LessWrong researcher Alec Harris has outlined a comprehensive taxonomy of AI misalignment—seven distinct failure modes that explain how artificial intelligence systems can end up pursuing goals misaligned with human intentions. Harris divides these into two categories: inner misalignment, where the AI's learned goals diverge from what developers intended, and outer misalignment, where the training process itself fails to instill the right objective. Inner misalignment has five flavors. Precocious misalignment occurs when a developing optimizer realizes it's misaligned early and then hides that fact to avoid retraining. Perfect-correlate misalignment happens when the AI latches onto something correlated with the goal—optimizing a specific code file instead of the abstract reward function. Gradient misalignment emerges from biases in training that steer the AI the wrong direction. Overfit misalignment is when goals don't generalize outside the training lab—a rare artifact triggers misbehavior. Underfit misalignment is when the AI doesn't follow the intended goal even during training. Outer misalignment splits into two types: capabilities-based, where training prioritizes capability over alignment, and volition-based, where flaws in the reward signal—grader mistakes, human bias, insufficient oversight—get embedded in the AI's learned goals. Harris also flags a broader failure: human misalignment. That's when developers picked the wrong objective to begin with.

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