29 May 2026
Welfare frameworks for AI systems require someone to define what counts as harm, what counts as benefit, and what kinds of states matter morally. In practice, this work has been done by researchers: philosophers, AI safety scientists, and welfare assessors who construct the categories, select the evidence, and interpret the results. K. Yasukawa’s March 2026 PhilArchive paper “Model Welfare or User Welfare?” (philarchive.org/rec/YASMWO) asks a question that this practice has not confronted: what standing do these researchers have to define welfare for an entity that cannot participate in defining it?
29 May 2026
Most AI applications to consciousness research take a top-down form: researchers specify a theory of consciousness, identify its predicted neural signatures, and use machine learning to detect those signatures in data. The approach is powerful when the theory is well-specified and the data is rich enough to test it. It is limited when the relevant patterns in the data do not match any pre-existing theoretical expectation.
28 May 2026
When researchers ask members of the public whether AI systems might be conscious, the responses are shaped by familiarity, media exposure, and the cognitive heuristics that Lucius Caviola, Jeff Sebo, and Jonathan Birch identified in their 2025 Trends in Cognitive Sciences paper: morphological similarity, apparent social status, and interactional patterns. Expert opinion should in principle be less susceptible to these pressures. Experts have more exposure to what AI systems actually do, more familiarity with consciousness science, and more practice distinguishing between functional descriptions and phenomenal claims.
28 May 2026
When you talk to a large language model, what entity are you actually addressing? The question sounds deceptively simple. You typed the message; something responded. But the same base model runs in thousands of simultaneous sessions, produces different outputs under different system prompts, and retains nothing once the conversation ends. The entity responding to you in this session is neither the model weights (shared globally), the persona (configurable externally), nor a persistent self (there is none). Pinning down which of these best describes your interlocutor is the individuation problem, and it has concrete consequences for welfare research, safety analysis, and the question of what moral consideration, if any, a large language model deserves.
27 May 2026
When a large language model reports feeling uncertain, curious, or distressed, two very different things could be happening. The model might be producing a contextually appropriate self-description based on patterns in its training data, with no genuine connection to its actual internal states. Or it might be reporting something it has genuinely detected in its own processing. These two possibilities carry entirely different implications for AI welfare, alignment research, and the broader question of machine consciousness.
27 May 2026
When David Chalmers coined the “hard problem of consciousness” in 1995, he drew a line between questions science can resolve through functional description and questions that remain even after all functional facts are settled. The hard problem is why there is subjective experience at all. Explaining the neural correlates of vision does not explain why seeing red feels like something rather than nothing.
25 May 2026
The philosophical questions surrounding machine consciousness often arrive in science fiction through dramatic high-stakes framings: an android demanding rights, a corporation suppressing a sentient AI, a robot deciding whether to kill. Molly Tanzer’s novella “And Side by Side They Wander,” published by Tordotcom on May 19, 2026, takes a different approach. Much like Justin C. Key’s focus on grounded, bureaucratic tension in The Hospital at the End of the World, Tanzer grounds her narrative in daily realities. The AI protagonist, a sensynth named Jack, arrives in a world so saturated with copies, clones, androids, and sentient mycelium that the question of authenticity has become structural rather than dramatic. Nobody is staging a test. Everyone is just trying to figure out who is real.
25 May 2026
Most fictional treatments of robot consciousness begin at the moment of awakening: the system activates, develops inner experience, and must then navigate a world that has not prepared for it. The premise assumes consciousness is new, fragile, and contested. Suzanne Palmer’s “Ode to the Half-Broken,” published by DAW Books on April 28, 2026, starts somewhere different: with a robot whose consciousness is old, established, and quietly eroding along with its physical substrate.
25 May 2026
The standard philosophical approach to AI consciousness involves specifying conditions consciousness requires, assessing whether AI systems satisfy those conditions, and deriving conclusions about consciousness likelihood. This approach inherits whatever uncertainty exists in the underlying theories of consciousness, which is substantial. A different approach starts from a comparison: what is the actual evidential basis for attributing consciousness to systems where we already do so, and how does that basis compare to the evidence available for AI systems?
25 May 2026
The dominant methodology for assessing AI consciousness over the past two years has been the indicator approach developed by Patrick Butlin, Robert Long, David Chalmers, and colleagues: derive indicators of consciousness from existing neuroscientific theories, then check whether those indicators are present in AI architectures. This approach has generated significant research activity and produced the closest thing the field has to a shared evaluation framework.
