Accountability in AI: From Principles to Industry-specific Accreditation

The AI accountability system is currently unbalanced and fragmented. The paper identifies that different stakeholders (companies, regulators, users) require different accountability mechanisms, but the present ecosystem lacks coordination—particularly in transparency, documentation, and formal processes needed for internal audits and external accreditation.

Transparency and explainability are critical gaps. The authors argue that improved AI explainability and adequate documentation are essential prerequisites before external accreditation processes can be effective, suggesting these foundational elements are currently insufficient across the industry.

Industry-specific accountability matters. Using the gambling sector as a case study, the paper demonstrates that generic AI principles are insufficient; different industries face distinct risks and require tailored accountability approaches, particularly around algorithmic bias and model explainability.

Algorithmic bias and explainability remain inadequately addressed in practice. The gambling industry case study critically evaluates how key accountability principles are actually implemented, revealing gaps between stated commitments and real-world practice in addressing bias and ensuring models can be understood and audited.

A pathway toward external accreditation is needed. The paper proposes a progression from improved internal documentation and audit processes toward formal external accreditation systems, suggesting this structured approach could help move accountability from principles on paper to enforceable standards.