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#01: Trust by Code: AI Governance and the Human Layer
‚Truth by code is a strong asset‘ -
with Anna Spitznagel, CEO of trail.ai
In the first episode of the in-between trust podcast, Eva Simone Lihotzky speaks with Anna Spitznagel—co-founder and CEO of trail.ai—about building trust at the heart of AI governance. Anna shares her journey designing a “co-pilot” for responsible AI systems, and explores how transparency, organizational culture, and technical rigor intersect in shaping trustworthy innovation.
Together, they dive into what it means to build “truth by code,” how compliance can enable—not hinder—progress, and why literacy, leadership, and lived experience are essential in navigating the AI era. Anna also opens questions about the future of trust across ecosystems—from upstream model providers to everyday users.
⸻
🔑 Takeaways
- Trust is the starting point for meaningful AI adoption.
- Transparency and quality must be designed into both code and culture.
- Responsible leadership requires communication, clarity, and tolerance for mistakes.
- Literacy in AI is not optional—it’s foundational to ethical use.
- Governance can be a growth engine, not just a constraint.
- The best AI use cases start with real problems, not hype.
- 80% of AI projects don’t reach production—trust structures can change that.
- Trust builds through use, experience, and critical questioning.
- Future trust will require cooperation across the AI supply chain.
⸻
🎙️ Sound Bites
“Trust is my personal highest value.”
“Truth by code is a strong asset.”
“Literacy is key to understanding AI.”
“Governance is not a blocker—it’s an enabler of scale.”
⸻
⏱️ Chapters
00:00 – Introduction to AI Governance and Trust
01:38 – Defining Trust in AI
04:27 – Building Trust in Organizations
10:13 – The Role of Leadership in AI
12:51 – Designing for Transparency
18:58 – Navigating Use Cases & Compliance
23:14 – The Future of Trust in AI
30:45 – Unanswered Questions That Remain
⸻
🧩 Keywords
AI governance, trust, transparency, leadership, compliance, data privacy, organizational culture, literacy, AI use cases, critical reasoning, automation, future of AI
View all episodes
By Eva Simone Lihotzky‚Truth by code is a strong asset‘ -
with Anna Spitznagel, CEO of trail.ai
In the first episode of the in-between trust podcast, Eva Simone Lihotzky speaks with Anna Spitznagel—co-founder and CEO of trail.ai—about building trust at the heart of AI governance. Anna shares her journey designing a “co-pilot” for responsible AI systems, and explores how transparency, organizational culture, and technical rigor intersect in shaping trustworthy innovation.
Together, they dive into what it means to build “truth by code,” how compliance can enable—not hinder—progress, and why literacy, leadership, and lived experience are essential in navigating the AI era. Anna also opens questions about the future of trust across ecosystems—from upstream model providers to everyday users.
⸻
🔑 Takeaways
- Trust is the starting point for meaningful AI adoption.
- Transparency and quality must be designed into both code and culture.
- Responsible leadership requires communication, clarity, and tolerance for mistakes.
- Literacy in AI is not optional—it’s foundational to ethical use.
- Governance can be a growth engine, not just a constraint.
- The best AI use cases start with real problems, not hype.
- 80% of AI projects don’t reach production—trust structures can change that.
- Trust builds through use, experience, and critical questioning.
- Future trust will require cooperation across the AI supply chain.
⸻
🎙️ Sound Bites
“Trust is my personal highest value.”
“Truth by code is a strong asset.”
“Literacy is key to understanding AI.”
“Governance is not a blocker—it’s an enabler of scale.”
⸻
⏱️ Chapters
00:00 – Introduction to AI Governance and Trust
01:38 – Defining Trust in AI
04:27 – Building Trust in Organizations
10:13 – The Role of Leadership in AI
12:51 – Designing for Transparency
18:58 – Navigating Use Cases & Compliance
23:14 – The Future of Trust in AI
30:45 – Unanswered Questions That Remain
⸻
🧩 Keywords
AI governance, trust, transparency, leadership, compliance, data privacy, organizational culture, literacy, AI use cases, critical reasoning, automation, future of AI