What AI Can Teach Us About Ourselves

 

Philosophy of Explainability (Part 4)

What AI Can Teach Us About Human Cognition

Introduction: The Mirror Effect

We often approach AI as though it must catch up to human intelligence. But in the debate over explainability, the opposite happens: AI forces us to confront the limits of our own explanations. When we ask, “Why did the AI make that decision?” we realize that humans themselves rarely provide perfect, transparent explanations for their own actions.

In this sense, AI explainability is not just about making machines more human-like. It is about understanding the ways in which humans have always been machine-like: opaque, approximate, and narrative-driven in our reasoning.

Human Explanations: Post Hoc Stories

Cognitive science research reveals that much of human explanation is post hoc rationalization—stories we tell ourselves after the fact.

  • Split-brain studies show that when one hemisphere takes an action, the other hemisphere invents a plausible reason—even if it’s false.

  • Everyday psychology shows that when asked “Why did you do that?”, people often produce socially acceptable narratives rather than true causal accounts.

Humans, like AI, generate approximations of causality rather than full transparency. We are interpretability machines, not transparency machines.

AI’s Similar Tricks

AI explainability techniques echo this same pattern:

  • Saliency maps highlight features in an image that “influenced” classification, even though they are only approximations.

  • LIME (Local Interpretable Model-Agnostic Explanations) perturbs inputs to generate plausible reasons for outputs—just-so stories that help humans make sense.

  • SHAP values assign contributions to features, simplifying the messy internal interactions of millions of parameters.

In both humans and AI, explanations are narratives constructed for comprehensibility, not complete causal truth.

Case Study 1: Medicine and Heuristics

Doctors often make diagnostic decisions quickly, using heuristics (mental shortcuts) and intuition. Later, they provide detailed rationales to patients. But these rationales may not represent the true decision process—they are explanations crafted for trust and communication.

AI diagnostic tools do something similar: they crunch data at massive scale, then use interpretability techniques to produce explanations for human users. Both doctor and AI are storytellers as much as decision-makers.

Case Study 2: Jury Decisions

Juries are asked to give verdicts with reasons. But research shows that jurors’ actual decision-making is influenced by unconscious biases, group dynamics, and emotions. The reasons offered afterward are often rationalizations that fit the verdict rather than the true causes of it.

Here again, human reasoning resembles an AI black box with a human-friendly interpretability layer.

The Epistemic Lesson: Limits of Perfect Explanation

What AI reveals is that perfect explanations may be impossible—not just for machines, but for us too.

Philosophically, this challenges the Enlightenment ideal that knowledge should be fully transparent and rational. Instead, both human and machine cognition remind us that understanding is always:

  • Partial: never fully complete.

  • Contextual: shaped by audience and purpose.

  • Narrative-driven: filtered into stories that make sense for human comprehension.

The Moral Lesson: Humility and Shared Responsibility

If human explanations are also partial and constructed, then the ethical demand on AI should not be for perfection, but for alignment with human needs and values.

  • Humility: We should admit that our own minds are opaque, and avoid demanding impossible clarity from AI.

  • Shared responsibility: Explanations are collaborative—humans and AI co-construct meaning together.

This is what philosophers call epistemic humility: recognizing the limits of our knowledge while striving for better shared understanding.

What AI Can Teach Us About Ourselves

  1. Explanations are social: Both humans and AI tailor them to the audience.

  2. Explanations are approximate: They capture the gist, not the full mechanics.

  3. Explanations are moral acts: They are not neutral—they signal respect, fairness, and trust.

Thus, AI is not only an object of ethical regulation—it is a philosophical tool that reflects our own cognitive biases and explanatory habits back to us.

Critical Thinking Prompts

  • If humans can’t always explain themselves transparently, what should be the realistic standard for AI?

  • Should we prioritize explanations that are truthful but complex, or simplified but useful?

  • Could studying AI explainability lead us to rethink how education, law, and medicine handle human explanations?

Conclusion: The Philosophy of Mutual Explanation

The demand for explainable AI pushes us to a profound realization: explanation is not about exposing hidden truths, but about co-creating understanding.

AI does not simply lag behind human cognition—it mirrors it, with all its strengths and flaws. By studying AI, we learn that both humans and machines rely on partial, audience-relative, and narrative-driven explanations.

The challenge ahead is not to eliminate this imperfection but to harness it: to design explanations that respect dignity, foster fairness, and acknowledge the limits of both human and machine cognition.

In the end, explainability is not just about machines. It is about us.

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