In this HCI Webinar, I talk with Kathryn Landis about effectively leading diverse teams. Kathryn Landis helps organizations accelerate success by empowering growth-minded leaders and their teams with executive coaching, team coaching, offsites, and leadership development training in today's dynamic business environment. Kathryn's insights and strategies have gained recognition in prestigious publications like Harvard Business Review, Fast Company, and Forbes, further establishing her reputation as a sought-after expert in the field. She holds an MBA from Northwestern University, Certificate in Executive and Organizational Coaching from Columbia University, Certificate in Diversity and Inclusion from Cornell University and BS from Indiana University. Moreover, Kathryn is a National Diversity Council Certified Diversity Professional (NDCCDP), Associate Certified Coach by the International Coaching Federation (ACC) and Professor of C-Suite Leadership New York University.

This research explores the critical intersection of artificial intelligence and human autonomy within the modern workplace. It argues that while AI can significantly boost productivity, it risks undermining individual agency if workers lose the ability to understand, challenge, or control algorithmic outputs. To prevent skill degradation and professional alienation, the research suggests implementing transparency architectures and involving employees directly in the design of AI workflows. Organizations must move beyond mere technical deployment to cultivate hybrid expertise, ensuring that human judgment remains the primary driver of decision-making. Ultimately, the research advocates for a values-centered approach where technology serves to enhance human flourishing rather than simply automating tasks for efficiency.

This video explores the profound impact of artificial intelligence (AI) on the modern workplace, emphasizing the tension between increasing automation and preserving human agency. It opens by illustrating a dystopian scenario where AI acts as a rigid, unfeeling manager, issuing instructions without room for human judgment or interaction. Though AI offers undeniable benefits—such as processing vast amounts of data quickly and handling repetitive tasks—it also poses risks by potentially undermining human skills, judgment, and decision-making power. The core concept introduced is human agency, defined as the individual’s ability to actively shape their work, understand AI-driven advice, and continue to develop professionally. Human agency consists of three dimensions: operational control (hands-on decision-making), epistemic control (understanding and questioning knowledge), and developmental control (opportunities for growth and learning). The video stresses that AI should serve as a partner that enhances, rather than replaces, human expertise. Examples such as doctors using AI to detect diseases or teachers employing AI for grading assistance illustrate how AI can free professionals to focus on complex, human-centered tasks. However, poorly designed AI—characterized by opaque decision-making and lack of transparency—can erode trust and reduce workers to passive executors of machine commands. To harness AI responsibly, organizations must prioritize transparency, involve workers early in design and deployment, provide adequate training, and establish governance frameworks. These steps ensure AI tools empower rather than disempower, maintaining meaningful human control in a rapidly evolving work environment. Highlights 🤖 AI is transforming workplaces by automating data-heavy and repetitive tasks. 🧠 Human agency is critical to maintaining control and meaning at work amid AI integration. ⚙️ Human agency includes operational control, epistemic control, and developmental control. 🩺 AI can assist professionals like doctors and teachers, enhancing decision-making and reducing burnout. 🚫 Poorly designed AI systems can strip workers of understanding and autonomy. 🔍 Transparency in AI systems fosters trust and informed decision-making. 👥 Involving workers in AI design and deployment ensures better adoption and preserves human control. Key Insights 🤖 AI as a double-edged sword: efficiency vs. autonomy AI significantly increases efficiency by automating tedious tasks, but this efficiency can come at the cost of reducing human autonomy. When AI dictates decisions without human input or explanation, workers risk becoming mere executors of machine commands, which diminishes job satisfaction and professional growth. Balancing AI’s speed and accuracy with human judgment is essential to avoid dehumanizing workplaces. 🧠 Human agency: the cornerstone of meaningful work Human agency is not just about having a job but about being an active participant in it. It empowers individuals to make decisions, understand the basis for those decisions, and continue developing skills. Without agency, work becomes monotonous and demoralizing. Recognizing and preserving agency helps sustain motivation, creativity, and ethical responsibility in the workplace. ⚙️ Three dimensions of human agency provide a useful framework Operational control allows workers to shape how tasks are done, ensuring flexibility and customization. Epistemic control empowers workers to question AI outputs critically, preventing blind reliance on algorithms and encouraging informed skepticism. Developmental control ensures workers continue to grow professionally, avoiding skill atrophy as AI takes over routine functions. This tripartite model can guide AI integration strategies. 🩺 AI as an augmentation tool, not a replacement Well-designed AI acts like a brilliant assistant, handling repetitive tasks and augmenting human expertise rather than replacing it. For instance, in medicine, AI can rapidly analyze scans to flag concerns but cannot replace a doctor’s judgment that incorporates patient history and ethical considerations. Similarly, AI can handle grammar checks for teachers but cannot replace personalized feedback. This synergy enhances productivity and job satisfaction. 🚫 Opaque AI systems undermine trust and efficacy Black-box AI systems that provide no explanation for their decisions erode epistemic control, leaving workers frustrated and powerless. This opacity can lead to mistakes going undetected if the AI’s hidden logic is flawed. Transparency is crucial to build trust and allow workers to validate or challenge AI outputs, which is vital for safety-critical fields and complex decision-making. #HumanAgency #ExplainableAI #AIGovernance OUTLINE: 00:00:00 - The New Boss Is A Bot? + Defining Agency 00:02:31 - Agency Dimensions + AI’s Two Faces 00:05:12 - Guardrails, Governance, and a Human-Centered Future

This conversation explores the critical intersection of artificial intelligence and human autonomy within the modern workplace. It argues that while AI can significantly boost productivity, it risks undermining individual agency if workers lose the ability to understand, challenge, or control algorithmic outputs. To prevent skill degradation and professional alienation, they suggest implementing transparency architectures and involving employees directly in the design of AI workflows. Organizations must move beyond mere technical deployment to cultivate hybrid expertise, ensuring that human judgment remains the primary driver of decision-making. Ultimately, they advocate for a values-centered approach where technology serves to enhance human flourishing rather than simply automating tasks for efficiency. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As AI reshapes professional work, this episode examines how algorithmic systems can both empower and erode human agency—operational, epistemic, and developmental—and why maintaining meaningful control matters for quality, trust, and worker wellbeing. Drawing on research and policy developments, it outlines evidence-based responses: calibrated transparency, co-design and participatory governance, capability-building for hybrid expertise, and human-centered workflow design to prevent complacency and skill loss. It concludes with practical recommendations for dynamic reconfiguration, collective oversight, and purpose-aligned AI integration so organizations can capture AI’s benefits without sacrificing human judgment or long-term capability.

This video addresses the growing crisis of public trust in major institutions such as governments, media, corporations, and healthcare systems, highlighting how decades of broken promises have eroded faith in their competence, fairness, and transparency. This pervasive distrust has real-world consequences, from tax evasion and public health non-compliance to social fragmentation and economic instability. Into this already fragile environment, artificial intelligence (AI) is being rapidly introduced as a purported cure-all, promising improved efficiency and decision-making. However, the integration of AI also exacerbates the trust deficit due to its inherent opacity, complexity, and susceptibility to bias. The “black box” nature of AI systems—where inputs go in but the decision-making process is hidden—leaves individuals feeling powerless and unfairly judged by inscrutable algorithms, often without recourse or explanation. Highlights 🔥 Trust in major institutions is at an all-time low, comparable to “gas station sushi” in reliability. 🤖 AI is being rapidly integrated into critical decisions but often operates as an opaque “black box,” magnifying trust issues. ⚖️ AI can amplify existing biases, leading to automated discrimination that masquerades as objective data-driven fairness. 🔍 Radical transparency about AI systems and their decision criteria is essential to rebuild public trust. 👩‍⚖️ Human oversight and appeal mechanisms must accompany AI decisions to ensure fairness and accountability. 🏥 Real-world examples, like AI ethics boards in hospitals and detailed feedback in loan applications, demonstrate trust-building practices. 🌱 Institutions must choose to use AI to foster transparency, fairness, and accountability rather than deepen distrust and alienation. Key Insights 🕵️ Institutional Trust Deficit is a Global Crisis: The erosion of faith in governments, media, corporations, and healthcare is not anecdotal but a documented worldwide phenomenon. This widespread distrust undermines social cohesion and compliance with societal norms, creating fertile ground for conspiracy theories and social fragmentation. Institutions have repeatedly broken promises regarding competence, fairness, and transparency, which has damaged their legitimacy. 🤖 AI as an Accelerant of Distrust: Introducing AI into critical decision-making processes without addressing transparency issues risks amplifying the trust problem exponentially. Unlike humans, algorithms can make millions of decisions quickly, but their opacity, complexity, and inscrutability make it almost impossible for individuals to understand or challenge those decisions. This “black box” phenomenon intensifies feelings of powerlessness and unfairness. ⚠️ Bias in AI is Bias on Steroids: AI systems learn from historical data embedded with societal biases. Without intervention, these biases are magnified and institutionalized, leading to systemic discrimination that appears “objective” because it stems from data rather than explicit human prejudice. This creates a vicious cycle where marginalized groups’ lived experiences of discrimination are confirmed by AI decisions, reinforcing distrust. 🔄 Accountability Must Include Human Review: Blind reliance on AI to make final decisions is dangerous. A robust system must include mechanisms for human oversight, appeal, and correction. This ensures that when algorithms err—and they inevitably will—there is a clear, accessible path to justice that does not require costly legal battles. Human intervention acts as a vital check on automated systems. 🛠️ Building Internal Expertise and Ethical Capacity is Crucial: Institutions cannot simply outsource AI deployment and hope for the best. It is essential to develop internal teams comprising technologists, ethicists, legal experts, and representatives of impacted communities. These teams should actively test, challenge, and improve AI systems before public deployment, identifying biases and potential harms proactively. 🔎 Transparency as a Foundation for Trust: Radical transparency means more than dumping code or technical jargon; it requires clear, plain-language explanations about what AI systems do, the data they use, their limitations, and how decisions are made. Public registries of algorithms used in public services are a practical step toward openness, enabling external scrutiny and trust-building. If you found this useful, please like and share to spread the conversation. #AI #InstitutionalTrust #AlgorithmicGovernance #Transparency #OrganizationalJustice OUTLINE: 00:00:00 - Why We're All Side-Eyeing the System (Part 1) 00:01:16 - Why We're All Side-Eyeing the System (Part 2) 00:02:32 - When 'Computer Says No' is the Whole Story 00:03:53 - Pattern Recognition and Accountability 00:05:03 - How AI Can Be Less HAL 9000, More R2-D2 00:06:19 - Real Steps for Real Institutions 00:07:38 - Examples and First Steps 00:08:41 - Building a Future That's Fair, Not Just Fast

This research explores the deteriorating state of public trust in modern institutions and how the rise of artificial intelligence complicates this crisis. While the opaque nature of algorithms can worsen skepticism, the research argues that organizations can rebuild legitimacy through deliberate, evidence-based strategies. Success requires prioritizing transparent communication, ensuring procedural justice in automated decisions, and maintaining a focus on human dignity. Rather than viewing technology as a purely technical update, leaders must treat AI integration as a relational challenge that requires new forms of accountability. Ultimately, the research suggests that institutional resilience depends on aligning these advanced tools with a clear sense of moral purpose and stakeholder value.

This episode examines the steady decline of public confidence in institutions and how the rise of AI intensifies both risks and opportunities for trust. Drawing on institutional theory and research on organizational justice, it outlines evidence-based responses—transparent communication, procedural fairness, capability building, governance and accountability, and psychological contract recalibration—that organizations can use to rebuild legitimacy. Listeners will learn practical interventions and long-term strategies to ensure AI enhances institutional missions rather than undermines them, with emphasis on stakeholder voice, continuous learning, and mission-aligned deployment.