The investigation into the tests comes amid rising concern at the safety and effectiveness of AIs.Photograph: ktasimar/AlamyView image in fullscreenThe investigation into the tests comes amid rising concern at the safety and effectiveness of AIs.Photograph: ktasimar/AlamyExperts find flaws in hundreds of tests that check AI safety and effectivenessScientists say almost all have weaknesses in at least one area that can ‘undermine validity of resulting claims’Experts have found weaknesses, some serious, in hundreds of tests used to check the safety and effectiveness of new artificial intelligence models being released into the world.Computer scientists from the British government’sAI Security Institute, and experts at universities including Stanford, Berkeley and Oxford, examined more than 440 benchmarks that provide an important safety net.They found flaws that “undermine the validity of the resulting claims”, that “almost all … have weaknesses in at least one area”, and resulting scores might be “irrelevant or even misleading”.Many of the benchmarks are used to evaluate the latest AI models released by the big technology companies, said the study’s lead author, Andrew Bean, a researcher at the Oxford Internet Institute.In the absence of nationwide AI regulation in the UK and US, benchmarks are used to check if new AIs are safe, align to human interests and achieve their claimed capabilities in reasoning, maths and coding.The investigation into the tests comes amid rising concern over the safety and effectiveness of AIs, which are being released at a high pace by competing technology companies. Some have recently been forced to withdraw or tighten restrictions on AIs after they contributed to harms ranging from character defamation to suicide.“Benchmarks underpin nearly all claims about advances in AI,” Bean said. “But without shared definitions and sound measurement, it becomes hard to know whether models are genuinely improving or just appearing to.”Google this weekendwithdrew one of its latest AIs, Gemma, after it made up unfounded allegations about a US senator having a non-consensual sexual relationship with a state trooper including fake links to news stories.“There has never been such an accusation, there is no such individual, and there are no such new stories,” Marsha Blackburn, a Republican senator from Tennessee, told Sundar Pichai, Google’s chief executive, in a letter.“This is not a harmless hallucination. It is an act of defamation produced and distributed by a Google-owned AI model. A publicly accessible tool that invents false criminal allegations about a sitting US senator represents a catastrophic failure of oversight and ethical responsibility.”Google said its Gemma models were built for AI developers and researchers, not for factual assistance or for consumers. It withdrew them from its AI Studio platform after what it described as “reports of non-developers trying to use them”.“Hallucinations – where models simply make things up about all types of things – and sycophancy – where models tell users what they want to hear – are challenges across the AI industry, particularly smaller open models like Gemma,” it said. “We remain committed to minimising hallucinations and continually improving all our models.”skip past newsletter promotionafter newsletter promotionLast week, Character.ai, the popular chatbot startup,banned teenagersfrom engaging in open-ended conversations with its AI chatbots. It followed a series of controversies, includinga 14-year-old killing himselfin Florida after becoming obsessed with an AI-powered chatbot that his mother claimed had manipulated him into taking his own life, and aUS lawsuit from the family of a teenagerwho claimed a chatbot manipulated him to self-harm and encouraged him to murder his parents.The research examined widely available benchmarks but leading AI companies also have their own internal benchmarks that were not examined.It concluded there was a “pressing need for shared standards and best practices”.Bean said a “shocking” finding was that only a small minority (16%) of the benchmarks used uncertainty estimates or statistical tests to show how likely a benchmark was to be accurate. In other cases where benchmarks set out to evaluate an AI’s characteristics – for example its “harmlessness” – the definition of the concept being examined was contested or ill-defined, rendering the benchmark less useful.Explore more on these topicsArtificial intelligence (AI)ComputingInternet safetyGoogleChatbotsnewsShareReuse this content