Everyday AI
Who Gets Written Out of the AI Future?
AI Summary
The episode examines how AI systems systematically exclude marginalized groups, highlighting biases that arise from skewed training data and the perspectives of those who supervise models. It discusses concrete examples such as AI misrepresenting Black hairstyles and the dangers of unedited "AI slop," while emphasizing the need for shared responsibility, human oversight, and diverse leadership to prevent echo chambers and ensure inclusive AI. Listeners are urged to amplify underrepresented voices and rethink AI governance to create a more equitable future.
Episode Description
One of the scariest parts of AI? 😰
Who (or what) gets left out.Â
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.Â
So, who gets written out of the AI future? And how do we fix it?Â
Join us to find out.Â
Newsletter: Sign up for our free daily newsletter
More on this Episode:Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn
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, AI slop, human in the loop, human-centered AI, ethical AI, trust in AI, AI and creativity, AI echo chambers, personalization in AI models, AI-generated content, voice amplification in technology, AI-powered surveillance, inverse surveillance, AI leadership, tech activism, AI for social good, AI media trust, challenge in AI adoption, AI community guidelines, inclusion in technology, future of AI representation, multi-agent orchestration, responsible AI auditing, training data selection, human feedback in AI, algorithmic transparency.
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)
Ready for ROI on GenAI? Go to youreverydayai.com/partner
Show Notes
One of the scariest parts of AI? 😰
Who (or what) gets left out.Â
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.Â
So, who gets written out of the AI future? And how do we fix it?Â
Join us to find out.Â
Newsletter: Sign up for our free daily newsletter
More on this Episode:Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn
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, AI slop, human in the loop, human-centered AI, ethical AI, trust in AI, AI and creativity, AI echo chambers, personalization in AI models, AI-generated content, voice amplification in technology, AI-powered surveillance, inverse surveillance, AI leadership, tech activism, AI for social good, AI media trust, challenge in AI adoption, AI community guidelines, inclusion in technology, future of AI representation, multi-agent orchestration, responsible AI auditing, training data selection, human feedback in AI, algorithmic transparency.
Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)
Ready for ROI on GenAI? Go to youreverydayai.com/partner
Comments
Want to join the conversation?
Loading comments...