AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 6 Jul 2026] Title:How to Navigate Uncertainty About AI Consciousness View a PDF of the paper titled How to Navigate Uncertainty About AI Consciousness, by Dr Tom McClelland View PDF Abstract:Given deep uncertainty about the possibility of artificial consciousness, it is unclear how we should treat potentially sentient AI. On the one hand, we could assume insentience but risk doing terrible harms to entities that deserve moral standing. On the other hand, we could assume sentience and instead risk wasting resources on insentient machines. The intractability of questions around AI consciousness mean that this dilemma is hard to escape. I suggest a way out of that shifts from intractable questions of AI consciousness to tractable questions of AI valence. Specifically, we can assess whether an AI has states that would constitute valenced experiences if it were conscious. I show how this is sufficient to ground a responsible approach to the development of potentially conscious AI. Comments: 8 Pages, Proceedings of AISB 2026 Symposium on AI, Consciousness and Ethics Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.19215 [cs.AI] (or arXiv:2608.19215v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.19215 arXiv-issued DOI via DataCite Submission history From: Tom McClelland Dr [view email] [v1] Mon, 6 Jul 2026 10:37:36 UTC (264 KB) Full-text links: Access Paper: View a PDF of the paper titled How to Navigate Uncertainty About AI Consciousness, by Dr Tom McClelland View PDF view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)