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