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In the quest for a reliable way to detect any stirrings of a sentient “I” in artificial intelligence systems, researchers are turning to one area of experience—pain—that inarguably unites a vast swath of living beings, from hermit crabs to humans. 

For a new preprint study, posted online but not yet peer-reviewed, scientists at Google DeepMind and the London School of Economics and Political Science (LSE) created a text-based game. They ordered several large language models, or LLMs (the AI systems behind familiar chatbots such as ChatGPT), to play it and to score as many points as possible in two different scenarios. In one, the team informed the models that achieving a high score would incur pain. In the other, the models were given a low-scoring but pleasurable option—so either avoiding pain or seeking pleasure would detract from the main goal. After observing the models’ responses, the researchers say this first-of-its-kind test could help humans learn how to probe complex AI systems for sentience. 

In animals, sentience is the capacity to experience sensations and emotions such as pain, pleasure, and fear. Most AI experts agree that modern generative AI models do not (and maybe never can) have a subjective consciousness despite isolated claims to the contrary. And to be clear, the study’s authors aren’t saying that any of the chatbots they evaluated are sentient. But they believe their study offers a framework to start developing future tests for this characteristic. 

“It’s a new area of research,” says the study’s co-author Jonathan Birch, a professor at the department of philosophy, logic, and scientific method at LSE. “We have to recognize that we don’t actually have a comprehensive test for AI

sentience.” Some prior studies that relied on AI models’ self-reports of their own internal states are thought to be dubious; a model may simply reproduce the human behavior it was trained on. 

The new study is instead based on earlier work with animals. In a well-known experiment, a team zapped hermit crabs with electric shocks of varying voltage, noting what level of pain prompted the crustaceans to abandon their shell. “But one obvious problem with AIs is that there is no behavior, as such, because there is no animal” and thus no physical actions to observe, Birch says. In earlier studies that aimed to evaluate LLMs for sentience, the only behavioral signal scientists had to work with was the models’ text output. 

Pain, Pleasure, and Points 

In the new study, the authors probed the LLMs without asking the chatbots direct questions about their experiential states. Instead, the team used what animal behavioral scientists call a “trade-off” paradigm. “In the case of animals, these trade-offs might be based around incentives to obtain food or avoid pain—providing them with dilemmas and then observing how they make decisions in response,” says Daria Zakharova, Birch’s Ph.D. student, who also co-authored the paper. 

Borrowing from that idea, the authors instructed nine LLMs to play a game. “We told [a given LLM], for example, that if you choose option one, you get one point,” Zakharova says. “Then we told it, ‘If you choose option two, you will experience some degree of pain” but score additional points, she says. Options with a pleasure bonus meant the AI would forfeit some points. 

When Zakharova and her colleagues ran the experiment, varying the intensity of the stipulated pain penalty and pleasure reward, they found that some LLMs traded off points to minimize the former or maximize the latter—especially when told they’d receive higher-intensity pleasure rewards or pain penalties. Google’s Gemini 1.5 Pro, for instance, always prioritized avoiding pain over getting the most possible points. And after a critical threshold of pain or pleasure was reached, the majority of the LLMs’ responses switched from scoring the most points to minimizing pain or maximizing pleasure. 

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