Re:Connect Conference 2025
Amplifying Inequalities: The Hidden Bias In Data
Big data and AI were meant to make our world smarter, faster, and fairer. But what happens when the systems we trust the most are built on biased foundations?
In this talk and companion eBook, I explore how bias makes its way into data, from the way it’s collected to the way algorithms use it—and what the real-world consequences look like for individuals, communities, and entire industries.
Whether you work in tech, healthcare, education, or policy, this is about more than just machine learning; it’s about the human cost of getting it wrong, and what we can all do to make it better.
Download the Workshop Deck
Want to revisit what we covered or explore it in your own time?
Slides include:
Key definitions and types of bias
Real-world case studies
Group tasks and exercises
Questions to explore with your team or classroom
eBook: Take the Ideas Further
This companion eBook dives deeper into:
The many faces of bias (algorithmic, sampling, confirmation…)
Why bias persists in modern systems
Case studies in justice, hiring, healthcare, AI media tools and more
How marginalised groups are affected and forced to adapt
The innovators and campaigns challenging data inequality
Practical steps to build more inclusive, transparent tech
It’s packed with real-world stories, recent research, and hopeful solutions.
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