Can journalism embrace AI without losing the trust that makes it valuable?

In this episode of Humans of AI, I sit down with Jessica Davis, former VP of AI Product at USA Today, to explore how AI is reshaping journalism, what it takes to build trustworthy AI systems, and why the future of news may depend more than ever on distinctly human judgment, relationships, and community.

Jessica shares a behind-the-scenes example of building AI tools for journalists. Her team developed a system to help reporters draft, file, and track public-records requests improving accuracy from roughly 70% to the high 90s through rigorous evaluations, real newsroom examples, and direct feedback from journalists.

But working on this project revealed a key insight: trustworthy AI isn't just about choosing the right model. It's about defining what success looks like, understanding the real-world consequences of failure, and empowering domain experts to shape how AI systems are evaluated and governed.

Jessica and I explore the transition from "human in the loop" to "human on the loop," the growing challenges of misinformation and AI literacy, and how AI-powered answer engines are transforming the economics of journalism.

We also ask a deeper question: What remains uniquely human about journalism when AI can generate and summarize information almost instantly?

For Jessica, the answer lies in firsthand reporting, meaningful relationships, local communities, accountability, and the ability to bring people together around stories that matter.

The conversation closes with Jessica's personal journey: from building products at USA Today to life in Nashville with her family, competitive tennis, gardening, and her reflections on what comes next.

AI can ace a Bible knowledge test. But does that mean we should trust it with questions of faith, morality, and wisdom?

In this episode of Humans of AI, I sit down with Michael Graham of The Gospel Coalition and the Keller Center for Cultural Apologetics about their 2026 AI Christian Benchmark, which evaluates leading AI models across theological reliability, Christian ethics, relational attachment, and Bible knowledge.

One of the benchmark’s most striking findings is also one of its biggest paradoxes: today’s AI models can retrieve biblical knowledge with extraordinary accuracy, yet give dramatically different answers when asked questions about morality, theology, relationships, and how people should live.

We unpack what explains that gap. How much of an AI’s answer comes from what it learned during pre-training and how much comes from the human decisions made during alignment, post-training, and product design? Why does a small change in prompting radically change a model’s ethical answers? And when an AI appears “neutral,” whose assumptions are actually shaping that neutrality?

We also explore principled pluralism, Michael’s proposal for how AI systems could handle religious questions more faithfully: rather than flattening every question into a survey of competing viewpoints, models could first represent a religious tradition accurately on its own terms before introducing alternative perspectives.

The conversation then moves beyond benchmarking into some of the deeper questions AI is forcing us to confront: Is AI conscious? And should AI systems ever receive moral consideration or welfare rights?

Ultimately, this is a conversation about more than how AI affects Christians. It is about the values being embedded into increasingly powerful systems, who gets to shape those values, and what kinds of authority we should (or shouldn’t) give artificial intelligence in our lives.

Learn more about the AI Christian Benchmark at ChristianBenchmark.ai and the Keller Center for Cultural Apologetics at thekellercenter.org.

As AI systems become more capable, who or what can reliably judge whether their behavior is correct, safe, and aligned?

In this episode of Humans of AI, I sit down with Rishub Jain and Josh Jacob, co-founders of Sampura Research, an independent nonprofit exploring how humans and AI can work together to oversee increasingly powerful systems.

Rishub and Josh explain why better AI “judges” may be central to the future of alignment. Many of the hardest behaviors to evaluate are subjective, ambiguous, or difficult to verify—and neither humans nor AI systems are reliable enough to handle every case alone.

Sampura’s research on "human–AI complementarity" asks how the strengths of each can be combined to create more trustworthy evaluations, stronger benchmarks, and oversight methods that continue to work as models improve.

We discuss what meaningful benchmarks for AI judges should look like, where human judgment remains essential, and how scalable oversight connects to the larger challenge of robust alignment. Rishub and Josh also share why they left Google DeepMind to start Sampura, how they are shaping the organization’s culture and research agenda, and the unexpected operational realities of building an independent research lab.

Learn more about Sampura Research: https://sampura.org/