AI Summaries Are Powerful—But Are They Fair?
As Large Language Models (LLMs) become central to how teams summarize meetings, a new risk is emerging: summaries that subtly reinforce bias. Research—including David Marquet’s work on “share of voice”—has long shown that those who dominate conversation often receive disproportionate credit. When AI is trained on conversational patterns without accounting for who said what, these distortions can become codified in summaries and action items.
🎯 The Problem: Opaque Attribution in AI Meeting Notes
AI-generated summaries often:
- Credit dominant voices—typically more senior, louder, or male speakers
- Overlook or paraphrase quieter, underrepresented team members
- Reinforce existing power dynamics in collaborative settings
Even with perfect transcription, if the AI isn’t attribution-aware, ideas can be misattributed—or vanish entirely.
🔍 Real-World Examples of Misattribution
We’ve seen it happen:
- A junior product manager raises a key concern early in a call. In the summary, it’s presented as the VP’s “key takeaway.”
- A woman’s idea gets interrupted, picked up later by a male colleague, and the AI attributes it to him.
- Action items list only those who dominated airtime—silencing contributors who were more concise but no less insightful.
These aren’t edge cases—they’re systemic issues in how meeting language is interpreted.
✅ How Equal Time Helps: Ground Truth for Inclusion
Equal Time was built with attribution awareness at its core. Here’s how it fixes what others miss:
- Who said what, when, and for how long: Equal Time tracks talk time per participant, response patterns, and interruptions—offering a factual record of conversation dynamics.
- Searchable notes with speaker tags: You can revisit exactly who contributed which idea, and when.
- Admin features for coaching: Managers can see which team members are consistently overlooked or interrupted—and coach accordingly.
- Inclusion analytics: See speaking patterns by gender and role. Get notified when conversations are imbalanced or exclusionary.
Equal Time empowers teams not just to remember what was said, but to see who contributed, and how.
🚀 Building a More Inclusive and Productive Culture
Research shows that diverse, inclusive teams are more innovative and make better decisions. But without the right tools, good intentions fall flat. Equal Time helps managers:
- Coach with context: Identify who may need support speaking up—or stepping back
- Run fairer meetings: Track interruptions, air time, and idea flow in real time
- Make summaries equitable: Ensure insights are accurately attributed to the right contributors
Ready to See What Your Meetings Are Really Saying?
🟢 Try Equal Time free and explore the only AI meeting assistant built for accuracy, attribution, and inclusion—not just summaries.