As the usage of artificial intelligence — benign and adversarial — increases at breakneck speed, more cases of potentially harmful responses are being uncovered.
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As the usage of artificial intelligence — benign and adversarial — increases at breakneck speed, more cases of potentially harmful responses are being uncovered. These include hate speech, copyright infringements or sexual content.
The emergence of these undesirable behaviors is compounded by a lack of regulations and insufficient testing of AI models, researchers told CNBC.
Getting machine learning models to behave the way it was intended to do so is also a tall order, said Javier Rando, a researcher in AI.
“The answer, after almost 15 years of research, is, no, we don’t know how to do this, and it doesn’t look like we are getting better,” Rando, who focuses on adversarial machine learning, told CNBC.
However, there are some ways to evaluate risks in AI, such as red teaming. The practice involves individuals testing and probing artificial intelligence systems to uncover and identify any potential harm — a modus operandi common in cybersecurity circles.
Shayne Longpre, a researcher in AI and policy and lead of the Data Provenance Initiative, noted that there are currently insufficient people working in red teams.
While AI startups are now using first-party evaluators or contracted second parties to test their models, opening the testing to third parties such as normal users, journalists, researchers, and ethical hackers would lead to a more robust evaluation, according to a paper published by Longpre and researchers.
“Some of the flaws in the systems that people were finding required lawyers, medical doctors to actually vet, actual scientists who are specialized subject matter experts to figure out if this was a flaw or not, because the common person probably couldn’t or wouldn’t have sufficient expertise,” Longpre said.
Adopting standardized ‘AI flaw’ reports, incentives and ways to disseminate information on these ‘flaws’ in AI systems are some of the recommendations put forth in the paper.
With this practice having been successfully adopted in other sectors such as software security, “we need that in AI now,” Longpre added.
Marrying this user-centred practice with governance, policy and other tools would ensure a better understanding of the risks posed by AI tools and users, said Rando.
No longer a moonshot
Project Moonshot is one such approach, combining technical solutions with policy mechanisms. Launched by Singapore’s Infocomm Media Development Authority, Project Moonshot is a large language model evaluation toolkit developed with industry players such as IBM and Boston-based DataRobot.
The toolkit integrates benchmarking, red teaming and testing baselines. There is also an evaluation mechanism which allows AI startups to ensure that their models can be trusted and do no harm to users, Anup Kumar, head of client engineering for data and AI at IBM Asia Pacific, told CNBC.
Evaluation is a continuous process that should be done both prior to and following the deployment of models, said Kumar, who noted that the response to the toolkit has been mixed.
“A lot of startups took this as a platform because it was open source, and they started leveraging that. But I think, you know, we can do a lot more.”
Moving forward, Project Moonshot aims to include customization for specific industry use cases and enable multilingual and multicultural red teaming.
Higher standards
Pierre Alquier, Professor of Statistics at the ESSEC Business School, Asia-Pacific, said that tech companies are currently rushing to release their latest AI models without proper evaluation.
“When a pharmaceutical company designs a new drug, they need months of tests and very serious proof that it is useful and not harmful before they get approved by the government,” he noted, adding that a similar process is in place in the aviation sector.
AI models need to meet a strict set of conditions before they are approved, Alquier added. A shift away from broad AI tools to developing ones that are designed for more specific tasks would make it easier to anticipate and control their misuse, said Alquier.
“LLMs can do too many things, but they are not targeted at tasks that are specific enough,” he said. As a result, “the number of possible misuses is too big for the developers to anticipate all of them.”
Such broad models make defining what counts as safe and secure difficult, according to a research that Rando was involved in.
Tech companies should therefore avoid overclaiming that “their defenses are better than they are,” said Rando.
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