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The Human-Centered Machine Learning (HCML) Course gives AI, data science, UX, and technology professionals a comprehensive, structured framework for designing machine learning systems that prioritise human needs, embed fairness and accessibility, and deliver AI that people can understand, trust, and use effectively.
Traditional machine learning development has often focused primarily on model accuracy and technical performance with the human experience, interpretability, bias implications, and ethical dimensions addressed as afterthoughts, if at all. The consequences of that approach are increasingly visible — from biased hiring algorithms and discriminatory credit models to opaque clinical decision systems that clinicians cannot understand or trust.
This course addresses every dimension of building ML systems differently from human-centered design principles, bias identification, and inclusive data collection, through Explainable AI techniques, HITL systems, reinforcement learning from human feedback, and practical tools including LIME and SHAP, to ethical governance, regulatory perspectives, and designing for marginalised and vulnerable populations.
The Human-Centered Machine Learning (HCML) Course is built for professionals who want to develop AI and ML systems that work not just technically, but for the people they are designed to serve.
The Human-Centered Machine Learning (HCML) Course is designed to develop comprehensive HCML capability from foundational principles and ethical frameworks through bias assessment, interpretable AI design, HITL systems, and responsible AI governance.
By the end of this course, participants will be able to:
The Human-Centered Machine Learning (HCML) Course is designed for AI, data science, UX, technology, and ethics professionals who are involved in the design, development, or governance of machine learning systems and who want to build ML systems that genuinely serve human needs.
This course is suitable for:
The Human-Centered Machine Learning (HCML) Course is delivered through a structured, reflective, and practically intensive learning approach that combines HCML theory with hands-on tool application, bias diagnosis workshops, interpretable model building, and a final group project presenting a human-centered AI proposal. The course moves progressively from foundational principles through bias and cognition, explainable AI, HITL systems, and ethical governance building a complete, integrated HCML capability across all five days.
Real-world case studies of human impact from poorly designed ML systems, bias diagnosis workshops, SHAP and LIME tool sessions, and HITL prototyping exercises are integrated throughout ensuring delegates connect HCML frameworks to the real design, ethical, and operational challenges of building human-centered AI.
Delivery methods include:
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Traditional ML development has typically optimised for technical performance metrics accuracy, precision, recall — without systematically addressing how human users experience, trust, and interact with the system; whether training data reflects the diversity of affected populations; or what happens when model outputs are opaque, biased, or inaccessible to the people making decisions with them. The Human-Centered Machine Learning (HCML) Course addresses each of these limitations directly building the design, fairness, and explainability practices that traditional ML development too often omits.
Day 3 covers Explainable AI in depth — examining the distinction between black-box and white-box models, the techniques and best practices for making ML outputs interpretable, and how to visualise model decisions for end-users who are not data scientists. Day 4 extends this with hands-on application of LIME and SHAP two of the most widely used XAI tools helping delegates develop practical interpretability skills that they can apply directly to model transparency and governance challenges in their work.
Designing for marginalised and vulnerable populations is addressed within Day 5 — examining the specific design, data, and ethical considerations required when ML systems affect people who are already underserved by existing systems. Delegates develop the ethical design awareness to evaluate whether their ML systems account for the needs, contexts, and risks of the full range of people they affect not just the majority or the default user and to apply the inclusive design principles that make AI systems genuinely equitable.
Day 2 dedicates full focus to human needs and bias — covering how bias enters ML systems through data collection, labelling, and model design, how to identify and measure bias using structured assessment approaches, and how inclusive data collection strategies and diversity-aware design practices reduce bias at source. Delegates complete a bias diagnosis workshop applying these methods to real-world AI application scenarios and leaving with a practical bias assessment capability they can apply to ML projects in their own organisations.
HITL learning is addressed within Day 4 — covering the concept of systems that continuously incorporate human feedback into model learning, the application of reinforcement learning from human feedback, interactive labelling, active learning, and adaptive system design. Delegates apply HITL principles using Teachable Machine and iterative prototyping exercises developing a practical understanding of how human feedback loops improve model performance and alignment with human values over time.
Ethical governance and regulatory perspectives are addressed within Day 5 — covering the role of empathy, transparency, and trust in responsible AI, the regulatory frameworks relevant to human-centered ML development, and how AI governance structures ensure accountability for the human impact of ML systems. Delegates develop the governance awareness to build ML systems that are not only technically effective but ethically defensible and aligned with the emerging regulatory standards that are reshaping what responsible AI development requires.
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