Can You Build AI Without Bias? | John Pasmore

Open||Source||Data

John Pasmore, thinks the answer is yes — but not if we keep doing things the old way. In this episode, the CEO and founder of Latimer AI lays out the company’s strategy for inclusive AI: replace scraped social content with vetted academic material, digitize underrepresented history, and build guardrails with purpose.

Charna and John also explore the implications for enterprise, healthcare, and education — sectors where small biases can cause serious harm.

TIMESTAMPS

[00:00:00] — Intro

[00:02:00] — John's Journey into AI

[00:04:00] — Data Sources & Historical Archives

[00:06:00] — Underrepresented Digital Histories

[00:08:00] — Flawed Training Sets in LLMs

[00:10:00] — Measuring & Detecting Bias

[00:12:00] — Algorithmic Bias in Hiring

[00:14:00] — Copyright & Ethical Data Use

[00:16:00] — Multimodal Platform Rollout

[00:18:00] — Enterprise Privacy & LLM Hosting

[00:20:00] — Optimism & Intergenerational Impact

[00:22:00] — Founding in a Crowded Market

[00:26:00] — Charna’s Takeaways on Systemic Bias

[00:28:00] — Guardrails vs Structural Solutions

[00:30:00] — Training Data vs Output Behavior

[00:32:00] — Algorithmic vs Contextual Bias

[00:34:00] — Providing Cultural Context to LLMs

[00:36:00] — Community-Based Data Labeling

[00:38:00] — The Yard Tour & HBCU Partnerships

[00:40:00] — Wrapping up the Season & What’s Next

QUOTES

John Pasmore

“If a company is using AI to look at resumes, what is it? How is it classifying people's names or, we're surprised that sometimes it's using the name and coming to some conclusion about the desirability of a candidate just based on their name, where maybe that wasn't the intent."

Charna Parkey

“Instead of modifying the model itself, we can say, okay, here's a historical context, here's a new cultural insight, and here's the situation. Now tell me about the outcome, right?"