Despite the growing demand for AI security and accountability, today’s tests and benchmarks will not be enough, a brand new report finds.

Generative AI models—models that may analyze and generate text, images, music, video, and more—are coming under increasing scrutiny for his or her tendency to make mistakes and usually behave unpredictably. Now, organizations from public sector agencies to big tech firms are proposing recent benchmarks to check the safety of those models.

At the tip of last yr, the startup Scale AI created lab dedicated to assessing how well models adhere to security guidelines. This month, NIST and the U.K. AI Safety Institute released tools designed to evaluate model risk.

However, these tests and model testing methods could also be insufficient.

The Ada Lovelace Institute (ALI), a British non-profit organization dedicated to artificial intelligence research, conducted test who interviewed experts from academic, civil society, and vendor modeling labs and examined recent research on AI security assessments. The co-authors found that while current assessments will be useful, they should not comprehensive, will be easily fooled, and don’t necessarily provide guidance on how models will perform in real-world scenarios.

“Whether it’s a smartphone, a prescription drug, or a car, we expect the products we use to be safe and reliable; in these sectors, products are rigorously tested to ensure they’re safe before being deployed,” Elliot Jones, a senior researcher at ALI and co-author of the report, told TechCrunch. “Our research aimed to examine the limitations of current approaches to assessing AI safety, assess how assessments are currently being used, and explore their use as a tool for policymakers and regulators.”

Benchmarks and red teaming

The study’s co-authors first surveyed the tutorial literature to determine an summary of the harms and risks that current models pose and the state of existing assessments of AI models. They then interviewed 16 experts, including 4 employees of unnamed technology firms developing generative AI systems.

The study revealed that there’s wide disagreement across the AI ​​industry on the perfect set of methods and taxonomies for evaluating models.

Some evaluations only tested how well the models matched benchmarks within the lab, not how the models might impact real-world users. Others were based on tests designed for research purposes, not on evaluating production models—yet vendors insisted on using them in production.

We’ve written before concerning the problems with AI benchmarking. This study highlights all of those issues and more.

Experts cited within the study noted that it’s hard to extrapolate a model’s performance from benchmark results, and it’s unclear whether benchmarks may even show that a model has a certain capability. For example, while a model may perform well on a state exam, that doesn’t mean it can have the ability to resolve more open legal challenges.

Experts also pointed to the issue of knowledge contamination, where benchmark results can overstate a model’s performance if it was trained on the identical data it’s being tested on. Benchmarks, in lots of cases, are chosen by organizations not because they’re the perfect assessment tools, but due to their convenience and ease of use, experts said.

“Benchmarks run the risk of being manipulated by developers who may train models on the same dataset that will be used to evaluate the model, which is equivalent to looking at an exam paper before an exam or strategically choosing which assessments to use,” Mahi Hardalupas, a researcher at ALI and co-author of the study, told TechCrunch. “Which version of the model is being evaluated also matters. Small changes can cause unpredictable changes in behavior and can override built-in safety features.”

The ALI study also found problems with “red-teaming,” the practice of getting individuals or groups “attack” a model to discover gaps and flaws. Many firms use red-teaming to judge models, including AI startups OpenAI and Anthropic, but there are few agreed-upon standards for red-teaming, making it difficult to evaluate the effectiveness of a given effort.

Experts told the study’s co-authors that finding individuals with the correct skills and experience to steer red teaming efforts will be difficult, and the manual nature of the method makes it expensive and labor-intensive, a barrier for smaller organizations that don’t have the mandatory resources.

Possible solutions

The foremost the reason why AI rankings have not improved are the pressure to release models faster and the reluctance to run tests that might cause issues before launch.

“The person we spoke to who works for a foundation modeling company felt that there is more pressure within companies to release models quickly, which makes it harder to push back and take assessments seriously,” Jones said. “The major AI labs are releasing models at a speed that outpaces their ability or society’s ability to ensure they are safe and reliable.”

One ALI survey respondent called evaluating models for safety an “intractable” problem. So what hopes does the industry—and those that regulate it—have for solutions?

Mahi Hardalupas, a researcher at ALI, believes there’s a way forward, but it can require greater commitment from public sector entities.

“Regulators and policymakers need to be clear about what they expect from ratings,” he said. “At the same time, the ratings community needs to be transparent about the current limitations and potential of ratings.”

Hardalupas suggests that governments mandate greater public participation in the event of assessments and implement measures to support an “ecosystem” of third-party testing, including programs to offer regular access to any required models and datasets.

Jones believes it could be mandatory to develop “context-aware” assessments that transcend simply testing a model’s response to a command, and as an alternative consider the sorts of users a model might affect (akin to people of a certain background, gender, or ethnicity), in addition to the ways wherein attacks on models could bypass security measures.

“This will require investment in fundamental evaluation science to develop more robust and repeatable evaluations based on an understanding of how the AI ​​model works,” she added.

However, there’s never a guarantee that a model is protected.

“As others have noted, ‘safety’ is not a property of models,” Hardalupas said. “Determining whether a model is ‘safe’ requires understanding the contexts in which it is used, to whom it is sold or shared, and whether the safeguards that are implemented are appropriate and robust to mitigate those risks. Baseline model assessments can serve exploratory purposes to identify potential risks, but they cannot guarantee that the model is safe, much less ‘completely safe.’ Many of our interviewees agreed that assessments cannot prove that a model is safe and can only indicate that the model is unsafe.”