Podcast Summary: Is Artificial Intelligence Ageist?

Podcast: Artificial Intelligence Podcast: ChatGPT, Claude, Midjourney and all other AI Tools
Host: Jonathan Green
Guest: Debra Albert
Date: December 8, 2025

Overview

This episode delves deep into the topic of ageism in artificial intelligence. Host Jonathan Green is joined by Debra Albert, a seasoned commentator and advocate for multi-generational inclusion. Together, they uncover how bias seeps into AI models—particularly age-related bias—through the unrepresentative data that trains these systems and the often invisible barriers that result. The conversation aims to raise awareness, encourage older generations to engage with AI, and spotlight both the risks and opportunities present as society increasingly relies on these tools.

Key Discussion Points & Insights

1. Is AI Ageist? Origins of the Problem

• AI reflects its training data: Jonathan kicks off by noting how most AI is trained on content by people who spend time online, implicitly skewing younger.
  • Quote (Jonathan, 01:06): “If AI is trained on content people write on the Internet, then the people who spend the most time on the Internet are going to be the most overly represented.”
• The workforce dynamic: Debra points out most people currently working in AI are under 35. Ageism often begins in the workplace at 40, leaving out expertise from older demographics.
  • Quote (Debra, 02:02): “When you think about all the people who are involved in AI right now, most of them are under 35. Ageism in the workplace starts at 40... and they're not necessarily in the mix of the population that is training the AI.”

2. How Bias Manifests in AI Outputs

• Models mirror creators: Jonathan shares how biases—sometimes obvious, sometimes subtle—show up in AI-generated images and stories. He recounts an incident with image models and gender roles in story prompts.
  • Quote (Jonathan, 04:19): “In the romance novel, the main character and the antagonist are both women... And the AI goes, woman can't be the bad guy. I'm like, what?”
• Missing perspective: Debra underscores the problem: “If you have a bunch of younger people all putting their quote unquote rules into the system, then it’s missing a lot of life.” (Debra, 04:53)

3. The “Invisible Barriers” & Repercussions

• Subtlety of exclusion: Jonathan likens discovering these limits to realizing the boundaries in "The Truman Show"—you don’t see it until you hit a wall.
  • Quote (Jonathan, 06:00): “It reminds me of the Truman Show when he’s on the boat until he hits the wall, he doesn’t realize that there’s really a barrier because it’s invisible. It looks like the edge of the sky.”
• Perpetuating bias through data churn: Most popular data sources (Reddit, Twitter) are generated and frequented by younger people. The risk compounds as more training data is AI-generated, creating an echo chamber effect.

4. Assumptions About Older Adults & Technology

• Prejudices debunked: Both hosts challenge the myth that older people can’t be tech-savvy, sharing anecdotes about their own families’ tech skills.
  • Quote (Debra, 10:01): “We look down upon the people who aren’t tech savvy. We look down upon—I'll say for the most part—older people because of the preconceived notion that we’re not up to snuff on the technology. And I’d like to dispute that right now... my 90 year old mother three years ago bought her own Oculus because she wanted to visit all around the world and do meditation right through her goggles.”
• Aging and Expertise: Jonathan shares his evolving experience with age perceptions:
  • Quote (Jonathan, 10:37): “When my hair turned gray around four years ago, right around when I turned 40. People started treating me way better, actually, like way more respect and like less like a kid.”

5. Why Diverse Participation Matters

• Direct encouragement to older listeners: Debra stresses the importance of older, more experienced individuals trying out AI tools to enrich the models with richer, broader input.
  • Quote (Debra, 11:47): “If you’re not already at least practicing or playing... go into these AI models and start to ask it questions, even simple questions. Once you get through the fear barrier... you’ll see it's fun, it’s easy... because we have to get people who have a wide range of experience... it will flavor the current data set that is in these models... The world does not consist only of Gen Z.”
• Cultural differences in respect for age: Linguistic and cultural context affect perceptions. Jonathan discusses how speaking Japanese changes his own outlook toward elders (13:34).

6. Objectivity is an Illusion: The Confidence Trap in AI

• AI can be confidently wrong: Both share examples of AIs making things up with total confidence and the dangers of people accepting these results at face value.
  • Memorable moment (Jonathan, 15:51): "The problem with AI is not that it’s wrong, it’s the confidence. So it tells the truth and an inaccuracy with the same level of confidence."
• The need for double-checking outputs: Jonathan always cross-verifies answers between multiple AIs. “The best users are the least technically savvy because people who are very technical see limitations that don’t actually exist.” (17:08)

7. The Power and Limits of Prompting

• How you ask matters: The way questions are phrased shapes responses—and can introduce (or mitigate) bias.
  • Quote (Jonathan, 21:12): “The most important thing is that it will always agree with you. So if you say, I think I have this, these symptoms match it, the AI is going to lean. Even if it’s a 1% lean, you’ve already got a finger on the scale.”
• Learning AI is quick—and skill-based: Mastering AI tools is less about tech skills, more about experimentation and conversational practice.
  • Quote (Jonathan, 19:42): “If you want to master an AI, it takes four to eight hours. It’s a single day of just going, I’m just going to ask questions until it starts to make sense.”

8. Encouraging Experimentation and Demystification

• Experience beats technicality (for users): The less technical the user, the more 'natural' the results; seasoned professionals are best placed to spot when something is off.
  • Quote (Jonathan, 24:00): “There’s almost a perfect inverse correlation between tech savviness and how good people are at using AI. You have to be tech savvy to build an AI, but to use it from the front end, the less tech savvy you are, the better results you get.”
• Verification is key: Older, experienced individuals are more likely to double-check AI output, catching errors that younger testers might miss.

Notable Quotes & Memorable Moments

| Timestamp | Speaker | Quote | |-----------|-----------|---------------------------------------------------------------------------------------------| | 01:06 | Jonathan | “If AI is trained on content people write on the Internet, then the people who spend the most time on the Internet are going to be the most overly represented.” | | 04:19 | Jonathan | “In the romance novel... And the AI goes, woman can't be the bad guy. I'm like, what?” | | 04:53 | Debra | “That’s the exact issue—who is making the rules?... it's missing a lot of life.” | | 06:00 | Jonathan | “It reminds me of the Truman Show... until he hits the wall, he doesn’t realize there’s a barrier because it’s invisible.” | | 10:01 | Debra | “We look down upon... older people because of the preconceived notion that we're not up to snuff on the technology.” | | 11:47 | Debra | “...go into these AI models and start to ask it questions, even simple questions... we have to get people who have a wide range of experience...” | | 15:51 | Jonathan | “The problem with AI is not that it’s wrong, it’s the confidence.” | | 19:42 | Jonathan | “If you want to master an AI, it takes four to eight hours... just going to ask questions until it starts to make sense.” | | 24:00 | Jonathan | “There’s almost a perfect inverse correlation between tech savviness and how good people are at using AI.” |

Timestamps for Important Segments

• [01:06] — Framing the ageism issue in AI data
• [02:02] — Ageism in the workplace and AI creator demographics
• [04:19] — Gender, age, and bias in story and image AIs
• [06:00] — “Invisible barriers” and the echo chamber effect
• [10:01] — Challenging stereotypes about older people and technology
• [11:47] — Debra’s call to action: why everyone (including older adults) should engage with AI
• [15:51] — Danger of AI’s overconfident errors and “hallucinations”
• [19:42] — Mastering AI through experimentation, not technical skills
• [24:00] — Experience and age as advantages in double-checking AI’s work

Takeaways & Calls to Action

• Diverse participation is vital: AI needs input from all ages to be truly representative and avoid institutionalizing ageist perspectives.
• Don’t fear experimentation: Older users are encouraged to “play” with AIs; it takes little time to get comfortable and their contributions matter.
• Objectivity is a myth: AI reflects biases present in its training data. Double-check its outputs, especially in critical applications.
• Conversational skills trump tech skills: The best prompts (and therefore, best AI results) often come from those willing to experiment and phrase questions naturally.
• Seasoned oversight is essential: Experienced professionals ensure quality, catching the subtle issues that automated tools and untrained users will miss.

Where to Find Debra Albert

• LinkedIn: Debra Albert (DebraAlbertNYC)
• Email: debraalbertnyc [at] mail (as provided)

Debra is stealthily co-founding a startup aimed at helping older adults find volunteering and flex-time work opportunities, with an emphasis on tech inclusion.

Final Thoughts

Both Jonathan and Debra call on listeners—particularly older adults—to join in experimenting with AI, adding their unique experiences and insights to the collective pool. The episode encourages a spirit of curiosity, cross-generational participation, and verification—highlighting that with more inclusion, AI will better serve us all.