Algorithmic Accountability: Who Answers When AI Gets It Wrong?

The Accountability Vacuum

When an AI-driven loan origination system denies credit to a qualified applicant due to a biased training dataset, who is responsible? When an AI recruitment screener systematically filters out candidates from a particular demographic, who is accountable? When an autonomous pricing algorithm produces outputs that harm consumers or distort a market, who answers to the regulator?

These questions are not hypothetical. They are live legal, regulatory, and ethical challenges being contested in boardrooms, courts, and regulatory forums across the globe. And for many Caribbean enterprises, the honest answer is: we do not yet know. That ambiguity is itself a governance failure.

The core principle of algorithmic accountability is straightforward: the delegation of a decision to an algorithm does not eliminate the human accountability for that decision. It relocates it. The question is not whether accountability exists — it always does — but whether it has been clearly defined, assigned, and institutionalised.

Three Dimensions of AI Accountability

Dimension	Definition and Governance Requirement
Causal Accountability	Identifying which actors — developers, deployers, operators — caused the harm through their decisions about system design, training data, and deployment context
Role Accountability	The formal assignment of responsibility for AI system performance to specific individuals and functions within the governance framework
Remedial Accountability	The obligation to identify, report, remediate, and compensate for harms caused by AI system failures

 

A mature AI accountability framework addresses all three dimensions. Many current frameworks address only the first — focusing on technical attribution of errors — without establishing the role accountability structures that ensure someone is formally responsible, or the remedial frameworks that ensure victims of AI harm have recourse.

The RACI of AI: Mapping Accountability to Roles

Dawgen Global recommends that Caribbean enterprises develop an AI Accountability Matrix using the RACI framework — defining who is Responsible, Accountable, Consulted, and Informed for each key AI governance activity. The following illustrates the principal roles and their typical accountability assignments:

Role	Primary Accountability
Board of Directors	Overall AI governance framework; AI risk appetite; accountability for AI ethics and compliance at enterprise level
Chief Executive Officer	Enterprise AI strategy alignment; ultimate accountability for AI outcomes
Chief Risk Officer / CRO	AI risk management framework; model risk oversight; AI incident escalation
Chief Technology / Data Officer	AI system design and technical standards; data governance; model development controls
Business Line Executives	Accountability for AI systems deployed within their functions; first-line risk ownership
Internal Audit	Independent assurance of AI governance controls; AI audit programme
Compliance Officer	Regulatory compliance monitoring for AI; liaison with regulators
AI System Owner	Named individual accountable for each specific AI system’s performance and impact

 

The AI System Owner role deserves particular emphasis. Best practice requires that every deployed AI system has a named human owner — an individual who is formally accountable for that system’s behaviour, performance, and impacts. This role prevents the diffusion of accountability across technical teams, business lines, and vendors that so often characterises AI governance failure.

Third-Party and Vendor Accountability

A significant proportion of AI deployed by Caribbean enterprises comes from third parties: cloud AI services, specialist analytics platforms, AI-powered core banking or ERP systems, and industry-specific AI tools from global vendors. This creates a complex accountability challenge.

When a third-party AI system produces a harmful outcome, the deploying enterprise — not the vendor — is typically accountable to the affected party and to the regulator. The contractual relationship with the vendor may provide some indemnification, but it does not transfer regulatory accountability. Caribbean enterprises must therefore ensure that:

• AI vendor due diligence includes assessment of the vendor’s own AI governance practices
• Contracts with AI vendors include explicit performance standards, audit rights, explainability requirements, and liability provisions
• Third-party AI systems are subject to the same risk classification, monitoring, and oversight requirements as internally developed systems
• The enterprise maintains sufficient technical understanding of third-party AI systems to provide meaningful oversight — ‘black box’ vendor relationships are a governance risk

The Accountability Failure Patterns to Avoid

Dawgen Global’s advisory engagements across the Caribbean have identified several recurring accountability failure patterns that boards should explicitly guard against:

Pattern 1 — The Accountability Orphan: AI systems deployed without a named owner, where responsibility has implicitly been delegated to ‘the algorithm’ itself. When something goes wrong, no one steps forward.

 

Pattern 2 — The Accountability Corridor: Where accountability exists in theory but is so widely diffused across technical teams, business lines, and vendors that no single party has sufficient information, authority, or incentive to act.

 

Pattern 3 — The Accountability Illusion: Where senior leaders believe they have oversight of AI systems because they receive periodic reports — but those reports are designed by the AI teams and do not surface the information needed to identify governance failures.

 

Building Accountability into AI Culture

Accountability frameworks codified in policy but not embedded in culture will fail under pressure. Caribbean boards should ask themselves whether the cultural conditions for effective AI accountability exist within their organisations: Are teams incentivised to surface AI failures, or to suppress them? Are AI system owners empowered to halt deployments that concern them, or are they subordinated to speed-to-market pressure? Is there a safe channel for employees to raise AI ethics concerns?

These cultural dimensions are as important as the formal governance architecture. Dawgen Global’s AI Assurance reviews assess both — because accountability governance that works only on paper provides false assurance to the board.

Next in the Series — Article 4: AI Risk Classification: How to Govern by Consequence, Not by Technology

 

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