Artificial Intelligence (AI) has gone from science fiction to daily reality. From recommendation engines and voice assistants to medical diagnostics, autonomous vehicles, and job alert systems, AI systems make decisions that affect our lives. But what happens when AI gets it wrong?

Whether it’s a biased hiring algorithm, a facial recognition system misidentifying people, or a chatbot generating harmful content, and job alerts and medical diagnostics errors, AI mistakes raise pressing questions. AI ethics is about resolving mistakes and preventing them from happening again.

Here’s how AI and system mistakes can be resolved through ethical principles, robust systems, and human oversight.

1. Acknowledge the Mistake: Transparency and Accountability

The first step to solving any problem is admitting it exists. In AI ethics, this means:

• Transparent disclosures: Companies should clearly communicate when their AI systems fail, what went wrong, and who is responsible.
• Accountability mechanisms: Ethical AI frameworks promote assigning accountability—not to the AI—but to the developers, designers, and organizations that deploy it.

2. Corrective Action: Redesign and Retrain

Once a mistake is identified, ethical AI development demands corrective action:

Explainability: Understanding why a model made a certain decision can guide effective fixes.

Bias audits: Regular assessments of datasets and algorithms to detect and correct biases.

Model retraining: Updating models with better, more representative data.

3. Ethical Governance: Preventing Future Errors

Prevention is better than cure. Ethical AI governance puts safeguards in place to reduce the chances of AI mistakes:

• Fairness and ethics checklists: Tools to ensure developers consider ethical implications at every stage.
• Continuous monitoring: Real-time feedback systems to catch errors early.

5. Human + AI = Teaming or not, better together

AI isn’t infallible, but neither are humans. The key is collaboration:

• Augmented intelligence: AI should support—not replace—human decision-making.
• Human oversight: Critical decisions (e.g., in healthcare or justice) must include human review.

6. Restoring Trust: Redress and Fair Compensation

When AI errors impact people—through missed opportunities or medical risks—ethics demands remediation:

Compensation or support: Where AI errors cause harm, affected parties should receive fair treatment or restitution.

Clear user feedback channels: Let users report issues easily.

Is That All? Why Classical Ethics Still Matters in Fixing AI Mistakes

So, AI systems make mistakes. We audit the data, retrain the models, update the code, issue a patch… problem solved, right?

Is that all?

Not quite. Resolving AI mistakes isn’t just about fixing lines of code or tweaking algorithms—it’s about confronting moral failures. It’s about how we decide what’s right, who is responsible, and how we restore trust.

This is where classical ethical theory steps in—not as an academic exercise, but as a practical guide to action. These time-tested frameworks help us navigate the grey areas of AI errors, offering principles to evaluate harm, uphold rights, and foster accountability.

Let’s break it down.

Utilitarianism – What’s the Greatest Good?

When a medical expert system misses a life-threatening alert, utilitarian ethics demands immediate action to minimize harm and maximize benefit. That means not only fixing the system but also compensating affected patients and improving future detection across the board.

Utilitarianism asks:

• How can we prevent the greatest number of future errors?
• What outcomes serve the well-being of the most people?

In AI ethics, utilitarian thinking fuels cost-benefit analyses and impact assessments—but it’s not just about numbers. It’s about using outcomes to guide justice.

Deontology – What are our non-negotiable Duties?

When a job alert AI filters out candidates based on biased criteria, deontological ethics steps in. It doesn’t matter if fixing the system is expensive or time-consuming—fairness and rights are non-negotiable.

Deontological principles remind us:

• Some actions are simply wrong—like denying equal opportunity.
• Developers have a duty of care to users, and organizations have a moral obligation to ensure AI respects autonomy, fairness, and dignity.

Here, rights-based governance and ethics-by-design aren’t optional—they’re moral imperatives.

Virtue Ethics – Who Do We Want to Be?

AI mistakes reveal more than technical flaws—they reveal the character of the people and companies behind them. Virtue ethics challenges us to act with integrity, empathy, and fairness.

When a system fails, do we hide it or own it? Do we engage affected users with compassion—or legalese? Virtue ethics says:

• Ethical behavior comes from ethical people.
• Organizations must cultivate a culture where doing the right thing is embedded, not enforced.

Classical ethics isn’t outdated—it’s the moral engine that powers responsible AI. It teaches us that resolving AI mistakes isn’t just about getting systems back on track—it’s about getting values back in alignment.

If AI is shaping our future, classical ethics ensures we stay human while we build it and more so when dealing with AI bias and mistakes.