Maintaining a consistent narrative across multiple model interactions helps build long-term user trust.
The Architecture of Consistency: Why Narrative Continuity is the Foundation of AI Trust Introduction We live in an era where…
April 2026
Cognitive biases, such as the framing effect, influence how users interpret probabilistic explanations.
The Architecture of Choice: How Cognitive Biases Shape Our Understanding of Probability Introduction Every day, we are bombarded with probabilistic…
User-centered design methodologies prevent the creation of explanations that are technically sound but practically useless.
Outline Introduction: The disconnect between technical accuracy and user utility. Key Concepts: Defining User-Centered Design (UCD) as an empathy-driven bridge…
Collaborative sense-making between human and AI improves outcomes in high-stakes environments like medicine.
The Symbiosis of Intellect: Why Collaborative Sense-Making Between Humans and AI is the Future of High-Stakes Decision Making Introduction In…
Explanations should not substitute for rigorous safety testing and validation of the primary model.
Outline Introduction: The “Black Box” illusion and the danger of relying on interpretability (XAI) as a safety proxy. Key Concepts:…
Auditing explanations for accuracy is difficult when the underlying model is a complex”black box.”.
The Black Box Dilemma: Auditing Machine Learning Explanations for Accuracy Introduction As machine learning models become the architects of critical…
Feedback loops between users and designers help refine the granularity of model disclosures.
The Feedback Loop: Refining Model Disclosures Through User-Designer Collaboration Introduction In the rapidly evolving landscape of artificial intelligence, the gap…
False justifications can appear highly authoritative if they adhere to established domain-specific jargon.
The Authority Trap: How Jargon Masks False Justifications Introduction In an era defined by information density, we are conditioned to…
Users require information about the “why” and “why not” of a prediction to gain actionable insights.
The Why and Why Not: Turning Predictive Analytics into Actionable Intelligence Introduction In the modern data-driven landscape, organizations are flooded…
Users are more likely to accept an explanation if it matches their intuitive understanding of the causal process.
Outline Introduction: The psychology of believability and the “Causal Gap.” Key Concepts: Defining intuitive causal models versus cognitive load. Step-by-Step…