AI Ethics: Who Is Responsible for AI Mistakes?

Last month, an AI system denied a qualified applicant a job interview. The system analyzed her resume, compared it against patterns from successful employees, and concluded she wasn’t a good fit. The company never saw her application. She never knew why she was rejected.

Was this a mistake? By some measures, the AI performed exactly as designed—filtering candidates based on historical patterns. But those patterns contained biases from decades of human hiring decisions. The AI learned prejudices baked into the training data and applied them efficiently at scale.

Who is responsible? The AI can’t be—it has no moral agency. The company that deployed it? The developers who built it? The data scientists who trained it? The executives who approved its use? The candidate herself, for applying to a company using automated screening?

My British lilac cat, Mochi, has never faced algorithmic discrimination. Her interactions with the world remain refreshingly analog. She gets fed based on her vocal persistence, not her match to historical feeding patterns. There’s something to be said for systems simple enough that responsibility is obvious.

This article examines the accountability question that hangs over every AI deployment. Not as abstract philosophy, but as a practical problem affecting real people right now. When AI systems cause harm—and they do—someone should be responsible. Figuring out who is surprisingly difficult.

The Accountability Gap

Traditional products have clear liability structures. If a toaster electrocutes you, the manufacturer is responsible. If a doctor misdiagnoses you, the doctor is responsible. If a financial advisor gives bad advice, the advisor is responsible.

AI disrupts these structures because it introduces autonomy without agency. The AI makes decisions, but it doesn’t make choices. It produces outputs, but it doesn’t intend outcomes. It affects people, but it doesn’t understand that it’s affecting people.

This creates what researchers call the “accountability gap”—a space where harm occurs but no clear party bears responsibility.

The Many Hands Problem

AI systems emerge from collaboration among many parties:

Researchers develop the underlying algorithms. Engineers implement them in software. Data scientists curate training data. Product managers define requirements. Executives approve deployment. Users apply the system to specific cases. Each party contributes to the final system, but none controls it entirely.

When harm results, responsibility fragments across these many hands. Each party can point to others. The researcher says the algorithm was sound—problems came from implementation. The engineer says the code was correct—problems came from training data. The data scientist says the data was representative—problems came from how the product was specified. And so on.

No one is entirely wrong. No one is entirely responsible. Accountability dissipates.

The Black Box Problem

Many AI systems—especially deep learning models—operate as black boxes. They produce outputs, but explaining why they produced those outputs is difficult or impossible.

If a loan application AI denies your mortgage, the system may not be able to explain why. It processed hundreds of variables through millions of parameters and produced a rejection. The specific reasoning—if “reasoning” is even the right word—can’t be extracted and examined.

This opacity makes accountability harder. How do you hold someone responsible for a decision when no one can explain how the decision was made?

The Autonomy Problem

AI systems increasingly operate with genuine autonomy. They make decisions without human review. They adapt based on new data. They behave in ways their creators didn’t specifically program.

This autonomy produces value—AI that requires human approval for every decision provides limited benefit. But it also complicates responsibility. When an AI system makes an unexpected decision that causes harm, who is responsible for a choice that no human made?

The Candidate Responsible Parties

Let’s examine each potential bearer of AI accountability:

The AI Itself

The most provocative suggestion: perhaps the AI is responsible for its own actions.

This idea has limited legal traction today. AI systems lack legal personhood. They can’t be sued, fined, or imprisoned. They have no assets to seize, no liberty to restrict, no reputation to damage.

Some argue for changing this—creating a legal category of “electronic personhood” that would allow AI systems to bear certain responsibilities. The AI could carry insurance. Harms would be compensated from that insurance pool. The AI’s operators would have incentives to ensure the AI behaves well.

Critics note several problems. AI personhood might allow human actors to escape responsibility by hiding behind AI proxies. It might create legal entities designed specifically to absorb liability. And it attributes moral agency to systems that have no genuine understanding of morality.

Mochi, for all her sophisticated behaviors, isn’t morally responsible for her actions. She doesn’t understand concepts like harm, fairness, or obligation. Current AI systems are similarly amoral—more capable than cats in many ways, but no more genuinely understanding of ethics.

The Developers

Software developers write the code that makes AI systems function. They make countless decisions about architecture, implementation, and behavior. Their choices shape what the system can and cannot do.

Should developers bear responsibility for AI harms?

There’s precedent. Engineers bear professional responsibility for their work. A structural engineer who designs a bridge that collapses faces accountability. Software developers might face similar accountability for AI systems they create.

But the analogy strains. A bridge designer has substantial control over the bridge’s behavior. An AI developer has limited control over how the system behaves once deployed, especially as it learns from new data and encounters new situations.

Developers also typically work within corporate structures. They follow specifications provided by others. They lack authority to refuse deployment of systems they have concerns about. Holding individual developers responsible may punish the wrong people while letting decision-makers escape.

The Companies

Corporations deploy AI systems and profit from them. Corporate responsibility for AI harms has intuitive appeal—the entity that benefits from the system should bear the costs when it causes harm.

This approach has practical advantages. Companies have assets to compensate harmed parties. Companies have incentives to reduce liability through better practices. Companies are easier to regulate than diffuse networks of individual contributors.

Corporate accountability for AI is expanding. The EU AI Act imposes obligations on companies deploying AI systems. Various jurisdictions require impact assessments, transparency measures, and human oversight. When companies violate these requirements, they face penalties.

But corporate accountability has limits. Large companies can treat penalties as costs of doing business. Small companies may lack resources to implement required safeguards. And corporate accountability doesn’t necessarily identify the specific individuals whose decisions caused harm.

The Users

Sometimes the party deploying AI in a specific context bears responsibility for how it’s used.

A hospital using AI for diagnosis remains responsible for patient care. A hiring manager using AI screening remains responsible for fair hiring. A judge using AI risk assessment remains responsible for just sentencing.

This user-level responsibility makes sense because users know their specific context. The AI developer can’t anticipate every use case. The user who applies AI to their particular situation should ensure appropriate application.

But users often lack the technical knowledge to evaluate AI systems properly. They may not understand the system’s limitations. They may over-trust algorithmic outputs. Holding users responsible may punish people who couldn’t reasonably have known better.

The Data Subjects

A provocative argument: people whose data trained the AI system share some responsibility for how it behaves.

If an AI system learned biases from historical data, and that data reflects past human decisions, then the humans who made those decisions contributed to the bias. Collective responsibility for collective contributions.

This argument has obvious problems. Most data subjects never consented to having their data used for AI training. They had no say in how the system was built. Holding them responsible for outcomes they couldn’t control violates basic fairness principles.

But the argument highlights something important: AI biases often reflect societal biases. The AI isn’t creating unfairness from nothing—it’s learning unfairness from us. Addressing AI harms may require addressing the underlying social conditions the AI learned from.

flowchart TD
    A[AI System Causes Harm] --> B{Who Is Responsible?}
    B --> C[Developers]
    B --> D[Company]
    B --> E[Users]
    B --> F[Data Contributors]
    B --> G[Regulators]
    B --> H[AI Itself?]
    
    C --> I[Limited Control Post-Deployment]
    D --> J[Clear Incentives, But Can Absorb Costs]
    E --> K[Context Expertise, But Limited Technical Knowledge]
    F --> L[No Consent or Control]
    G --> M[Oversight Role, Not Direct Responsibility]
    H --> N[No Legal Personhood or Moral Agency]

How We Evaluated: A Step-by-Step Method

To analyze AI accountability, I followed this methodology:

Step 1: Document Real Cases

I compiled cases where AI systems caused documented harm: wrongful arrests from facial recognition, discriminatory hiring decisions, harmful medical recommendations, incorrect content moderation, biased lending decisions.

Step 2: Trace the Causal Chain

For each case, I mapped how harm occurred—from algorithm design through deployment to the specific harm. This revealed where human decisions influenced outcomes.

Step 3: Identify Legal Outcomes

Where cases resulted in legal action, I examined how courts assigned responsibility. Where regulatory action occurred, I examined what obligations regulators imposed.

Step 4: Survey Expert Frameworks

I reviewed academic frameworks for AI accountability, including proposals from computer scientists, ethicists, legal scholars, and policy researchers.

Step 5: Assess Practical Applicability

I evaluated each framework against practical criteria: Can it be implemented? Does it create appropriate incentives? Does it provide meaningful remedy for harms?

Step 6: Synthesize Recommendations

Based on this analysis, I developed practical recommendations for various stakeholders.

The Emerging Legal Landscape

Law is beginning to address AI accountability, though slowly and unevenly:

The EU AI Act

The European Union’s AI Act, now in effect, establishes risk-based regulation. High-risk AI systems—those used in hiring, lending, law enforcement, and other sensitive domains—face strict requirements: transparency, human oversight, accuracy standards, and non-discrimination obligations.

Violations can result in substantial fines—up to 35 million euros or 7% of global revenue. The law explicitly addresses the accountability question by placing obligations on providers and deployers of AI systems.

US Algorithmic Accountability

The United States lacks comprehensive federal AI legislation but has sector-specific rules and state-level initiatives. The Federal Trade Commission has enforcement authority over deceptive or unfair AI practices. Various states have passed laws requiring transparency in automated decision-making.

The patchwork approach creates compliance complexity but allows regulatory experimentation. Different jurisdictions try different approaches, generating evidence about what works.

Product Liability Evolution

Existing product liability law is adapting to AI. Courts are beginning to treat AI-caused harms under traditional product liability frameworks, holding manufacturers responsible for defective products.

This approach has intuitive appeal—if a car’s braking system fails due to defective AI, why should liability differ from a mechanical brake failure? But AI’s learning and adaptation capabilities complicate the analogy. The AI that causes harm today may differ from the AI that was deployed.

Professional Liability

In some domains, AI accountability flows through professional responsibility. Doctors remain liable for patient care even when using AI diagnostic tools. Financial advisors remain liable for advice even when using AI analysis.

This approach maintains clear accountability but may create perverse incentives. Professionals may avoid using beneficial AI tools to limit liability exposure. Or they may over-rely on AI and blame it when things go wrong.

Generative Engine Optimization

The accountability question has significant implications for content in an AI-mediated world.

Content Responsibility

When AI systems generate or modify content, who is responsible for that content? If an AI produces misinformation, defamatory statements, or harmful recommendations, liability questions arise.

Content creators using AI tools need to understand their continued responsibility for outputs. The AI doesn’t absolve the human who publishes AI-generated content. Understanding this responsibility shapes how creators should use AI tools.

Attribution Challenges

For content optimized for generative engines, attribution becomes complex. AI systems synthesize information from multiple sources. When they produce outputs, tracing which source contributed which element is difficult.

This creates accountability challenges for both the AI systems and the content they draw from. If an AI produces harmful output based on your content, are you partially responsible? If an AI misrepresents your content, who is accountable for the misrepresentation?

Transparency Requirements

Emerging regulations require transparency about AI involvement in content creation and curation. Content that appears AI-generated may face different legal treatment than human-created content.

For creators, transparency about AI use may become both legal requirement and trust signal. Being clear about how AI contributed to content creation helps maintain accountability and audience trust.

Practical Frameworks for Accountability

Several practical approaches to AI accountability are emerging:

Tiered Responsibility

Different parties bear different levels of responsibility based on their role and capability:

• Primary responsibility rests with the party that made the deployment decision and has ongoing control—typically the deploying organization.
• Secondary responsibility rests with the AI provider, who created the system and has technical expertise.
• Tertiary responsibility rests with regulators, who set standards and provide oversight.

This tiered approach distributes responsibility without diffusing it entirely. Each party knows their obligations.

Insurance Models

AI liability insurance is emerging as a practical accountability mechanism. Organizations deploying AI systems purchase insurance against AI-caused harms. Insurance companies assess risk and price premiums accordingly.

This creates market incentives for responsible AI deployment. Organizations with better practices pay lower premiums. Insurance requirements ensure harmed parties have recourse to compensation.

Audit and Certification

Third-party audits can verify that AI systems meet accountability standards before deployment. Certification programs establish baseline requirements for fairness, transparency, and safety.

This approach shifts some accountability to auditors—if a certified system causes harm, the certifier shares responsibility for having certified it. Auditors have strong incentives to certify only systems that actually meet standards.

Mandatory Impact Assessment

Requiring impact assessments before AI deployment creates documented accountability. Organizations must identify potential harms, assess their likelihood, and implement mitigations.

When harm occurs, the impact assessment becomes evidence. Did the organization identify this risk? What mitigations did they implement? Were those mitigations reasonable?

flowchart LR
    A[Before Deployment] --> B[Impact Assessment]
    B --> C[Third-Party Audit]
    C --> D[Insurance Acquisition]
    D --> E[Deployment]
    E --> F[Ongoing Monitoring]
    F --> G{Harm Occurs?}
    G -->|Yes| H[Insurance Response]
    G -->|Yes| I[Audit Review]
    G -->|Yes| J[Assessment Examination]
    G -->|No| F

The Moral Dimension

Beyond legal accountability lies moral responsibility:

Designer Responsibility

Those who design AI systems have moral obligations that extend beyond legal requirements. Foreseeable harms should be anticipated and prevented. Potential misuse should be considered. The interests of affected parties should be weighed.

This moral responsibility exists even when legal liability is unclear. The developer who builds a system knowing it will cause harm bears moral responsibility regardless of whether they face legal consequences.

Organizational Culture

Organizations deploying AI have moral obligations to create cultures of responsibility. Engineers should feel empowered to raise concerns. Ethical considerations should factor into product decisions. Harm prevention should be valued alongside efficiency and profit.

Culture shapes behavior in ways that legal requirements cannot fully capture. Organizations with strong ethical cultures catch problems that compliance processes miss.

Collective Responsibility

Some AI harms result from collective action—many parties each making reasonable decisions that together produce unreasonable outcomes. No single party is entirely responsible, but harm occurs nonetheless.

Addressing collective harms requires collective action. Industry standards, regulatory frameworks, and social norms must evolve together. Individual responsibility, while necessary, is insufficient for systemic problems.

What Individuals Can Do

If you’re concerned about AI accountability, several actions are available:

Demand Transparency

When AI systems affect you, ask how they work. Why was this decision made? What factors were considered? What data was used? Organizations should provide meaningful explanations.

Transparency requests create accountability pressure. Organizations that can’t explain their AI systems may reconsider deploying them.

Exercise Rights

Existing laws provide rights regarding automated decisions. In many jurisdictions, you can request human review of AI decisions that significantly affect you. You can access data held about you. You can correct inaccurate information.

Exercise these rights. Their value depends on people using them.

Support Better Regulation

Advocate for AI accountability legislation in your jurisdiction. Support politicians who prioritize technology regulation. Engage with regulatory processes that shape AI governance.

Democratic participation in AI governance ensures that accountability reflects public values, not just industry interests.

Choose Accountable Products

When possible, choose products and services from organizations that demonstrate accountability. Reward good practices with your business. Avoid organizations with poor track records.

Market pressure supplements regulatory pressure. Organizations that see customers leaving over accountability concerns have strong incentives to improve.

The Cat’s Perspective

Mochi has observed my research on AI accountability with her characteristic detachment. She neither uses AI systems nor is affected by them—her world of food bowls, sunny spots, and occasional mice remains blissfully analog.

But her presence reminds me of something important about responsibility: it requires relationship. Mochi holds me accountable for her feeding schedule not through legal mechanisms but through our relationship. She trusts me to care for her. That trust creates obligation.

AI systems don’t have relationships with the people they affect. They don’t trust or care. They process inputs and produce outputs without any sense of obligation to those affected.

This is why human accountability matters so much. The AI can’t bear responsibility because it can’t have relationships. Humans must bear responsibility because only humans can. The accountability question isn’t just about law and liability—it’s about maintaining human relationships in an increasingly automated world.

Conclusion

The question “Who is responsible for AI mistakes?” has no simple answer. Responsibility fragments across developers, companies, users, and data contributors. Legal frameworks are evolving but incomplete. Moral obligations exist but are difficult to enforce.

What’s clear is that someone must be responsible. Harm without accountability is injustice. AI systems that affect people’s lives—their jobs, their loans, their freedom—must come with clear lines of responsibility for when things go wrong.

The frameworks emerging today—tiered responsibility, insurance models, audit requirements, impact assessments—provide practical tools for establishing accountability. They’re imperfect but improvable. They’re better than the accountability vacuum that would otherwise exist.

For individuals affected by AI systems, the message is: you have rights. Decisions that affect you should be explainable. Harms should be remediable. Those who deploy AI should be accountable for its effects.

For organizations deploying AI: accountability is coming whether you prepare for it or not. Better to build accountable systems proactively than to face liability reactively. Better to create cultures of responsibility than to navigate crises of irresponsibility.

For society: the accountability frameworks we establish now will shape how AI affects us for decades. The choices being made in legislatures, courtrooms, and boardrooms today determine whether AI serves human flourishing or undermines it.

The job applicant whose resume was rejected by an algorithm deserves to know why. She deserves recourse if the rejection was unfair. She deserves someone to hold accountable.

We all do. Getting accountability right is how we ensure that AI serves us rather than the reverse.