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Event PyDataMCR November 2025-11-12
Faireness and Inclusivity in AI Systems 2025-11-12 · 18:30 Tito Osadebey – AI researcher and data scientist @ Keele University; Synectics Solutions; Unify Fairness and inclusivity are critical challenges as AI systems influence decisions in healthcare, finance, and everyday life. Yet, most fairness frameworks are developed in limited contexts, often overlooking the data diversity needed for global reliability. In this talk, Tito Osadebey shares lessons from his research on bias in computer vision models to highlight where fairness efforts often fall short and how data professionals can address these gaps. He’ll outline practical principles for building and evaluating inclusive AI systems, discuss pitfalls that lead to hidden biases, and explore what “fairness” really means in practice. Tito Osadebey is an AI researcher and data scientist whose work focuses on fairness, inclusivity, and ethical representation in AI systems. He recently published a paper on bias in computer vision models using Nigerian food images, which examines how underrepresentation of the Global South affects model performance and trust. Tito has contributed to research and industry projects spanning computer vision, NLP, GenAI and data science with organisations including Keele University, Synectics Solutions, and Unify. His work has been featured on BBC Radio, and he led a team from Keele University which secured 3rd place globally at the 2025 IEEE MetroXraine Forensic Handwritten Document Analysis Challenge. He is passionate about making AI systems more inclusive, context-aware, and equitable bridging the gap between technical innovation and human understanding. AI/ML Data Science GenAI NLP
Building a price comparison website using AI 2025-11-12 · 18:30 Alex Lewzey – ML Engineer Building a price comparison platform requires solving multiple ML challenges at scale. This talk covers a year-long production project combining LLMs, graph algorithms, and computer vision. We'll explore: Orchestrating complex ML workflows with Vertex AI Pipelines Using Gemini to classify products, extract attributes, and generating titles/descriptions Connecting product variants across retailers with graph algorithms Deduplicating images using computer vision You'll learn practical lessons from deploying these systems in production, including trade-offs and challenges encountered along the way vertex ai pipelines gemini graph algorithms computer vision llms

Fairness and inclusivity are critical challenges as AI systems influence decisions in healthcare, finance, and everyday life. Yet, most fairness frameworks are developed in limited contexts, often overlooking the data diversity needed for global reliability.

In this talk, Tito Osadebey shares lessons from his research on bias in computer vision models to highlight where fairness efforts often fall short and how data professionals can address these gaps. He’ll outline practical principles for building and evaluating inclusive AI systems, discuss pitfalls that lead to hidden biases, and explore what “fairness” really means in practice.

Tito Osadebey is an AI researcher and data scientist whose work focuses on fairness, inclusivity, and ethical representation in AI systems. He recently published a paper on bias in computer vision models using Nigerian food images, which examines how underrepresentation of the Global South affects model performance and trust.

Tito has contributed to research and industry projects spanning computer vision, NLP, GenAI and data science with organisations including Keele University, Synectics Solutions, and Unify. His work has been featured on BBC Radio, and he led a team from Keele University which secured 3rd place globally at the 2025 IEEE MetroXraine Forensic Handwritten Document Analysis Challenge.

He is passionate about making AI systems more inclusive, context-aware, and equitable bridging the gap between technical innovation and human understanding.

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