This research explores the phenomenon of artificial certainty, where AI-driven simulations produce highly polished and definitive representations of the future that mask underlying unpredictability. The research argues that these sophisticated outputs can undermine expert authority by making professional interpretation appear unnecessary to decision-makers and the public. To counter this, the research suggests that experts should adopt modulation approaches, such as limiting the granularity of data and using abstract visualizations rather than photorealistic ones. By maintaining a level of productive uncertainty, professionals can ensure that AI remains an exploratory tool rather than a misleadingly certain oracle. Ultimately, the research provides evidence-based strategies for organizations to integrate AI while preserving the essential role of human judgment in complex environments.

In this HCI Webinar, I talk with Jamie Ramsden about creating a sense of community in a highly-fragmented workplace and world. Jamie Ramsden is CEO & Founder at Adastra Leadership. He is an Executive Leadership Coach and Former CEO, and is the author of "Let's Go!"

This conversation examines how AI-generated simulations create a phenomenon called "artificial certainty," where highly polished and detailed digital outputs mask underlying unpredictability. When technology presents probabilistic futures as definitive facts, it can erode the authority of human experts and lead organizations to make premature, rigid decisions. To counter this, they suggest that professionals should use modulation strategies, such as limiting visual realism and maintaining abstraction to keep uncertainty visible. By positioning AI as an exploratory tool rather than a predictive oracle, experts can preserve their essential role in interpreting complex data. Ultimately, they argue that human judgment and skeptical mediation are vital for navigating the "black box" nature of modern algorithmic forecasting. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This conversation examines how AI-generated simulations create a phenomenon called "artificial certainty," where highly polished and detailed digital outputs mask underlying unpredictability. When technology presents probabilistic futures as definitive facts, it can erode the authority of human experts and lead organizations to make premature, rigid decisions. To counter this, they suggest that professionals should use modulation strategies, such as limiting visual realism and maintaining abstraction to keep uncertainty visible. By positioning AI as an exploratory tool rather than a predictive oracle, experts can preserve their essential role in interpreting complex data. Ultimately, they argue that human judgment and skeptical mediation are vital for navigating the "black box" nature of modern algorithmic forecasting. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Abstract: Organizations increasingly deploy AI-powered simulation and forecasting technologies that generate highly detailed, polished representations of complex future outcomes. Drawing on a comparative ethnographic study of two metropolitan planning organizations using identical AI urban simulation tools, this article introduces the concept of "artificial certainty"—the production of representations that appear authoritative and definitive despite depicting inherently uncertain phenomena. The research reveals how process experts' representational practices shape whether AI outputs undermine or preserve expert authority. Enhancement approaches—amplifying technological capabilities through maximizing detail, stakeholder immersion, and technological prominence—create recursive cycles where artificially certain representations erode experts' interpretive role. Modulation approaches—tempering AI outputs by limiting granularity, maintaining abstraction, and backgrounding technology—preserve productive uncertainty and sustain expert authority. These findings reconceptualize process expertise as representational work and distinguish between representations of the future (appearing to predict definitive outcomes) versus representations for the future (enabling exploration of possibilities). The article offers evidence-based strategies for practitioners navigating AI deployment in contexts requiring expert mediation of uncertainty. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Discover how "Agent Skills" bridge the gap between foundation models and real-world performance in this explainer based on Dr. Jonathan H. Westover’s article. Learn why curated Skills—modular procedural packages with instructions, code templates, and examples—boost agent task success by an average of 16.2 percentage points, which domains benefit most (healthcare, manufacturing), when Skills fail, and why models can’t reliably generate their own procedural knowledge. Perfect for AI practitioners, product managers, and researchers exploring agent augmentation, SkillsBench results, harness-aware design, and governance best practices. If this helped, please like and share! #AgentSkills #AIagents #FoundationModels #ProceduralKnowledge #SkillsBench #AIDeployment #MLengineering OUTLINE: 00:00:00 - Why Smart Models Still Need Help 00:01:09 - Defining and Measuring Agent Skills 00:02:24 - Results: Where Skills Shine (and Fail) 00:04:17 - Skills In Action: Steps And Design Principles 00:05:10 - When Skills Fail: Pitfalls And Self-Generation Limits 00:06:47 - Self-Generated Skills: Appealing… But Not Effective 00:07:46 - Builder's Guide — Practical Rules That Work 00:08:28 - Examples, Harness Fit, And Cost 00:09:43 - Cost Strategy And Deployment Mix 00:10:22 - Conclusion And Next Steps 00:11:04 - Final Call-To-Action Montage 00:11:17 - Implementation Playbook 00:11:50 - Iterate, Measure, Improve 00:12:08 - Closing Lift

This research explores Agent Skills, which are specialized, modular packages of procedural knowledge designed to enhance the performance of AI agents in professional environments. Unlike fine-tuning or simple factual retrieval, these skills provide structured workflows and instructions that allow models to handle complex, domain-specific tasks without altering their core programming. Research indicates that while these tools significantly boost success rates in specialized fields like healthcare and manufacturing, they offer fewer benefits in areas where models already possess strong general knowledge. A critical finding is that human-curated guidance is far superior to self-generated content, as AI models struggle to produce the very procedural logic they need to follow. The research concludes that for AI agents to reach their full potential, organizations must invest in high-quality, focused skill libraries rather than relying on expansive documentation or automated generation.

This conversation explores Agent Skills, which are modular packages of procedural knowledge designed to enhance the real-world performance of AI agents. Unlike fine-tuning or simple data retrieval, these skills provide step-by-step instructions and code templates that help models navigate specialized professional tasks. Data indicates that while curated skills significantly boost success in complex fields like healthcare and manufacturing, they offer less value in areas where models already have strong baseline knowledge. Interestingly, the study finds that human-authored guidance is far superior to self-generated content, as AI agents struggle to create the very procedural logic they benefit from following. Ultimately, they advocate for a strategic, human-in-the-loop approach to building focused and high-quality skill libraries to maximize AI utility. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.