“Robots playing the trust game” image created by Google Gemini 1.5, Jan 27, 2025.
To our FIRST Spotlight readers,
Hard to believe it’s now 2025. The year has been off to a tumultuous start and our hearts go out to all those affected by the wildfires in Southern California.
In the realm of AI and online media, there also seems to be upheaval and uncertainty. For example, earlier this month Meta CEO Mark Zuckerberg announced that the company would end its third-party fact-checking system in favor of an X-inspired “Community Notes” program. The argument was that professional fact-checking had become too politically motivated and that users should be the ones to determine what content is potentially misleading. This change will have serious implications for journalism, especially as AI-generated content spreads across our information ecosystem. Therefore, for this month’s Spotlight we’d like to highlight a couple of resources related to the theme of trust.
The first is a piece “AI and Trust” by Bruce Schneier, a public-interest technologist, privacy specialist, and lecturer in public policy at Harvard’s Kennedy School. It’s a couple of years old by now, but still relevant in light of the trust issues raised by emerging AI technologies and the movement away from fact-checking mechanisms that ensure trust in online information. In his piece, Schneier makes a distinction between two types of trust: interpersonal and social. Interpersonal trust refers to the ways we might trust a friend: “We trust their intentions, and know that those intentions will inform their actions.” Social trust refers to the ways we might trust going out to eat at a restaurant, which is often a result of reliability and predictability. If we’ve eaten at a restaurant before, had a pleasant experience, and didn’t get sick, we continue to go out. Perhaps we trust the system of health codes, food standards, and workplace regulations that ensure the experience won’t harm us. Schneier writes, “Interpersonal trust and social trust are both essential in society today. This is how it works. We have mechanisms that induce people to behave in a trustworthy manner, both interpersonally and socially. This, in turn, allows others to be trusting. Which enables trust in society. And that keeps society functioning. The system isn’t perfect—there are always going to be untrustworthy people—but most of us being trustworthy most of the time is good enough.”
Schneier argues that we currently make categorical errors between interpersonal and social trust. We trust large companies–or at least their spokespeople or their “brand”–to have decent intentions, the way we might trust actual people. Furthermore, as new AI technologies develop, Schneier points out that we’ll mistakenly trust these technologies due to their more relational interfaces; they’ll seem to know our mannerisms and desires. However, they are not friends, but services. More importantly these services often work for profit-maximizing corporations. Unlike a friend, these services aren’t invested as much in our happiness or well-being as they are in making money from our engagement with them. We must keep this in mind as chatbots and other AI “agents” continue to proliferate and train on our data. We must keep this in mind as AI-generated content, some trustworthy and some not, continues to populate our information ecosystems. We highly recommend you read the full piece if you’d like to see some of Schneier’s recommendations for establishing trustworthy AI systems.
The second resource is an AI ethical use checklist by Turnitin. As we step into a new semester, fostering trust between students and instructors is more important than ever, especially in the era of generative AI. The checklist is a practical tool designed to help students use AI responsibly while strengthening transparency and communication in the teaching and learning process.
The checklist is structured to guide students through key stages: before and during the assignment and after the final submission. Before starting, students are encouraged to clarify instructor expectations around AI use, ensure their unique voice is evident, and document their writing process through brainstorming, drafts, and revisions. Open communication with instructors during this phase, especially asking clarifying questions helps align expectations and build trust. After submission, the checklist prompts students to evaluate whether they can clearly explain how and why they used AI, including their decision-making process and adherence to guidelines. Being prepared to discuss their work openly not only reinforces their accountability but also nurtures a collaborative, trust-filled relationship with their instructors.
This emphasis on proactive communication mirrors Bruce Schneier’s distinction between interpersonal and social trust. Just as reliable systems foster social trust, clear and honest dialogue between students and instructors strengthens interpersonal trust, which is essential for academic success.
Note: the section on Turnitin’s “AI Ethical Use Checklist” was revised with the input from ChatGPT.