Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions
Nicole Mitchell, Dhruv Agarwal, Maty Bohacek, Remi Denton, Roma Patel
Why It Matters
What makes this one worth your time
Understanding the long-term implications of AI interactions is crucial for developing safer and more beneficial AI technologies.
This work advocates for a shift towards longitudinal studies in human-AI interactions to identify long-term risks.
Summary
The paper proposes a framework for understanding long-term effects of human-AI interactions, emphasizing the need for longitudinal measurements to assess cognitive and socio-affective changes in users over time.
Key contributions
- Proposes a new framework for longitudinal evaluation of human-AI interactions.
- Identifies specific cognitive and socio-affective risks associated with AI usage.
- Suggests integration of social science methodologies into NLP research.
Notable insights
- Combining social science measurement techniques with NLP could enhance the understanding of behavioral changes over time.
- The focus on online detection of problematic behaviors could lead to proactive rather than reactive safety measures.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.02491v2 Announce Type: replace Abstract: Language models have taken on the role of a very new type of technology, by virtue of their "human-ness" and rapid integration into users' daily lives. This combination of features can introduce longitudinal risks---cognitive, developmental and socio-affective changes in humans---that might not surface during a short-term interaction, but can have lasting long-term effects on users. This forms the basis of a critical new mission for NLP: to pivot from static, short-term evaluations of text generations to long-term measurements of behavioral changes, towards a diachronic understanding of human-model interactions. In this work, we draw from measurements used in social science fields that are crucial to understand emergent phenomena in longitudinal data. We discuss how computational methods in the field of NLP need to be combined with such measurements, not only to understand long-term safety risks of human-model interactions, but to help steer model development towards positive rather than negative outcomes for users. This ability to model human behavioral shifts as a function of model interactions can facilitate online rather than post-hoc detection of problematic behaviors, and should be leveraged in alignment frameworks to mitigate long-term risks in users.