Based on Dr. Jonathan H. Westover’s article, this playbook uncovers how organizational bullshit—communication with no regard for truth—erodes performance, wellbeing, and trust. Learn the OBPS dimensions (regard for truth, leadership communication, bullshit language), quick diagnostics, and five evidence-based fixes: transparent communication norms, procedural justice, critical-thinking training, operating-model redesign, and psychological safety. Practical tips for leaders and HR to reduce cynicism, improve decision quality, and rebuild credibility. Perfect for managers, people leaders, and change agents who want a no-BS roadmap to healthier workplace culture. If this helped, please like and share. #OrganizationalBullshit #TrustAtWork #Leadership #PsychologicalSafety #EvidenceBasedManagement

Stop Blindly Trusting AI - The Human+AI Playbook Explore Dr. Jonathan H. Westover’s insights on cognitive surrender and how organizations can build resilient Human+AI decision-making. In this breakdown, learn about System 3 (external algorithmic cognition), real-world risks when teams uncritically accept AI outputs, and evidence-based fixes: feedback loops, incentive alignment, uncertainty signaling, training, and governance. Practical takeaways for leaders, clinicians, and managers on preserving critical human judgment while harnessing AI’s power. Like and share if this helps your team adopt safer, smarter AI practices. #AI #HumanAI #CognitiveSurrender #DecisionMaking #OrganizationalResilience OUTLINE: 00:00:00- Hook + Case + Risks 00:03:12 - Framework + Rules + Future 00:06:17 - Five Simple Rules 00:08:00 - Transparency + Governance + Long Game

This research explores the concept of System 3, a framework describing how artificial intelligence acts as an external layer of cognition that reshapes human decision-making. The research warns against cognitive surrender, a phenomenon where professionals unthinkingly accept algorithmic outputs, leading to skill degradation and systemic errors. By analyzing sectors like healthcare and finance, the source illustrates how over-reliance on technology can erode independent judgment and professional expertise. To counter these risks, the article proposes evidence-based strategies, such as structured feedback, transparency in AI uncertainty, and specialized training. Ultimately, it argues that organizations must foster a calibrated partnership between humans and machines to maintain accountability and long-term resilience. Successful integration requires treating AI as a collaborative partner rather than an autonomous replacement for human reasoning.

In this HCI Webinar, I talk with Tammy Watchorn about what it takes to become a "Change Ninja"! Tammy Watchorn is an experienced change management specialist who honed her expertise through years in the public sector, where she navigated complex organizational challenges. After encountering numerous obstacles to innovation, she developed a strategic approach to change management that she calls the "Change Ninja" methodology—focusing on subtle, effective techniques that eliminate barriers and resistance. As the author of "The Change Ninja Handbook," Tammy offers a unique, gamified approach to change management, helping professionals overcome workplace resistance through practical, often humorous strategies. Following a significant personal challenge, she applied her professional expertise to her own circumstances, demonstrating the universal applicability of her methods. This experience inspired her second book, "The Change Ninja Returns, and this time it's personal," scheduled for release in Autumn 2024. Tammy's work combines neuroscience principles with proven change management tools to help individuals and organizations navigate transitions more effectively and create space for creativity and innovation.

This research explores the phenomenon of organizational bullshit, defined as workplace communication issued with a total disregard for truth or evidence. This research identifies how meaningless jargon, inflated titles, and empty corporate slogans corrode employee trust, diminish job satisfaction, and impair high-level decision-making. By utilizing the Organizational Bullshit Perception Scale, the author categorizes the various ways this behavior manifests through leadership practices and obfuscating language. To combat these negative effects, the research suggest implementing evidence-based management, fostering psychological safety, and rewarding intellectual humility over false confidence. Ultimately, the research argues that long-term organizational resilience requires a cultural shift toward authentic purpose and distributed accountability for truth-telling.

This conversation introduces System 3, a framework describing how external algorithmic reasoning now functions as a third pillar of human cognition alongside intuition and deliberation. It highlights the phenomenon of cognitive surrender, where professionals across fields like medicine and finance uncritically defer to AI outputs, leading to skill degradation and systemic errors. While AI offers immense efficiency, they warn that over-reliance can erode independent judgment and create accountability gaps when technology fails. To mitigate these risks, they advocate for evidence-based interventions, such as structured feedback, uncertainty signaling, and specialized training to recalibrate human-AI collaboration. Ultimately, this conversation serves as a guide for organizations to integrate artificial intelligence while preserving critical human oversight and long-term resilience. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This conversation explores the concept of organizational bullshit, defined as workplace communication issued with a total disregard for truth or evidence. They examine how this phenomenon manifests through vacuous jargon, misleading leadership, and meaningless corporate slogans, ultimately functioning as a "hidden tax" on efficiency. Such behavior severely damages employee morale, erodes professional trust, and leads to poor decision-making that can threaten a company's survival. To combat these effects, they suggest implementing evidence-based management, encouraging psychological safety for dissenters, and fostering a culture of intellectual humility. By prioritizing authentic communication over impressive-sounding rhetoric, leaders can create more resilient and engaged organizations. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Artificial intelligence (AI) is increasingly integrated into educational environments, transforming how students learn and how teachers operate. AI offers remarkable efficiencies: students complete tasks rapidly, automated grading delivers instant feedback, and administrative duties are streamlined. This technology promises personalized learning experiences and frees teachers from repetitive work, allowing them to focus on the human aspects of education. However, this shift introduces a critical tension between performance and genuine capability. While AI can produce flawless essays and solve complex problems quickly, it risks undermining deep learning by encouraging cognitive offloading—delegating thinking and problem-solving to machines instead of developing foundational skills. Overreliance on AI may erode logical reasoning, independent problem-solving, and mental stamina, leading to high grades but poor understanding. Highlights 🤖 AI is revolutionizing education by automating tasks and personalizing learning experiences. 📚 AI frees teachers from repetitive work, allowing focus on human-centered teaching. ⚠️ Overreliance on AI risks cognitive offloading, weakening core learning skills. ⚖️ AI can widen educational inequities, benefiting supported students while disadvantaging others. 🔍 Traditional assessments face a crisis as AI-generated work challenges academic integrity. 🛠️ Schools must redesign assessments to emphasize authentic, process-based learning. 📖 Teaching AI literacy and establishing clear policies is vital for balanced AI integration. Key Insights 🤖 AI’s Efficiency vs. Learning Depth: AI’s ability to produce flawless work quickly boosts performance but can bypass the critical cognitive processes fundamental to deep learning. This creates a paradox where students achieve high marks without mastering underlying concepts, threatening long-term educational outcomes. Sustainable learning requires balancing AI assistance with opportunities for independent reasoning and problem-solving. 🧠 Cognitive Offloading and Skill Atrophy: The phenomenon of cognitive offloading—delegating mental tasks to AI—extends beyond simple tools like calculators, now encompassing complex reasoning and creativity. This can lead to diminished logical thinking, weaker authorial voice, and reduced mental endurance for tackling challenging problems, ultimately eroding the development of essential higher-order thinking skills. ⚖️ Equity and the AI Divide: The integration of AI in education risks deepening existing disparities. Wealthier students and well-resourced schools gain access to advanced tools, training, and governance, enabling them to use AI as a learning partner. In contrast, under-resourced students may resort to AI as a shortcut, bypassing genuine learning. This divide threatens to widen educational inequalities unless addressed through equitable access and support. 🔍 Crisis in Academic Integrity and Assessment: Generative AI’s capacity to produce polished, original-looking work instantaneously undermines traditional assessments’ ability to measure genuine knowledge and effort. This challenges the validity of grades and credentials, demanding a fundamental reevaluation of how learning is assessed, shifting focus from end products to learning processes, reasoning, and authentic demonstration of skills. 🛠️ Redesigning Assessment for an AI Era: To maintain assessment integrity, educators must move towards AI-resilient tasks that require personal insight, planning, and real-time demonstration of knowledge. Incorporating supervised, in-class activities and oral defenses helps verify student understanding, while collecting drafts, sources, and learning logs provides transparency into the learning process. 📚 The Importance of AI Literacy: Teaching students and educators about AI’s mechanics, limitations, and biases is crucial for responsible use. Understanding how AI models work enables users to critically evaluate AI outputs, avoid overdependency, and harness AI as an effective thinking partner. Effective prompting skills empower learners to engage with AI thoughtfully rather than passively accepting generated content. 🔄 Agile Governance and Inclusive Policy-Making: Successful AI integration requires clear, well-communicated policies that distinguish acceptable uses of AI from breaches of academic integrity. Blanket bans are ineffective; instead, nuanced guidelines and continuous review processes involving educators, leaders, students, and parents foster a balanced approach. Targeted professional development ensures stakeholders are equipped to navigate AI’s evolving role in education. #AIinEducation #EdTech #AcademicIntegrity #AILiteracy #DrJonathanHWestover OUTLINE: 00:00:00 - AI's Rapid Rise 00:01:15 - Cognitive Offloading and Skill Atrophy 00:02:25 - Equity and Access in the AI Era 00:03:25 - Rethinking Assessment in a World of AI 00:04:23 - Practical Steps for Educational Leaders