Who Gets Written Out of the AI Future?
December 30, 2025
AI Summary
5 min read🎙️ The Voices & The Context
- The Format: This live stream podcast interview features host Jordan guiding a philosophical exploration of AI biases and societal exclusion through candid dialogue with expert guest Bridget Todd, creating a reflective and interrogative atmosphere focused on ethical implications for everyday users.
- The Format: This is an interview.
- The Key Players:
- Guest: Bridget Todd – Podcast host at the Mozilla Foundation (IRL, examining power in AI) and iHeartRadio (There Are No Girls on the Internet, on tech, identity, and social media); renowned for amplifying marginalized voices in technology discussions.
🗝️ Key Themes & Topics
The episode dives into AI's risks of perpetuating exclusion and biases, emphasizing human responsibility to foster inclusive futures through diverse perspectives and oversight.
- Topic 1: Marginalized Groups Written Out of the AI Future – Bridget highlights how racialized people, women, queer and trans individuals, older folks, youth, and working-class communities are sidelined in AI development and representation, mirroring broader tech exclusion and leading to unrepresentative models that ignore their stories.
- Topic 2: AI Biases Stemming from Human Training Data – Discussion reveals how large language models (LLMs) amplify societal flaws like sexism and racism from internet data, w
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What you'll learn
- 1 `(00:00)` **🎙️ Introduction: Bridget Todd**
- 2 `(03:20)` **Who Gets Written Out of the AI Future?**
- 3 `(04:15)` **Dangers of Over-Reliance on AI**
- 4 `(05:51)` **Shared Responsibility for Better AI**
- 5 `(07:47)` **AI Slop and Hands-Off Content Creation**
- 6 `(09:10)` **Barriers for Marginalized Voices Online**
- 7 `(10:40)` **Real-World Bias Examples**
+ Full timestamped outline available in the app
Show Notes
One of the scariest parts of AI? 😰
Who (or what) gets left out.
As a result, LLM outputs are heavily skewed toward the perspectives and content most common in their training data and the people who supervise them.
Which is almost always an absolutely terrible thing.
So, who gets written out of the AI future? And how do we fix it?
Join us to find out.
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Topics Covered in This Episode:
- Over-Reliance on AI in Daily Life
- Marginalized Groups Excluded from AI Future
- AI Reflecting Societal Biases and Blind Spots
- Responsibility for AI Training Data and Bias
- Dangers of "AI Slop" and Unedited Content
- Biased AI Moderation and Platform Challenges
- Importance of Human Oversight in AI Outputs
- Avoiding AI Echo Chambers and Algorithmic Divide
- Trust and Quality Concerns with AI Content
- Amplifying Diverse Voices in AI Leadership
Timestamps:
00:00 "AI Reliance and Ethical Risks"
03:37 "Inclusion in AI Conversations"
06:32 "Shared Responsibility for AI Change"
11:03 "AI Bias Against Black Hairstyles"
15:02 "Growing Businesses with Generative AI"
16:21 "For Us, By Us"
20:07 Preventing AI Echo Chambers
25:14 "Rethinking Leadership and AI Use"
26:46 "Everyday AI Wrap-Up"
Keywords:
AI bias, large language models, marginalized voices in AI, representation in AI, diversity in AI, AI and identity, technology and power, algorithmic bias, training data bias, cultural competence in AI, AI exclusion, social media moderation algorithms, biased AI moderation, racial bias in AI, gender bias in AI, queer representation in AI, trans representation in technology, working class and AI, age bias in AI, responsible AI use, AI content creation,
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