Introduction to the Digital Consciousness Model (DCM)

Artificially intelligent systems, especially large language models (LLMs) used by almost 50% of the adult US population, have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious?

The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for tracking how the evidence changes over time as AI develops. Instead of adopting a single theory of consciousness, it incorporates a range of leading theories and perspectives—acknowledging that experts disagree fundamentally about what consciousness is and what conditions are necessary for it.

Here, we present some of the key initial results of the DCM. The full report is now available here.

We will be hosting a webinar on February 10 to discuss our findings and answer audience questions. You can find more information and register for that event here.

Why this matters

It is important to assess whether AI systems might be conscious in a way that takes seriously both the many different views about what consciousness is and the specific details of these systems. Even though our conclusions remain uncertain, it's worth trying to estimate, as concretely as we can, the probability that AI systems are conscious. Here are the reasons why:

• As AI systems become increasingly complex and sophisticated, many people (experts and laypeople alike) find it increasingly plausible that these systems may be phenomenally conscious—that is, they have experiences, and there is something that it feels like to be them.
• If AIs are conscious, then they likely deserve moral consideration, and we risk harming them if we do not take precautions to ensure their welfare. If AIs are not conscious but are believed to be, then we risk giving unwarranted consideration to entities that don’t matter at the expense of individuals who do (e.g., humans or other animals).
• Having a probability estimate that honestly reflects our uncertainty can help us decide when to take precautions and how to manage risks as we develop and use AI systems.
• By tracking how these probabilities change over time, we can forecast what future AI systems will be like and when important thresholds may be crossed.

Why estimating consciousness is a challenging task

Assessing whether AI systems might be conscious is difficult for three main reasons:

• There is no scientific or philosophical consensus about the nature of consciousness and what gives rise to it. There is widespread disagreement over existing theories, and these theories make very different predictions about whether AI systems are or could be conscious.
• Existing theories of consciousness were developed to describe consciousness in humans. It is often unclear how to apply them to AI systems or even to other animals.
• Although we are learning more about how AI systems work, there is still much about their inner workings that we do not fully understand, and the technology is changing rapidly.

How the model works

Our model is designed to help us reason about AI consciousness in light of our significant uncertainties.

• We evaluate the evidence from the perspective of 13 diverse stances on consciousness, including the best scientific theories of consciousness as well as more informal perspectives on when we should attribute consciousness to a system. We report what each perspective concludes, then combine these conclusions based on how credible experts find each perspective.
• We identify a list of general features of systems that might matter for assessing AI consciousness (e.g., attention, complexity, or biological similarity to humans), which we use to characterize the general commitments of different stances on consciousness.
• We identified over 200 specific indicators, properties that a system could have that would give us evidence about whether it possesses features relevant to consciousness. These include facts about what systems are made of, what they can do, and how they learn.

We gathered evidence about what current AI systems and biological species are like and used the model to arrive at a comprehensive probabilistic evaluation of the evidence.

• We considered four systems: 2024 state-of-the-art LLMs (such as ChatGPT 4 or Claude 3 Opus); humans; chickens; and ELIZA (a very simple natural language processing program from the 1960s)
• We asked experts to assess whether these systems possess each of the 200+ indicator properties.
• We constructed a statistical model (specifically, a hierarchical Bayesian model) that uses indicator values to provide evidence for whether a system has consciousness-relevant features, and then uses these feature values to provide evidence for whether the system is conscious according to each of the 13 perspectives we included.

How to interpret the results

The model produces probability estimates for consciousness in each system.

We want to be clear: we do not endorse these probabilities and think they should be interpreted with caution. We are much more confident about the comparisons the model allows us to make.

• Because the model is Bayesian, it requires a starting point—a "prior probability" that represents how likely we think consciousness is before looking at any evidence. The choice of a prior is often somewhat arbitrary and intended to reflect a state of ignorance about the details of the system. The final (posterior) probability the model generates can vary significantly depending on what we choose for the prior. Therefore, unless we are confident in our choices of priors, we shouldn’t be confident in the final probabilities.

• What the model reliably tells us is how much the evidence should change our minds. We can assess how strong the evidence for or against consciousness is by seeing how much the model’s output differs from the prior probability.
• In order to avoid introducing subjective bias about which systems are conscious and to instead focus just on what the evidence says, we assigned the same prior probability of consciousness (⅙) to each system. By comparing the relative probabilities for different systems, we can evaluate how much stronger or weaker the evidence is for AI consciousness than for more familiar systems like humans or chickens.

Key findings

With these caveats in place, we can identify some key takeaways from the Digital Consciousness Model:

• The evidence is against 2024 LLMs being conscious. The aggregated evidence favors the hypothesis that 2024 LLMs are not conscious.

• The evidence against 2024 LLMs being conscious is not decisive. While the evidence led us to lower the estimated probability of consciousness in 2024 LLMs, the total strength of the evidence was not overwhelmingly against LLM consciousness. The evidence against LLM consciousness is much weaker than the evidence against consciousness in simpler AI systems.
• Different stances (perspectives) make very different predictions about LLM consciousness. Perspectives that focus on cognitive complexity or human-like qualities found decent evidence for AI consciousness. Perspectives that focus on biology or having a body provide strong evidence against it.

• Which theory of consciousness is right matters a lot. Because different stances give strikingly different judgments about the probability of LLM consciousness, significant changes in the weights given to stances will yield significant differences in the results of the Digital Consciousness Model. It will be important to track how scientific and popular consensus about stances change over time and the consequences this will have on our judgments about the probability of consciousness.
• Overall, the evidence for consciousness in chickens was strong, though there was significant diversity across stances. The aggregated evidence strongly supported the conclusion that chickens are conscious. However, some stances that emphasize sophisticated cognitive abilities, like metacognition, assigned low scores to chicken consciousness.

What’s next

The Digital Consciousness Model provides a promising framework for systematically examining the evidence for consciousness in a diverse array of systems. We plan to develop and strengthen it in future work in the following ways:

• Gathering more expert assessments to strengthen our data
• Adding new types of evidence and new perspectives on consciousness
• Applying the model to newer AI systems so we can track changes over time and spot which systems are the strongest candidates for consciousness
• Applying the model to new biological species, allowing us to make more comparisons across systems.

Acknowledgments

This report is a project of the AI Cognition Initiative and Rethink Priorities. The authors are Derek Shiller, Hayley Clatterbuck, Laura Duffy, Arvo Muñoz Morán, David Moss, Adrià Moret, and Chris Percy. We are grateful for discussions with and feedback from Jeff Sebo, Bob Fischer, Alex Rand, Oscar Horta, Joe Emerson, Luhan Mikaelson, and audiences at NYU Center for Mind, Ethics, and Policy and the Eleos Conference on AI Consciousness and Welfare. If you like our work, please consider subscribing to our newsletter. You can explore our completed public work here.