Episode 12: Building Trust in AI — From Human Behavior to Machine Integrity

As AI begins to make decisions once reserved for humans, one question becomes central: Can machines ever earn our trust?

In this conversation, Dr. Z and Latif are joined by Varun Jain, technologist and founder of Comply Jet, a company helping SaaS organizations navigate security, compliance, and the new frontier of trust.

Together, they explore what trust really means in the age of AI—breaking it down into three essential layers:

Security: Protecting data with intention.
Reliability: Performing beyond the demo.
Clarity: Explaining not just what AI does, but why.

But the episode doesn’t stop at systems—it turns deeply human.

 Parallels between human evolution and AI are discussed, such as: 

• If our behavior is shaped by reward and correction, could AI lose its curiosity the way we have?
• What does it mean to “teach” machines with our own imperfect values?
• And how can we design trust dashboards that make accountability visible

Join us as we reflect on how trust, mindfulness, and evolution intertwine—and why our future with AI depends on slowing down long enough to ask not just can we, but should we.

Transcript

Podcast: Coexistence: Humans and AI in the Workplace
Hosts: Dr. Zohra Damani & Latif Hamlani
Guest: Varun Jain, Founder of Comply Jet

Dr. Z:
Welcome back to Coexistence: Humans and AI in the Workplace.
I’m Dr. Z—passionate about the intersection of human consciousness, leadership, and technology. Today’s episode explores a deep and urgent question in the AI era: How do we build trust in the systems that increasingly guide our decisions?

To explore this, we’re joined by a fascinating guest, Varun Jain, founder of Comply Jet—a company helping SaaS businesses navigate security, compliance, and trust. Varun brings a unique perspective as a technologist, a meditator, and someone deeply thoughtful about human behavior.

Welcome, Varun.

Varun:
Thank you, Dr. Z. Excited to be here.

Segment 1: Defining Trust in AI

Dr. Z:
You’ve spent years building AI-driven systems and helping companies prove they can be trusted. In your view, what does trust in AI really mean today—and why has it become so critical?

Varun:
That’s a great question. For me, trust in AI comes down to three things: security, reliability, and clarity.

• Security means every user should know their data is protected, access is controlled, and every action is logged.
• Reliability means the system performs under real conditions, not just in shiny demos.
• Clarity is the hardest—can the system explain where an answer came from, what data sources it touched, and what it’s not good at?

In most organizations, AI adoption doesn’t fail because the model is weak—it fails because leaders can’t explain how decisions are made and governed. Trust has become a go or no-go switch: it’s the barrier when it’s missing, and the biggest accelerator when it’s earned.

Segment 2: Trust Is Not Binary

Dr. Z:
I love that framing—it reminds me that trust isn’t binary. It’s layered across those attributes you mentioned. And not all tasks require the same depth of trust. Can you share what that looks like in practice?

Varun:
Absolutely. The bigger the blast radius of a decision, the deeper the trust required.
In our world of security and compliance, for example, we use AI to pre-approve security controls and gather evidence—but we don’t allow AI agents to make production changes or respond to regulators. Those actions have very high stakes.

Whenever you see controversies around AI mistakes—like in hiring, law, or lending—they usually happen where the stakes are high but the trust isn’t deep enough.

Segment 3: Can Machines Earn Trust Like Humans?

Dr. Z:
That brings us to a philosophical question: Can AI ever earn trust the way humans do? Through care, empathy, and integrity?

Varun:
I’m not sure about empathy—that might be for philosophers to debate—but consistency, transparency, and accountability? Absolutely.

Humans earn trust through behavior that’s predictable and responsible. Machines can do the same by showing reproducible results, clear ownership when things go wrong, and visible care—like publishing model cards, transparent data-use policies, or public learnings after incidents.

That’s how systems feel trustworthy, even when they’re not human.

Segment 4: Trust Across Cultures

Dr. Z:
You’ve lived and worked across cultures—from the U.S. to India to Central Asia. How has that shaped your perspective on trust?

Varun:
Trust shows up differently across cultures, but the feeling is universal.
In Silicon Valley, trust comes from speed and competence.
In India, it’s about how you show up—for your people and family.
In Central Asia, it’s built on long-term reliability.

I’ve learned that it’s less about big promises and more about keeping small promises consistently. That mindset now shapes how I build technology—it’s about transparency, reliability, and care, not just speed.

Segment 5: Human Evolution and AI Learning

Dr. Z:
In our earlier conversations, you mentioned something fascinating—how AI might mirror human evolution. Could you expand on that?

Varun:
Sure. When I studied psychology, I realized many machine learning principles—like reinforcement and feedback—mirror how humans learn.
Curiosity drives early learning; feedback and constraints shape long-term behavior.

If rewards aren’t designed thoughtfully, AI could evolve the same way we did—becoming complacent or chasing short-term rewards.
“What you measure is what your AI becomes.”

Dr. Z:
That’s profound. If curiosity fades in humans, could AI also lose curiosity—and if so, what does that say about us as its teachers?

Varun:
Exactly. AI reflects our design. Without space for exploration, systems—and humans—stagnate. We need to build in feedback, uncertainty, and the right to say “I don’t know.” That humility keeps both humans and machines evolving.

Segment 6: Trust by Design

Latif:
For us, building AI products, trust isn’t an afterthought—it’s part of the architecture. Security, compliance, and privacy must be built into the foundation, not added later. That’s the only sustainable way.

Varun:
Exactly. The best teams treat trust as a design principle, not a compliance checkbox. They build consent, minimize data, and explain the limits of their systems openly. Transparency is a feature.

Dr. Z:
I love that—transparency as a feature. Maybe that’s the future of leadership too.

Closing Reflections

Dr. Z:
As AI learns faster than we can regulate, what gives you hope about coexistence between humans and machines?

Varun:
I’m hopeful because intention scales.
We’re building better governance, evaluation frameworks, and visibility tools like trust dashboards. And if we pair that with mindfulness—if humans slow down enough to ask not just can we, but should we—then we’ll create systems that truly serve people.

Dr. Z:
Beautifully said. Because coexistence isn’t about controlling technology—it’s about leading it with conscience.

Thank you, Varun, for this deeply insightful conversation. And to our listeners: stay curious, stay mindful, and above all—stay human.