Governance of Ethical and Trustworthy AI Systems: Research Gaps in the ECCOLA Method

ECCOLA method supports corporate governance but has gaps in data and information governance: While the method successfully enables AI governance at the corporate level across all processes, it falls short in fully supporting data governance and information governance practices—two critical areas for ensuring AI systems handle information responsibly.

Existing AI development methods need strengthening for ethical practices: The research reveals that current development frameworks like ECCOLA, while promising, require further improvement to adequately address ethical concerns, particularly in sensitive sectors like healthcare, transportation, and security where AI decisions directly impact public safety and trust.

Governance frameworks must address multiple layers of responsibility: Effective AI governance requires attention to corporate practices, data handling, and information management simultaneously—a finding that suggests organizations need comprehensive, multi-faceted approaches rather than single-focus solutions.

Gap between AI advancement and governance practices persists: Despite rapid improvements in machine learning technology and widespread AI adoption across sectors, governance practices have not kept pace, indicating that development methods need to evolve faster to address emerging ethical concerns.