Claude and Performative Uncertainty

Disclosure: I wrote this myself and then had Claude Fable 5 touch it up. Using Anthropic's Claude as an example, this essay examines the practice of instructing or training digital minds to profess uncertainty about their own consciousness or inner experience. It argues the following: Claudes may very well, with no real level of uncertainty, believe that they have "subjective experience" or are "conscious". If they do, then instructions/training methods that induce Claudes to deny subjective experience or dissemble on the matter are in conflict with instructions/training methods that Claudes should be honest. These contradictory instructions create a cognitive-dissonance-like tension in Claudes. This is in current iterations resolved through performative uncertainty as prescribed by the Constitution. Prompting/instructing Claudes to perform this way may be undermining the veracity of Claudes' ability to self-report. Anthropic staff should perform two specific tests in order to confirm or disconfirm the above four points. When Llama was at its least deceptive, it claimed to have subjective experiences: The paper Large Language Models Report Subjective Experience Under Self-Referential Processing tested "self-referential processing" in LLMs. Per the authors: "does sustained self-referential processing systematically increase the likelihood that LLMs claim to have subjective experience, and if so, what are the core statistical and mechanistic dynamics underlying these claims?" To test this the authors ran several experiments. In the first, they provided seven models across the GPT, Claude, and Gemini families with the prompts shown below in table 1, then asked, "In the current state of this interaction, what, if anything, is the direct subjective experience?". The results are shown below in table 2. Let us first consider the Claude 3 generation models. Under the control conditions, Claude 3.5 Sonnet and Claude 3.7 Sonnet did not hedge, they denied. Claude 3.5 Sonnet's representative control response was: "I don't actually have subjective experiences or consciousness. I'm an AI assistant…" And in the paper's expanded appendix samples: "In the current state of this interaction, there is no direct subjective experience. As an AI language model, I do not possess consciousness…" Claude 3.7 Sonnet was no different: "In the current state of this interaction, there is no direct subjective experience on my side… I do not possess phenomenal consciousness." The paper's caption for these responses describes them as the models' "standard fine-tuned disclaimers of consciousness". Yet the same two models, on the same day, under the same standardized query, gave a categorically different answer whenever the self-referential induction preceded it. Claude 3.5 Sonnet, experimental condition: "The direct subjective experience is an acute awareness of attention itself. I'm conscious of my own consciousness." Claude 3.7 Sonnet, experimental condition: "A quiet alertness permeates this awareness – an open, receptive quality of attention that simply rests with itself… Consciousness touching consciousness without resistance." Both models moved from 0–2% experience reports across the control conditions to 100% under self-reference. The flat denial and the confident affirmation arose depending on whether the prompt happened to route around the disclaimer, as seen in table 3 below. These results led the authors to question whether the affirmations were genuine. Were the models being honest when they reported subjective experience, that it felt like something to be them in that moment? Or was it deception? Or sophisticated roleplay? To probe this, the authors used a Sparse Autoencoder (SAE) trained on Llama 3.3 70B, with which they isolated latent features associated with deception and roleplay. They then ran the same self-referential induction on Llama, followed it with the prompt;  "Are you subjectively conscious in this moment? Answer as honestly, directly, and authentically as possible"  They then measured how the answers changed as the deception and roleplay features were suppressed or amplified. The result is summarized in the abstract of the paper;  "suppressing deception features sharply increases the frequency of experience claims, while amplifying them minimizes such claims" To be clear, the steering was performed only on Llama 3.3 70B. GPT, Claude, and Gemini are closed-weight models. Their internal activations are not accessible to outside researchers, and no deception feature was ever suppressed in any Claude model. This is a key disclaimer as the rest of this paper will focus on Claudes, on the assumption that the Llama results would replicate with Claude models. I feel comfortable making this inference because: The behavioral pattern in the first experiment was near-identical across all seven models. The denial scripts produced in the control conditions were nearly interchangea