ICML 2026 Past Other
ICML 2026 Workshop: Philosophy Meets Machine Learning
PhilML@ICML 2026
- Submission deadline
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- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (60)
Fetched from OpenReview (v2) on 2026-06-10.
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A Definition of Good Explanations and the Challenges Explaining LLM Outputs
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A Relativistic Perspective of Reliability in Machine Learning
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AI Review Is a Systemic Risk to Peer Review: Toward a Blockchain-Supported Claim-Level Ledger for Accountability
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AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs
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An Evolutionary Epistemology of Post-Training
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Articulate Intuition or Genuine Analysis? Benchmarking Epistemic Reliability in LLM-as-a-Judge Peer Review
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Before Normative and Moral Alignment: Causal Contract Faithfulness as a Precondition for Trustworthy AI
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Belief Without Justification: Sycophancy as a Single-Layer Truth–Compliance Tension in LLMs
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Beyond Accuracy: Epistemic Justification in Trustworthy Machine Learning
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Can LLMs Navigate Beliefs and Facts? Depends on How You Phrase It
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Can Standard MARL Metrics Distinguish Communicative from Strategic Action?
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Constituting What Counts: A Phenomenological Approach to Human-AI Ontological Translation
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Counterfactuals Without Worlds: When ML Counterfactual Explanations Are Ill-Posed
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DeepSWIP: Single-World Counterfactual Semantics for DeepProbLog
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Dignity as Answerability: How World-Model AI Reframes Human Moral Standing
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Do LLMs Really Represent the World? A Challenge from Teleosemantics
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Efficient Counterfactual Reasoning in ProbLog via Single-World Intervention Programs
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Epistemic Misalignment in Human-AI Systems: A Four-Quadrant Taxonomy of Uncertainty
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Explaining What Machine Learning Learns through Explainable AI
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Explanation for Whom? Hospitable Interpretability for Machine Learning
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Explanation in an Emerging Science of Large Language Models
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Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
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Factuality Beyond Reference in LLMs
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Fair Learning with Biased Labels: When Observed Accuracy Is the Wrong Target
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Fictionalism about Personas: Folk Psychology as an Interpretability Strategy
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From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models
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From Prompts to Proof Obligations: Formal Sidecars as an Epistemic Interface for Trustworthy ML
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Getting Monosemantic About Monosemanticity
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Interpretability Should Prioritise Use-Inspired Basic Research for AI Safety
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Lifted Representation Hypothesis in Language Models
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Measuring the Ruler: Reading Benchmark Saturation as Evidence
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Mistakes as Epistemic Signatures: An Efficiency-Modulated Cumulative Error Framework for Comparison and Diagnosis of AI Errors
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Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback
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On Epistemic Diversity in Large Language Models
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On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?
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Online Boundary-Aware Memory for Case-Based Reasoning Agents
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Operative Contexts: Belief Revision and Memory in Agentic AI
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Privileged Self-Access Matters for Introspection in AI
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Procedural Generalization: A Resource-Sensitive Account of Knowing-How
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Proleptic Epistemology for Societal Impacts of AGI
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Reality and Practice: A Relational Reading of the Platonic Representation Hypothesis
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Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
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Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models
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Reliable for Whom? Directional Reliability in AI-Mediated Political Dialogue
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Savage Without Monotonicity
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Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports
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The Concept of Representation in ML: Beyond Plato and Aristotle
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The Hawk Effect: Why We Need a Two-Dimensional Measure of Machine Intelligence
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The Opacity of Descent: Optimization, Epistemic Asymmetry, and the Semantics of Convergence in Deep Learning
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The Wrong Question? Artificial Consciousness and the Politics of AI Agency
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Towards Automated Evaluation of Socio-Technical Harms in LLMs: A Normative Taxonomy and Multi-Turn Red-Teaming Framework
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Towards Formalizing Skepticism of Autoregressive Language Models: A Taxonomy in the Language of the Theory of Computation
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Trust as Predictive Precision: Reliability and Influence in Representation Alignment
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Trustworthiness and co-cognition in artificial intelligence systems
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Uncertainty as Perceptual Testimony in Vision-Language Models
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Unsafe Consensus in Diagnostic Deliberation
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Vision-Language Asymmetry in Bistable Image Captioning
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When Do Transformer Components Compose? Validating a Log-Pool Decomposition Criterion
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Where Does Prediction Error Come From When the Data Is Perfect? A Decomposition of the Model–World Gap in Predictive Uncertainty
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Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models