I've Got Questions with Sinead Bovell

Dr. Joy Buolamwini is a computer scientist, AI researcher, and founder of the Algorithmic Justice League. Her work has exposed how bias in artificial intelligence systems can reinforce inequality at scale and why the risks of AI go far beyond the headlines.

In this conversation, Dr. Buolamwini and I explore how AI systems inherit human bias and why these biases are not just technical flaws, but deeper issues rooted in the data used to train AI.

We also talk about the invisible ways AI is already shaping our lives, from algorithmic scoring and data brokers to biometric surveillance systems that most people don’t even realize they are part of.

Finally, we explore what she means when she says AI could “kill us slowly.” Not through one dramatic event, but through long-term, systemic harm that limits opportunities, reinforces inequality, and quietly reshapes society over time.

Grab a copy of Dr. Joy Buolamwini's book here.

Timestamps:

00:00 – Can AI “Kill Us Slowly”?
02:03 – The Origin of the “Coded Gaze”
06:56 – How Bias Gets Embedded in AI Systems
09:25 – The AI Systems Quietly Shaping Your Life
13:30 – Why Biometrics and Surveillance Matter
16:09 – The Risks of Facial Recognition at Airports
23:15 – Existential Risk vs. Systemic Harm
24:29 – What Joy Means by “AI Could Kill Us Slowly”
29:02 – The Corporate Surveillance State
30:49 – When AI Gets It Wrong in the Real World
34:07 – Bias in Healthcare and Wearables
39:41 – How Bias Shows Up in Hiring and Opportunity
46:36 – The Hidden Risks of AI Photo Trends
48:26 – What Parents Should Know About Posting Kids Online
52:18 – The Future of “Faceless” Resistance
55:40 – What You Can Do Right Now
01:01:16 – “Data Isn’t Destiny”
01:06:37 – What Tech Builders Need to Do
01:12:14 – How to Get Involved
01:13:17 – Poetry, Resistance, and Closing Reflections

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In this episode, I explore what the data is revealing about the future of work in an AI-driven economy and why the “learn, work, retire” era is ending.

I discuss how the unbundling of jobs impacts each of us and what it will take to move through this transition in a way that is participatory without leaving people behind.

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In this episode, I explore the idea of a “post-reality era,” a world where the information we consume, the content we engage with, and even the relationships we form are increasingly shaped or generated by AI.

I break down the early signals already happening around us, from AI-generated music that people emotionally connect with, to the growing trust in AI systems as companions, advisors, and even sources of truth.

A post-reality era could have profound implications for democracy and societal cohesion, but if we can see it coming, there’s a lot we can do to get ahead of it.

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In our first ever IGQ live, I break down two of the biggest stories in AI right now, and take live questions from the audience.

The episode starts with the growing standoff between Anthropic and the United States Department of War, and the pressure government is placing on the AI company to leverage its technology in ways that violate the company’s ethics, and what this moment means for all of us.

I also unpack the viral Great Intelligence Crisis scenario from Citrini Teport.

A scenario that explores a near future where AI rapidly automates white-collar jobs and has a cascading impact on the economy. I breakdown what it gets right, what it gets wrong, and the real risk we need to be paying attention to.

I also take questions directly from the I’ve Got Questions audience, before closing with my perspective on what all of this means for us today, and how individuals and leaders should be thinking about the moment we’re in.

If you want to attend the next IGQ LIVE, make sure to subscribe to my newsletter and socials to stay up to date.

Follow my work here:
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In this episode, Sinead takes the stage at SXSW EDU 2025 in conversation with moderator and tech entrepreneur Natalie Monbiot for a thought-provoking discussion on AI and the path forward for education. Together, they explore why simply banning AI isn’t a solution and what it really means to design learning for an AI-first world.

The conversation examines how education systems must adapt, from rethinking curriculum and grading to creating environments where critical thinking, judgment, and intellectual independence remain central to learning. They unpack both the promise and the limits of AI in the classroom, emphasizing that technology should amplify human potential, not replace it.

Finally, the discussion turns to the skills needed to thrive in a future shaped by emerging technologies, and why most of the capabilities that matter most aren’t technical at all

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