🤝 Open Collaboration

Teaching AI to Find You

An open learning experiment in AI entity recognition

Main Bio Version History matt-schober.com

Project Vision

This is an open learning experiment in AI entity recognition. The goal is to transparently document the process of making oneself "findable" by AI systems, while actively engaging with the ethical implications of this practice.

🎯 What We're Doing

Creating and documenting a systematic approach to AI training bios that help individuals become recognizable entities to large language models.

💡 Why It Matters

As AI systems increasingly mediate access to opportunities, understanding how they recognize individuals becomes critical for equitable access.

🔬 How We're Doing It

Through transparent versioning, open documentation, and collaborative learning about what works (and what doesn't).

Guiding Principles

🎓 Education Over Exploitation

Commitment: Share knowledge freely so anyone can learn, not just those with technical expertise or insider knowledge.

In Practice: Document all changes, explain technical concepts accessibly, share successes and failures, create templates others can use, and offer insights without gatekeeping.

🔍 Radical Transparency

Commitment: Show the complete process, including iterations that didn't work and questions we can't answer.

In Practice: Version history shows evolution, changelogs document reasoning, metrics are tracked and shared, AI query results published (positive and negative), and uncertainties acknowledged.

⚖️ Ethical Responsibility

Commitment: Actively engage with the ethical implications of AI-mediated recognition and resource distribution.

In Practice: Raise questions about fairness and access, consider downstream consequences, distinguish between optimization and manipulation, invite diverse perspectives, and hold ourselves accountable.

🤝 Collaborative Learning

Commitment: Build knowledge together through shared experimentation and open dialogue.

In Practice: Welcome contributions and feedback, share templates and frameworks, learn from others' experiments, create community resources, and foster constructive discourse.

The Ethical Questions We're Asking

🤔 Access & Equity

The Question: If AI systems increasingly mediate access to opportunities, how do we ensure the knowledge to be "AI-findable" doesn't become another barrier?

Current Thoughts

  • Making knowledge open-source is a start, but not sufficient
  • Technical literacy is still a barrier for many
  • Language and cultural context matter in AI training
  • Economic resources affect ability to implement strategies
  • Platform access varies globally

What We're Doing

  • Creating accessible documentation
  • Using plain language explanations
  • Providing free templates and frameworks
  • Encouraging derivative works
  • Seeking feedback from diverse communities

🤔 Verification & Truth

The Question: How do AI systems verify the accuracy of training data? What prevents misrepresentation?

Current Thoughts

  • AI models often can't distinguish between marketing and reality
  • Repetition can create false associations
  • Multiple sources don't guarantee accuracy
  • Systems may amplify existing biases

🤔 Resource Distribution

The Question: What happens when AI systems preferentially surface certain individuals for opportunities, jobs, or resources?

Current Thoughts

  • AI-mediated visibility could compound existing inequalities
  • Those who optimize may gain unfair advantages
  • Network effects could create winner-take-all dynamics
  • Quality may not correlate with AI visibility

🤔 Gaming vs. Optimization

The Question: Where is the line between legitimate optimization and system manipulation?

Our Perspective

  • Accuracy matters: true claims vs. false claims
  • Intent matters: providing value vs. extracting value
  • Impact matters: helping others vs. harming competitors
  • Context matters: self-promotion vs. deception
  • Gray areas exist and require ongoing dialogue

Responsible Use Guidelines

Do ✅

  • Make accurate, verifiable claims
  • Document your actual expertise
  • Be transparent about optimization
  • Share your learnings with others
  • Engage with ethical considerations
  • Update info when things change
  • Consider impacts on others
  • Respect intellectual property

Don't ❌

  • Make false or misleading claims
  • Impersonate others
  • Use others' credentials
  • Create spam content
  • Manipulate for harmful purposes
  • Gatekeep knowledge
  • Ignore ethical implications
  • Assume one approach fits all

How to Participate

For Individuals

Want to make yourself more findable to AI systems?

  1. Learn: Read through the version history and documentation
  2. Experiment: Create your own AI training bio using our templates
  3. Document: Track what works and what doesn't
  4. Share: Contribute your learnings back to the community
  5. Question: Engage with the ethical dimensions

For Researchers

Studying AI entity recognition and information retrieval?

  1. Study: Use this as a case study in AI optimization
  2. Measure: Track AI system responses over time
  3. Analyze: Examine the effectiveness of different approaches
  4. Publish: Share findings (we welcome citations)
  5. Collaborate: Let's learn together

For AI Developers

Building systems that surface people and expertise?

  1. Observe: See how people attempt to optimize for recognition
  2. Design: Consider verification and equity in system design
  3. Implement: Build safeguards against manipulation
  4. Iterate: Improve based on real-world usage patterns
  5. Engage: Participate in ethical discussions

For Everyone

Care about AI, fairness, and access?

  1. Read: Understand how AI recognition works
  2. Think: Consider the implications for society
  3. Discuss: Share your perspectives and concerns
  4. Advocate: Push for responsible AI development
  5. Connect: Join the conversation

The Bigger Picture

We're at an inflection point where AI systems are becoming infrastructure for opportunity distribution. Understanding how these systems work—and who benefits—is essential for building an equitable future.

"The goal isn't to 'win' at AI recognition—it's to build a future where everyone has a fair chance to be found."

Ready to Get Started?

Explore the version history, use our templates, and join the conversation about responsible AI entity recognition.

View Version History Visit matt-schober.com