# New contexts, old heuristics: How young people in India and the US trust online content in the age of generative AI

arXiv: https://arxiv.org/abs/2405.02522 · HTML: https://mlsanity.com/paper/2405.02522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 70 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 6 of 6)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Scientists' Perspectives on the Potential for Generative AI in their Fields](https://mlsanity.com/paper/2304.01420/) (2023)
- [Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc](https://mlsanity.com/paper/2308.04445/) (2023)
- [The Social Impact of Generative AI: An Analysis on ChatGPT](https://mlsanity.com/paper/2403.04667/) (2024)
- [Like-minded, like-bodied: How users (18-26) trust online eating and health information](https://mlsanity.com/paper/2402.18753/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI and the Future of Digital Public Squares](https://mlsanity.com/paper/2412.09988/) (2024)

## Abstract (arXiv)

We conducted in-person ethnography in India and the US to investigate how young people (18-24) trusted online content, just as generative AI (genAI) became mainstream. We found that when online, how participants determined what content to trust was shaped by emotional states, which we term "information modes." Our participants reflexively shifted between modes to maintain "emotional equilibrium," and eschewed engaging literacy skills in the more passive modes in which they spent the most time. We found participants imported trust heuristics from established online contexts into emerging ones (i.e., genAI). This led them to use ill-fitting trust heuristics, and exposed them to the risk of trusting false and misleading information. While many had reservations about AI, prioritizing efficiency, they used genAI and habitual heuristics to quickly achieve goals at the expense of accuracy. We conclude that literacy interventions designed to match users' distinct information modes will be most effective.