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Human-Centered Machine Learning (HCML)

Dates Venues Register
03/01/2027 - 07/01/2027 ISTANBUL

Introduction

Human-Centered Machine Learning (HCML)

 

Course Objectives:

  • Understand and apply the principles of Human-Centered Design within the realms of AI and machine learning
  • Recognize, assess, and reduce algorithmic bias while ensuring ethical and fair outcomes in ML models
  • Incorporate continuous human feedback to refine machine learning systems and enhance user relevance
  • Design AI interfaces that promote transparency, explainability, and user trust
  • Apply participatory design methodologies to ensure stakeholder inclusion in AI development
  • Evaluate both the usability and the broader societal impact of intelligent systems and AI-driven applications

Who Should Attend?

  • AI and machine learning engineers, data scientists, and technical practitioners
  • UX/UI designers creating interfaces for intelligent systems
  • Researchers and professionals in human-computer interaction (HCI) domains
  • Product managers and innovation leaders driving AI-enabled solutions
  • Ethics officers, digital transformation specialists, and technology strategists
  • Policymakers, regulators, and tech governance professionals shaping AI policies and standards

 

Course Outline

Day 1

Foundations of Human-Centered Machine Learning

  • Introduction to HCML: Concepts and Principles
  • The limitations of traditional ML approaches
  • Human-Centered Design vs. Technology-Centric Design
  • Overview of ethical frameworks in AI development
  • Case studies: Human impact of poorly designed ML systems
Day 2

Understanding Human Needs and Bias in ML

  • Human perception, cognition, and trust in AI systems
  • Identifying and measuring bias in datasets and models
  • Inclusive data collection strategies
  • Human diversity and accessibility in AI
  • Workshop: Diagnosing bias in real-world AI applications
Day 3

Designing User-Friendly and Interpretable AI Systems

  • UX principles for AI-driven applications
  • Explainable AI (XAI): Techniques and best practices
  • Transparency and interpretability in different models (e.g., black-box vs. white-box)
  • Visualizing machine learning outputs for end-users
  • Hands-on: Building interpretable models using user-centric tools
Day 4

Human-in-the-Loop Learning and Feedback Integration

  • Concepts of Human-in-the-Loop (HITL) systems
  • Reinforcement learning from human feedback
  • Interactive labeling, active learning, and adaptive systems
  • Tools for prototyping HCML systems (e.g., Teachable Machine, LIME, SHAP)
  • Case study: Iterative refinement with user feedback
Day 5

Ethical, Social, and Practical Implications of HCML

  • The role of empathy, transparency, and trust in AI adoption
  • Regulatory perspectives and ethical AI governance
  • Designing for marginalized and vulnerable populations
  • Group activity: Propose and present a human-centered AI project
  • Final discussion: The future of HCML in responsible AI

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