Cognitive processes in technology trust development

As AI becomes more embedded in how we work and make decisions, an important question is emerging: Are we becoming more efficient or are we quietly changing how we think? I’m pleased to share my latest peer-reviewed paper: “Beyond Efficiency: A Qualitative Exploration of Human Agency, Epistemic Vigilance, and Cognitive Boundaries in Human–AI Interaction.” This study was co-authored with Dr Jonathan Ee, PhD, a clinical psychologist, researcher and academic leader, drawing on qualitative interviews with professionals across psychology, technology and leadership. This work builds on my earlier research on Human-Centred AI design developed during my MSc in Psychology, moving from how we design AI to how people experience and trust it and now to how AI is reshaping human cognition and agency. Some key insights from the research: - AI significantly reduces cognitive load and accelerates tasks but shifts effort away from thinking towards monitoring and verification, with cognitive boundaries becoming more fluid as individuals negotiate what to think through themselves and what to delegate - Human agency must be actively maintained through deliberate judgement and oversight - Epistemic vigilance, the ability to question and scrutinise information, becomes critical when interacting with AI outputs - There is a growing risk that overreliance on AI may weaken creativity, critical thinking and sense of ownership over decisions A central finding is that AI does not replace human cognition. It reshapes it. This creates a new responsibility. Not just to use AI effectively but to remain the primary thinker in the process. For organisations, this has clear implications for workflows, governance and training. Efficiency alone cannot be the goal. We must also preserve human judgement and cognitive capability. Full paper here: https://lnkd.in/gf4VM_fC My other publications are available at www.techsynapse.ai I’d be interested in your perspective: How do you decide what to rely on AI for and what you need to think through yourself? #AI #HumanAIInteraction #HumanCentredAI #Psychology #CognitiveScience #ResponsibleAI #TrustInAI #AIethics #BuildingbetterAI #TechSynapse.ai

🔬 Paper Alert: Trust in AI is not built in isolation – it’s social. 🤖 Proud supervisor moment: My (and Simon B. de Jong´s) doctoral student Türkü Erengin has just published her very first paper, "You, Me, and the AI: The Role of Third-Party Human Teammates for Trust Formation Toward AI Teammates," in Wiley´s Journal of Organizational Behavior. 🤖 So, what does this research tell us? AI teammates are becoming a reality in modern workplaces. But while research has focused on how humans individually evaluate AI, Türkü’s work brings a fresh perspective: trust in AI is shaped severely by and learned from the people around us. Using two main (+ two supplementary) studies including a really cool observational, incentivized study with human-AI teams (including real GPT-powered physical service robot Temi, see picture)—this paper shows that: ✅ If a human teammate trusts an AI, their colleagues are more likely to trust it too. This effect is not only quite strong, it is also stable when controlling for people´s own, initial preferences after trying the AI the first time and holds true in contexts where actual money is on the table! ✅ This effect disappears if the human teammate themselves is seen as untrustworthy. ✅ Trust in AI is not just about the AI's own reliability—it depends on social context and human relationships. 🚀 Why does this matter? 1️⃣ Organizations implementing AI should focus on social dynamics and context rather than just AI performance. It does not (only) matter how well AI functions - if relevant others around employees don´t trust AI, employees won´t either. 2️⃣ Building trust in AI requires trusted human advocates—if key employees are skeptical, adoption suffers. 3️⃣ AI trust calibration is crucial: Over-reliance and under-reliance on AI both have risks, and leaders should consider social influences when introducing AI teammates. 🎉 Huge congratulations to Türkü for this important contribution! If you’re interested in how social cognitive theory can explain trust in AI teams, check out the full paper. What makes this even more special? JOB is the journal where my first academic paper was published—and where my own PhD supervisor (Frank Walter) had their first journal publication. A true academic full-circle moment! 🎓🔁 I’d love to hear from others: Have you noticed social influences shaping how people trust AI in your workplace? Have you ever seen CEOs, leaders, colleagues modeling (or refraining from modeling) trust in AI? #AI #TrustInAI #HumanAITeams #OrganizationalBehavior #FutureOfWork #Leadership #AcademicMentorship

Trust in technology is not about making systems look friendly or adding more explanations. It is about how people decide to rely on something when there is uncertainty. In human computer interaction, trust is a judgment users make. It is shaped by expectations, experience, social cues, perceived control, and context. The same system can be trusted in one situation and distrusted in another. That is why trust is so hard to design and so easy to break. Research shows that users do not trust systems for a single reason. Sometimes trust comes from reasoning. Does this system behave consistently? Does it do what I expect? Other times trust comes from feeling. Does this interface feel human, present, or socially responsive? In many cases trust is social. If people I trust rely on this system, I am more likely to trust it too. There are also moments where trust collapses. When users feel forced, manipulated, or stripped of control, distrust appears even if the system is accurate. When early experiences violate expectations, trust erodes fast and rarely recovers on its own. One of the most important insights is that trust is dynamic. It builds slowly through repeated positive interactions and can disappear quickly after a single negative one. Designing for trust is not about maximizing trust. It is about supporting appropriate trust. Helping users know when to rely on a system and when not to. For AI, automation, and complex digital products, this matters more than ever. Overtrust is just as dangerous as distrust. Good design respects user agency, supports understanding, and stays honest about limitations. Trust is not a feature you add at the end. It is an outcome of how the entire system behaves over time.

Is AI Replacing Our Thinking? | ¿Estamos empezando a delegarle el pensamiento a la IA? Two recent research papers (links below) challenged my assumption that AI is simply a tool. The reality may be more complex...and these studies suggest a deeper shift may already be underway. 😱 AI is starting to reshape how we reason 😱 Let’s unpack this step by step. Keep reading! 👇 👇 👇 1️⃣ A new participant in human cognition For decades, behavioral science (popularized by Daniel Kahneman) described thinking as two systems: • System 1 (fast, intuitive, automatic) • System 2 (slow, analytical, effortful) But generative AI introduces a third actor: • Artificial cognition —> external reasoning generated by AI systems. 🤭 For the first time in history, part of our thinking process can occur outside the human mind. 2️⃣ The risk of "cognitive surrender" It happens when people rely heavily on AI outputs without critical evaluation. This doesn’t necessarily happen because people are careless. It happens because AI often appears confident, coherent, and fast...qualities that naturally trigger trust. 3️⃣ What real-world conversations reveal Studies provide rare empirical evidence from real usage: severe "disempowerment" cases were rare, but not negligible at scale. Researchers identified patterns where AI can subtly reduce human agency, including: • Users asking the AI to decide what they should do • Delegating personal communication (messages, decisions, judgments) • Treating the model as an authority rather than a collaborator Perhaps the most surprising finding: Interactions with higher disempowerment potential often receive higher user satisfaction scores. 🤯 🔥 4️⃣ The paradox of the AI era AI can dramatically improve decisions when used well. But if we stop questioning outputs, the outcome of many decisions becomes dependent on the AI rather than the human judgment behind it. 🫣 What does this mean? It seems clear to me: the real challenge of AI is far from technical capability, but close to cognitive governance. So... If AI is becoming part of our cognitive process, then AI literacy becomes a core capability, not a nice-to-have. People need to understand: • how AI systems reason • where they fail • when to trust them • and when to challenge them Links: - “Who’s in Charge? Disempowerment Patterns in Real-World LLM Usage.” https://lnkd.in/emi-DVQp - “Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender.” https://lnkd.in/eVPb3pQv - Thoughtful analysis published by My Tech Plan: https://lnkd.in/eJuSxmV5 #GovernYourAINow #GobiernaYaTuIA

Trust determines how AI is actually used. This is a lesson about human-system interaction that human factors psychology taught us long ago. Too many AI discussions focus mostly on accuracy. If a system performs well, people assume it will be used well. That is not what happens in practice. People do not respond to AI based on capability alone. They respond based on trust. When trust is too low, systems are underused. People ignore outputs or redo the work. When trust is too high, systems are over-relied on. Outputs are accepted without enough scrutiny. Errors pass through. Neither outcome is what we want. The goal is not maximum trust, nor is it minimal trust. It is calibrated trust. Trust is psychological. Trust is shaped by more than accuracy. Trust is shaped by how the system is introduced, how transparent it is, and how costly errors are (including how embarrassing they may be). Trust is also shaped by how the work is structured around it. Poorly designed workflows create constant monitoring and correction. Trust erodes. Clear roles and decision points stabilize use. Trust holds. The same system can be trusted in one setting and resisted in another. If we want effective use of AI, we have to design for trust. That is a psychological problem as much as a technical one. #appliedpsychology #psychology #humanfactors #artificialintelligence Brandon May Ph.D Emanuel Robinson Fred Oswald Mindy Shoss David Blustein

I often hear that AI fails due to technical challenges: models, data, or infrastructure. But AI initiatives most often stumble because people don’t trust them, cognitively and emotionally. If you want your teams to follow and your leadership to have real impact, join me for Part #2 of the AI mini-series. Too many AI strategies focus solely on accuracy, scale and ROI. Few address how people actually feel when AI becomes part of their daily work. Trust isn’t built with dashboards or motivational quotes on the walls... It’s built when people feel truly seen, safe and respected. To achieve this, AI must be positioned as a catalyst for human potential and not as a silent evaluator. If your AI strategy overlooks HUMAN emotions like fear, curiosity, pride and identity, then it will never fully land. Adoption isn’t just a technical hurdle. It’s fundamentally a leadership behavior challenge. Let me unpack how education, empathy and intentional leadership foster cognitive trust and why EMOTION is the missing layer in most enterprise AI transformations. Here are 5 actionable leadership behaviors to drive successful, AI Enterprise adoption: 1.     Lead with intent, not just tools: Launch every AI initiative by clearly stating its purpose for people, not just the business. Connect AI to personal growth and learning, not only efficiency. 2.     Normalize emotion in AI conversations: Invite concerns, discomfort, and skepticism. Treat emotional responses as valuable data, not resistance. Psychological safety is essential for adoption. 3.     Educate beyond “how it works”: Train teams on AI’s limitations, trade-offs, and failure modes not just on capabilities. Cognitive trust grows when leaders are transparent about what AI cannot do. 4.     Model vulnerability at the top: Leaders should share openly what they are learning and calibrating about AI. Authentic leadership accelerates trust. 5.     Align incentives, lexicon, and decision-making with people-centric AI use. One misaligned KPI can erode months of trust-building. Consistency creates credibility. Let’s remember: the success of AI isn’t just about technology, it’s about human connection and trust. #AILeadership #TrustInAI #EnterpriseAITransformation #HumanCenteredAI #CognitiveTrust #LeadershipMatters #ChangeManagement