Explainability as a Moral Imperative

 

Philosophy of Explainability (Part 3)

Explainability as a Moral Imperative

Introduction: Why Ethics Enters the Room

We could treat explainability as a technical challenge—something for engineers and data scientists to solve. But the stakes are higher. When AI systems deny a loan, recommend prison sentences, or decide who receives scarce medical resources, explanation is not optional. It is a moral imperative.

Ethics enters the room because explainability shapes autonomy, fairness, and accountability. To refuse explanation is to deny people the ability to contest, understand, or influence the decisions that affect their lives.

Philosophical Grounding: Duties vs. Consequences

Philosophers approach moral responsibility in two dominant traditions:

  • Deontological duty (Kantian ethics): People must be treated as rational agents, capable of understanding reasons. If an AI affects you, you deserve an explanation because it is a matter of respect.

  • Consequentialist ethics (Utilitarianism): Explanations prevent harm and maximize well-being. They allow errors to be corrected, biases to be identified, and trust to be built.

Either way, the conclusion is the same: Explainability is not just nice-to-have—it is an ethical requirement.

Case Study 1: Medical AI

Imagine an AI system recommending treatments for cancer patients.

  • If the AI suggests Chemotherapy A over Chemotherapy B, patients and doctors deserve to know why. Was it based on tumor size? Genetic markers? Statistical patterns in training data?

  • Without explanation, trust collapses. Patients may feel like objects in a machine, not autonomous agents.

Here, explainability directly supports the ethical principles of informed consent and patient autonomy.

Case Study 2: Criminal Justice

The COMPAS algorithm (used in U.S. courts to assess re-offense risk) notoriously misclassified Black defendants as higher risk.

  • The ethical question wasn’t only Was it accurate? but Could its reasoning be explained and challenged?

  • Because the system was opaque and proprietary, defendants could not meaningfully contest its judgments.

This is an affront to the principle of procedural justice—the idea that fairness is not only about outcomes but also about the fairness of the process.

Case Study 3: Finance and Everyday Life

Credit scoring, hiring algorithms, and insurance models quietly shape everyday opportunities.

  • When a job candidate is rejected by an AI resume screener, explanation ensures the decision is not arbitrary or discriminatory.

  • Without it, individuals are reduced to data subjects, stripped of their agency to contest or improve.

Philosophically, this links to Rawls’ theory of justice: social systems must be structured so that the least advantaged are not unfairly burdened. Without explainability, hidden biases persist and amplify inequality.

Beyond Compliance: The Deeper Ethical Stakes

Some companies treat explainability as a compliance checkbox—a way to satisfy regulations like the GDPR “right to explanation” in Europe. But philosophy pushes us deeper:

  • Accountability: Explanations make it possible to assign responsibility when things go wrong.

  • Fairness: Explanations reveal whether groups are being systematically disadvantaged.

  • Dignity: Explanations affirm the humanity of individuals by acknowledging their right to reasons.

To neglect explainability is not only risky but dehumanizing.

The Risk of “Explanation Theater”

But there is a danger: explanation theater—offering superficial, polished explanations that look ethical but mask the underlying opacity.

For example:

  • A hiring AI tells rejected candidates: “You were not selected because your qualifications didn’t meet requirements.”

  • This seems interpretable but hides the reality: the model used biased proxies, such as the candidate’s ZIP code or alma mater, which correlated with socioeconomic background.

Ethically, this is worse than no explanation at all—it creates an illusion of fairness.

The Standard of Moral Sufficiency

So what makes an explanation morally sufficient? We can set three ethical tests:

  1. Comprehensibility: Is it pitched at a level the affected person can reasonably understand?

  2. Contestability: Does it give the person enough information to question, appeal, or improve their standing?

  3. Traceability: Can independent auditors verify that the explanation aligns with the actual workings of the system?

If an explanation fails any of these, it is ethically deficient.

A Philosophical Twist: Do Humans Owe Explanations Too?

AI forces us to reflect on ourselves. We expect explanations from machines—but how often do humans explain their decisions?

  • Judges give reasoning for rulings, but often with room for interpretation.

  • Doctors may simplify explanations, balancing truth with compassion.

  • Hiring managers rarely disclose all reasons for rejecting a candidate.

Philosophy reminds us: Explainability is a universal ethical practice, not just a technical AI issue. If we demand it from machines, we must also demand it from ourselves.

Critical Thinking Prompts

  • Is explainability always required, or are there cases where withholding explanation is justified (e.g., for security reasons)?

  • Should AI explanations prioritize accuracy or human comprehensibility, if the two conflict?

  • How do we guard against “explanation theater” while still simplifying complex systems?

Conclusion: Explainability as a Safeguard of Human Dignity

The moral case is clear: explainability is not a luxury—it is a safeguard of dignity in the algorithmic age.

  • Without explanations, AI risks becoming a machinery of hidden power.

  • With explanations, we affirm respect for persons, ensure fairness, and create the conditions for trust.

Philosophically, explainability is where ethics meets epistemology: we owe reasons not because they are easy to give, but because they are essential to living together as rational, autonomous beings.

👉 Next up is Article 4: What AI Can Teach Us About Human Cognition, which flips the lens—arguing that in studying AI’s limits of explainability, we learn something profound about the limits of our own minds.

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