Webinar Description
Key Takeaways
- Examines how audit, risk, and governance leaders can maintain human accountability as AI increasingly influences organisational decisions
- Addresses the challenge of building defensible evidence trails for AI-influenced decisions that may face scrutiny
- Explores practical approaches to AI oversight that go beyond compliance checklists
- Relevant to chief audit executives, chief risk officers, IT leaders, and board members across regulated industries
- Hosted by Diligent Corporation as a live virtual webinar with expert-led discussion and Q&A
Introduction
As artificial intelligence becomes embedded in business operations, a fundamental question emerges for governance professionals: when an AI-influenced decision goes wrong, who bears responsibility? The webinar “Governance Gets Personal: Human Judgement in the Age of AI” confronts this challenge directly, offering audit, risk, and compliance leaders practical frameworks for maintaining meaningful human oversight in an era of algorithmic decision-making. Hosted by Diligent Corporation, this virtual session arrives at a moment when regulatory expectations around AI accountability are intensifying and boards are demanding greater assurance that AI-driven processes remain under appropriate human control.
About This Event
This live webinar brings together governance professionals to examine the intersection of artificial intelligence and human accountability within organisational decision-making. The session is designed as an expert-led discussion incorporating research-backed insights and practical guidance, followed by a Q&A segment allowing attendees to address specific challenges within their organisations.
The format reflects the executive-level audience the event targets, prioritising actionable frameworks over theoretical discussion. Rather than focusing on AI technology itself, the webinar centres on the governance structures, evidence requirements, and oversight mechanisms that organisations need when AI contributes to consequential decisions.
The Accountability Challenge in AI-Influenced Decisions
The central premise of this webinar is straightforward but increasingly urgent: accountability for AI-influenced decisions ultimately rests with the people expected to govern them. This creates a significant challenge for audit and risk leaders who must provide assurance over processes they may not fully understand, using evidence trails that traditional governance frameworks were never designed to capture.
When organisations deploy AI systems that inform or automate decisions—whether in credit approvals, resource allocation, risk assessment, or operational planning—the outputs may appear authoritative while masking underlying uncertainties. Governance professionals face the task of distinguishing between genuinely valuable AI outputs and what might be termed misleading confidence: results that seem definitive but rest on flawed assumptions, biased training data, or inappropriate application.
The webinar addresses how organisations can establish what it terms “defensible oversight”—governance mechanisms that go beyond surface-level compliance checks and polished outputs to demonstrate genuine human judgement in the decision-making process. This distinction matters considerably when decisions face external scrutiny from regulators, auditors, or stakeholders seeking to understand how conclusions were reached.
Building Evidence Trails for AI Governance
One of the practical challenges the session explores is how organisations can document and evidence human judgement within AI-influenced processes. Traditional audit trails capture inputs, approvals, and outcomes, but AI introduces intermediate steps that may be opaque or difficult to interrogate. When a model contributes to a decision, governance frameworks need to capture not just what the AI recommended, but how humans evaluated that recommendation, what additional factors they considered, and why they chose to accept, modify, or reject the algorithmic output.
This evidence requirement becomes particularly important in regulated industries where decisions affecting customers, patients, or the public may be subject to review. Financial services firms, healthcare organisations, and public sector bodies increasingly face expectations that they can explain and justify AI-influenced decisions—not merely demonstrate that a process was followed, but show that appropriate human judgement was applied at critical points.
The webinar aims to provide guidance on building these evidence trails without requiring organisations to dramatically expand their governance teams. The focus is on identifying which decisions warrant intensive oversight and documentation, allowing resources to concentrate where accountability risks are highest.
Converging Risk Domains
AI governance does not exist in isolation from other risk management disciplines. The session addresses how AI oversight intersects with cyber risk and operational risk management—domains that governance leaders are already responsible for but which take on new dimensions when artificial intelligence is involved.
AI systems introduce cyber risk through their reliance on data pipelines, model integrity, and integration with broader IT infrastructure. They create operational risk through potential failures, unexpected behaviours, or dependencies on third-party AI services. Governance frameworks that treat AI as a separate category may miss these interconnections, while frameworks that integrate AI into existing risk taxonomies can leverage established oversight mechanisms.
For audit and risk leaders, this convergence means that AI governance cannot be delegated entirely to technology teams. The business implications of AI-influenced decisions—and the accountability when those decisions prove problematic—remain with governance functions that must understand enough about AI capabilities and limitations to provide meaningful oversight.
Establishing Board-Level Confidence
Boards and senior stakeholders increasingly ask governance leaders to provide assurance that AI is being used responsibly within their organisations. This creates pressure to demonstrate oversight without necessarily having deep technical expertise in machine learning or data science.
The webinar explores how governance professionals can translate technical AI processes into board-ready confidence—assurance that is meaningful to directors who need to understand risk exposure and accountability structures without requiring them to evaluate model architectures or training methodologies. This translation function is becoming a core competency for chief audit executives and chief risk officers as AI adoption accelerates across industries.
Effective board reporting on AI governance requires clarity about which decisions involve AI, what controls exist around those decisions, how human judgement is incorporated and evidenced, and what mechanisms exist to detect and respond to AI failures or unexpected outcomes. The session aims to help attendees structure this reporting in ways that satisfy board-level accountability requirements.
Who Should Attend
This webinar is designed for mid to senior-level leaders responsible for audit, risk, compliance, IT governance, and internal controls. Chief audit executives and chief risk officers will find the content directly relevant to their oversight responsibilities, while heads of IT and governance managers will benefit from the practical frameworks for evidencing human judgement in AI-influenced processes.
Board members seeking to understand their accountability exposure as AI adoption increases within their organisations may also find value in the session’s approach to translating technical AI governance into board-level assurance. The content applies across industries, though organisations in regulated sectors—financial services, healthcare, energy, and public administration—face particularly acute pressure to demonstrate defensible AI oversight.
Attendees should expect an executive-level discussion that assumes familiarity with governance fundamentals while providing new frameworks for addressing the specific challenges that AI introduces to traditional oversight models.