Extending the UXR Point of View Pyramid: A Generative AI-Augmented Methodology for Human-Centred AI Systems

AI systems in financial services require specialized research frameworks: The paper demonstrates that existing User Experience Research methods were insufficient for AI-driven debt management systems, necessitating new approaches that specifically account for algorithmic opacity, fairness concerns, and accountability—highlighting that responsible AI deployment demands tailored evaluation methodologies beyond standard product design practices.

Vulnerable populations face heightened risks from opaque financial AI: The research emphasizes that UK households experiencing cost-of-living pressures and rising debt are increasingly dependent on AI systems for credit assessment and debt support, yet these systems often lack transparency, underscoring the need for human-centered design that protects those most affected by algorithmic decisions.

Generative AI should support rather than replace human judgment in high-stakes decisions: The framework positions AI as an "epistemic support mechanism subject to human validation" rather than an autonomous decision-maker, suggesting that for consequential financial decisions, AI tools should augment human expertise while maintaining traceability and oversight rather than automating critical judgments.

Regulatory awareness and ethical oversight must be embedded in research workflows: The paper advocates for integrating regulatory constraints and ethical considerations directly into the design process for AI financial systems, rather than treating compliance as a separate concern, reflecting a shift toward proactive responsible AI governance.

Traceability and transparency are essential for accountability: The AI-enabled Playbook Card system described in the research prioritizes maintaining clear documentation of how insights were generated and decisions were made, addressing the "black box" problem that undermines trust and accountability in financial AI systems.