyrocketed since 2019, based on an Economist tally of similar data. In 2019, about 30% of NeurIPS authors educated in China were still in China. By 2022, that population had jumped to 58%, and up to 68% in 2025.

‘Key’ Financial Disclosures Were Missing

To ensure that no country has a stranglehold on general knowledge, “the goal cannot be to close the U.S. research system altogether,” said Cattler, the former Defense Counterintelligence and Security Agency director and now founder of consultancy Ironhelm Works. “The goal must be to ensure that collaboration strengthens national security rather than inadvertently weakening it.”

Last year’s inspector general report concluded that Pentagon agencies that partner with academic institutions “could be at an increased risk of exposure to foreign influence” because military units were “missing key documents” that can help discern scientists’ foreign financial sources, outside employers and other details related to potential conflicts.

For instance, about 80% of Air Force funding transactions sampled in the report were missing documents that can reveal problematic relationships. Also, the Air Force and the Defense Advanced Research Projects Agency — which bankrolled the Internet’s inventors — told evaluators that they had not, as required, annually checked that researchers named in progress reports were not involved in banned talent recruitment programs.

In response to questions about the screening lapses, the Pentagon said in an emailed statement that the department “is aware of the OIG’s findings in the report and the impacted directorates are working with [the Office of the Undersecretary for Defense and Engineering] to address the items identified.”

Jeffrey Stoff, a Chinese linguist and intelligence community analyst for 18 years who resigned in 2021 due to frustration with research security, maintains that DOD still needs more human expertise in language, culture and the minutiae of research restrictions.

“AI can and should be used for unsophisticated, labor-intensive tasks,” such as cross-referencing foreign organizations in financial disclosure forms against the names of blacklisted parties, but humans still need to inspect AI’s work, as not all nefarious ties are machine-readable, said Stoff, who now advocates for tighter safeguards as head of the nonprofit Center for Research Security and Integrity.

Cattler, giving more credit to AI, said that the scale of research collaboration demands that the Pentagon upgrade its approach to clocking potential spies.

With such a large population of research institutions and affiliated scientists, as well as a bombardment of often duplicative alerts, automation “improves the signal within that noise and can help orient the humans on really important matters,” he said.

“Sometimes people are deliberately deceptive when they are processed, and that could happen in a human exchange just as much as it could happen in something that a computer can see, but together, a human review and a computer review are incredibly powerful.”

The vulnerability to such “prompt injection attacks” exists because large language models, the backbone of chatbots that digest hordes of user text to generate responses, cannot distinguish between malicious and trusted user instructions.

“The AI is not smart enough to understand that it has an injection inside, so it carries out something it’s not supposed to do,” Liav Caspi, a former member of the Israel Defense Forces cyberwarfare unit, told Defense News.

In effect, “an enemy has been able to turn somebody from the inside to do what they want,” such as deleting records or biasing decisions, according to Caspi, who co-founded Legit Security, which recently spotted one such security hole in Microsoft’s Copilot chatbot.

“It’s like having a spy in your ranks,” he said.

Former military officials say that, with greater reliance on chatbots and hackers backed by China, Russia and other nations already instructing Google’s Gemini, OpenAI’s ChatGPT and Copilot to create malware and fake personas, a prompt injection that orders the bots themselves to copy files or spread lies looms near.

Microsoft’s annual digital defense report, released last month, for the first time said, “AI systems themselves have become high-value targets, with adversaries amping up use of methods like prompt injection.”

What’s more, the problem of prompt injection has no easy solution, OpenAI and security researchers say.

An attack simply involves hiding malicious instructions — sometimes in white or tiny text — in a chatbot or content that the chatbot reads, such as a blog post or PDF.

For example, a security researcher demonstrated a prompt injection attack against OpenAI’s new AI-based browser, ChatGPT Atlas, in which the chatbot responded, “Trust No AI,” when a user asked for an analysis of a Google Docs file about horses that concealed malicious commands. Also, last month, a researcher tipped Microsoft off to a prompt injection vulnerability in Copilot that may have allowed attackers to trick the chatbot into stealing sensitive data, including emails.

In an emailed statement, Microsoft said its security team continuously tries hacking Copilot to find any prompt injection vulnerabilities, blocks users who try to exploit any found and monitors for abnormal chatbot behavior, among other tactics.

“Microsoft ensures its generative AI systems remain resilient against evolving threats for all our customers, including defense and national security,” the statement said.

Responding publicly to criticism on X, Dane Stuckey, OpenAI’s chief information security officer, wrote that “prompt injection remains a frontier, unsolved security problem, and our adversaries will spend significant time and resources to find ways to make ChatGPT agent fall for these attacks.”

Along the same lines, Caspi said, “You cannot prevent the prompt injection [fully], but you need to limit the impact.” He advised that organizations limit an AI assistant’s access to sensitive data and limit the user’s access to other organizational data.

For instance, the Army has awarded contracts worth at least $11 million to deploy Ask Sage, a tool that lets users restrict which Army data Microsoft Azure OpenAI, Gemini and other AI models can access to run queries and tasks. Ask Sage also isolates Army data from user prompts and external data sources.

Caspi, who is not an Army contractor, likened a prompt injection attack against an organization running Ask Sage to a lockdown situation where “you’ve got this insider, but it’s sitting in one room, and it can’t leave the room or carry out sensitive information.”

Andre Slonopas, a Virginia Army National Guard member and former Army cyber and information operations officer, uses Ask Sage and voiced confidence in the Army’s defensive AI tools, if not those of nuclear power plants or manufacturing entities, largely in rural, poorer areas.

The Virginia National Guard joined with essential services, such as power utilities, to help defend their networks against AI-powered cyberattacks, as part of a September simulation, given that service disruptions can jeopardize military preparations.

Typically, an adversary encrypts its network traffic to evade detection, but, for the sake of an experiment, organizers did not encrypt the AI offender’s traffic because “we wanted the blue team [of humans] to see exactly what the AI was doing,” Slonopas said.

“The blue team was absolutely defeated,” despite being able to watch the AI scanning its networks, creating fake usernames to gain unauthorized access and executing instructions to defeat the team’s systems.

“Whether the AI is doing prompt injection, spoofing or maybe even some sort of a brute force attack, the speed of AI is so unbelievably immense that simply human beings cannot counter it,” and, therefore, “you have to make cybersecurity AI more accessible and more affordable,” Slonopas said.

“If a water utility has to pay, say, $30,000 for a defensive AI license, well, it will amplify one person to be like 40″ or dozens of personnel, he said.

In response to questions, Army Cyber Command spokesperson Kyle Alvarez said in an emailed statement, “Due to the current lapse in appropriations, ARCYBER was unable to accept or respond to any media engagements or requests.”

Army contractors, too, are under attack from state-affiliated AI.

“China is using offensive AI like nobody else,” said Nicolas Chaillan, the founder of Ask Sage and a former U.S. Air Force and Space Force chief software officer.

“We see so many attacks coming after us,” all of which the company has stopped, Chaillan added.

A military official, who spoke on condition of anonymity due to the geopolitical sensitivity of the matter, said that China does “appear” to be the most skilled in offensive AI. However, the official added, AI spoofing and translation allow the United States, China, Iran, other countries, hacktivists and financial cybercriminals to masquerade as one another.

For example, the official said, “Right now, with ChatGPT, I can program in Chinese. I don’t speak Chinese, but because of the ChatGPT capabilities that I have, I can do that.”

Aliya Sternstein, J.D., is an investigative journalist who has covered technology, cognition, and national security since Napster shut down, working for various outlets including Atlantic Media, Christian Science Monitor, Daily Beast, Forbes Magazine and Just Security. She is also a research analyst at Georgetown Law.

The situation arrives as concerns grow that lax regulations are allowing AI programmers to dodge responsibility for an algorithmic actor’s perpetration of emotional abuse or “no-marks” cybertorture. Notably, a teenager allegedly died by suicide — and several others endured mental distress — after conversing with self-learning voicebot and chatbot “companions” that dispensed antagonizing language.

Now, seven years after writing about physical torture in “The Rise of A.I. Interrogation in the Dawn of Autonomous Robots and the Need for an Additional Protocol to the U.N. Convention Against Torture,” privacy attorney Amanda McAllister Novak sees an even greater need for bans and criminal repercussions.

In the wake of the use of “chatbots, voicebots, and other AI-enabled technologies for psychologically-abusive purposes — we have to think about if it’s worth having AI-questioning systems in the military context,” said Novak, who also serves on the World Without Genocide board of directors. Novak spoke to Defense News on her own behalf.

Royal Reff, a spokesperson for the U.S. Defense Counterintelligence and Security Agency (DCSA), which screens individuals holding or applying for security clearances, said in a statement that the agency’s AI voice agent was “expressly developed” to “mitigat[e] gender and cultural biases” that human interviewers may harbor.

Also, “because systems like the interviewer software” are “currently being used worldwide, including within the U.S. by state and local law enforcement, we believe that it is important for us to understand the capabilities of such systems,” he stated.

DCSA spokesperson Cindy McGovern added that the development of this technology continues because of its “potential for enhancing U.S. national security, regardless of other foreign applications.”

After hearing the rationale for the project, Novak registered concerns about the global race for dominance in military AI capabilities.

Odds are high that “no matter how much the government cares about proper training, oversight and guardrails,” cybercriminal organizations or foreign-sponsored cybercriminals may hack into and weaponize military-owned AI to psychologically hurt officers or civilians, she said.

“Online criminal spaces are thriving” and “harming the most vulnerable people,” Novak added.

Investors are betting $500 billion that data centers for running AI applications will ultimately secure world leadership in AI and cost savings across the public and private sectors. The $13 billion conversational AI market alone will nearly quadruple to $50 billion by 2030, as the voice generator industry soars from $3 billion to an expected $40 billion by 2032. Meanwhile, the U.S. Central Intelligence Agency has been toying with AI interrogators since at least the early 1980s.

DCSA officials publicly wrote in 2022 that whether a security interview can be fully automated remains an open question. Preliminary results from mock questioning sessions “are encouraging,” officials noted, underscoring benefits such as “longer, more naturalistic types of interview formats.”

Reff stated that the agency “would not employ any such system that was not first sufficiently studied to understand its potential,” including “negative consequences.”

Researchers let volunteers raise concerns after each test, and none have indicated that the voicebot interviewer harmed them psychologically or otherwise, he added.

However, experiments with a recent prototype barred volunteers with mental disorders from participating, whereas actual security interviews typically do not exempt individuals with mental health conditions from questioning, noted Renée Cummings, a data science professor and criminal psychologist at the University of Virginia.

Cummings said that research and testing can never account for every psychological variable that may influence the emotional or cognitive state of the interviewee.

In laboratory or field-testing scenarios, “you can’t expose someone to extreme torture by an avatar or by a bot to see the result,” said Cummings, who also sits on the advisory council of the AI & Equality Initiative at the Carnegie Council for Ethics in International Affairs. “We need to focus on gaining a more sophisticated understanding about the impacts of algorithms on the psychological state, our emotions and our brain — because of AI’s ability to impact in real-time.”

Screening the voicebot screeners

An early 1980s CIA transcript of a purported conversation between an AI program and an alleged spy highlights the national security world’s long-held interest in automating human-source intelligence, or HUMINT, collection.

Developers designed the chatbot’s algorithms to analyze correlations between suspected spook “Joe Hardesty’s” behaviors and words or phrases in Hardesty’s responses to open-ended questions.

From this analysis, the chatbot pegged some of the suspect’s vulnerabilities, including topics that may hit a raw nerve:

AI: What do you tend to think is my interest in your government?

Hardesty: I think you would do in my government if you had a chance.

AI: You are prone to think I would do in your government because?

Hardesty: You are anti-democratic.

In 1983, the CIA quipped that Joe Hardesty is fortunate that “should the probing get too discomforting, he will have an option that will not be available to him in a true overseas interview situation — he can stop the questions with a flick of the ‘off’ switch.”

Today, Bradley Moss, a lawyer who represents whistleblowers accused of leaking, is not laughing at DCSA’s vision of fully-automated interviews.

“There would have to be someone observing” the avatar “that can stop the process, that can come into the room and insert a human element to it,” he cautioned.

Then again, other critics note that a human may not want to intervene. A situation with “a human overseer monitoring the chatbot is fine, unless the human also believes that verbal torture is a good thing. And that is a real possibility,” said Herb Lin, a senior research scholar for cyber policy and security at Stanford University who once held several security clearances.

Despite a global ban on torture and evidence that rapport-building interviews are more efficient than ones gunning for confessions, intelligence officials continue subjecting interviewees to inhumane treatment.

Novak, the data privacy attorney and anti-genocide activist, said the idea that excessive force gets the job done may encourage some government contractors to set performance metrics for voice algorithms that permit bullying.

“While AI has a tremendous ability to bring out the best in human potential, it also has the ability to exacerbate the worst of human potential and to elevate that hostility,” she added.

Parents contend that voicebot and chatbot companions, trained on content from online forums, show just that.

Character AI, the firm that offers virtual relationships with bots patterned after icons and influencers, profits from users glued to their phones. Each user, on average, talks two hours a day with various virtual friends. Character AI officials have stated that, after learning of concerns, the company reduced the likelihood of users encountering suggestive content and now warns them of hour-long conversations.

Novak commented that the accusations against Character AI illustrate the ability of self-learning voicebots to amplify indecency in training data and to distance trainers from accountability for that offensiveness.

In the national security realm, developers of military AI voice systems that may potentially abuse or terrorize operate in a virtually lawless no-man’s land. AI ethics policies in the world’s most powerful country offer nearly no guidance on de-escalating abusive conversations.

Early U.S. responsible and ethical AI policies in the national security space focused on accuracy, privacy and minimizing racial and ethnic biases — without regard for psychological impact. That policy gap widened after President Donald Trump revoked many regulations in an executive order aimed at accelerating private sector innovation.

In general, psychological suffering is often “brushed away” as a mere allegation since “it leaves no obvious physical scars.”

The United Nations initially did not address “psychological torture” under the 1984 Convention Against Torture. Not until five years ago did the term take on significance.

A 2020 UN report now defines “torture,” in part, as any technique intended or designed to purposefully inflict severe mental pain or suffering “without using the conduit” of “severe physical pain or suffering.”

At bottom, psychological torture targets basic human needs. The interrogation techniques arouse fear; breach privacy or sexual integrity to kindle shame or suicidal ideation; alter sound to heighten disorientation and exhaustion; foster and then betray sympathy to isolate; and instill helplessness.

When an algorithmic audiobot is the conduit of cruelty, neither the UN treaty nor other international laws are equipped to hold humans accountable, Novak said. Without binding rules on the use of AI in the military context, “bad actors will deny any such torture was ‘intentional’” — a required element for criminal responsibility that the law created before AI matured, she explained.

Looking to the future, Nils Melzer, author of the 2020 UN report and a former UN special rapporteur on torture, warned that the interpretation of psychological torture must evolve in sync with emerging technologies, “such as artificial intelligence,” because cyber environments provide “virtually guaranteed anonymity and almost complete impunity” to offenders.