No learner learns from data alone. Before the first example arrives, a system must already prefer some explanations over others, and that preference decides what it can learn at all. This built-in leaning is not a flaw but a precondition.
The claim
Inductive bias is the set of assumptions a learner brings to its data. It narrows the space of hypotheses to those it is willing to consider. Without such narrowing, infinitely many explanations fit any finite evidence equally well.
Beneath the surface
The bias shows itself in architecture and objective. A design that assumes locality, or smoothness, or symmetry, thereby favors explanations that share those traits. What the model can express easily, it will tend to prefer.
A reframing
The bias is double-edged. The very assumptions that let a learner generalize from little data blind it to patterns those assumptions exclude. Every strength of prior is a corresponding refusal to see.
The trade-off
Bias fails when the world violates the assumption. A learner tuned for smooth structure flounders where reality is jagged, and no volume of data fully cures the mismatch. The prior is a bet, and bets can lose.
Where it breaks
The implication is that there is no view from nowhere. Learning is always learning-as-if, conditioned on commitments made before the evidence. To choose an architecture is to choose what kinds of truth are reachable.
The larger point
Inductive bias is the unavoidable prior that turns an ill-posed problem into a solvable one. It is both the source of generalization and the shape of blindness. Every learner sees the world through assumptions it did not learn. The principle rewards the patience to state it precisely and the humility to mark its limits. Precision reveals what it truly claims; humility reveals where it quietly fails. Between these two disciplines lies genuine understanding, which is never the possession of a conclusion but the grasp of why the conclusion holds and exactly how far it reaches.