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Infallibilism About Knowledge

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infallibilism certainty knowledge error

Core Idea

Infallibilism requires that knowledge entails infallibility or certainty—one cannot know a proposition one could be mistaken about. This aligns knowledge closely with certainty and aims to avoid the problem of easily-refutable knowledge claims. Infallibilists typically restrict knowledge to special domains (logic, mathematics, or self-knowledge) or radically limit what we can know about the world. The view struggles against the intuition that we do know empirical facts despite potential for error.

How It's Best Learned

Identify domains where infallibilism seems plausible (mathematics, logic, introspection) and domains where it fails (empirical science, perception). Discuss whether restricting knowledge to infallible domains matches our practices.

Common Misconceptions

Explainer

You've already studied the classic analysis of knowledge as justified true belief (JTB) and its problems. Infallibilism is a response to one underlying worry about that analysis: that justification admits of degrees and could always, in principle, fall short. If knowledge requires only "good enough" justification, the skeptic can always ask: but what if your evidence is deceiving you? Infallibilism eliminates that loophole by demanding that genuine knowledge be infallible — that knowing a proposition entail that one *cannot be mistaken* about it. If you could be wrong, you don't know.

This is a powerful and internally coherent position. It draws on a pre-theoretical intuition that knowledge and luck don't mix: if you just happened to believe something true despite having misleading evidence, that isn't knowledge. Infallibilism takes this intuition to its logical limit. Consider mathematical knowledge as the clearest example. If you've correctly proven a theorem, you cannot be mistaken about it (given the axioms and rules of logic). The proof *guarantees* the conclusion. Similarly, when you introspect and report "I am in pain right now," it's very hard to see how you could be wrong — your access to your own current mental states seems uniquely privileged and error-proof. These are the domains where infallibilism feels most plausible.

The problem emerges when you apply infallibilism to ordinary empirical claims: "There is a cup on this table," "It rained yesterday," "Water is H₂O." For nearly all empirical beliefs, the skeptical possibility looms: perceptual illusions, memory errors, systematic deception. Infallibilism seems to imply that you don't know any of these things — not because your evidence is weak, but because it's *logically possible* that you're wrong. This is where infallibilism and skepticism become entangled. The infallibilist doesn't want to say you know nothing about the external world; they typically want to restrict knowledge to domains where certainty is achievable (logic, mathematics, introspection, perhaps a narrow class of perceptual reports). But this restriction may exclude most of what we think of as the body of human knowledge.

The key distinction to keep sharp is between infallibility in the *logical* sense and certainty in the *psychological* sense. Infallibilism doesn't require that you feel certain — it requires that the truth of the proposition be *guaranteed* by your epistemic position. This is a much stronger claim. A person can feel absolutely certain about a false belief (zealots, victims of cognitive bias, people under hypnosis), and such psychological certainty clearly doesn't constitute knowledge. Infallibilism points in the other direction: not that feeling certain = knowing, but that knowing = being in a position where error is impossible. Understanding this distinction — between epistemic guarantee and subjective confidence — is essential for navigating the full landscape of epistemological views about justification, certainty, and the scope of knowledge.

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Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Introduction to Exponents → Order of Operations → Integer Order of Operations → Variable Expressions → The Distributive Property → Variables and Expressions Review → Introduction to Polynomials → Adding and Subtracting Polynomials → Multiplying Polynomials → Factorial → Permutations → Combinations → Counting Principles: Addition and Multiplication Rules → Introduction to Graph Theory → Propositional Logic Foundations → Logical Equivalences → Boolean Algebra → Introduction to Propositional Logic → Introduction to Predicate Logic (First-Order Logic) → First-Order Logic Syntax → Terms and Atomic Formulas in FOL → Variable Binding and Scope → Open and Closed Formulas in First-Order Logic → Variable Substitution and Capture-Avoidance in First-Order Logic → Quantifier Instantiation Rules in First-Order Proof Systems → Universal Quantification: Meaning and Scope → Free Variables and Bound Variables → Substitution and Instantiation in Predicate Logic → Terms and Atomic Formulas → Formulas and Well-Formed Expressions → Structures and Interpretations → Model Interpretation and Satisfaction → Interpretation, Truth, and Satisfaction of Formulas → Logical Consequence and Entailment → Soundness Theorem and Validity of Proof Systems → Deductive Reasoning and Formal Proof Systems → First-Order Resolution → Propositional Resolution → Semantic Tableaux (Propositional) → Semantic Tableaux (First-Order) → Decidable Fragments of First-Order Logic → Gödel's Completeness Theorem for First-Order Logic → Gödel's Incompleteness Theorems → Introduction to Intuitionistic Logic → Introduction to Modal Logic → A Priori and A Posteriori Knowledge → Infallibilism About Knowledge

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