Generative AI and Contextual Confidence: Discussion, Acknowledgements and References

Table of Links

Abstract & Introduction

Challenges to Contextual Confidence from Generative AI

Strategies to Promote Contextual Confidence

Discussion, Acknowledgements and References

4 Discussion

4.1 Enforcing Protective Norms

In this paper, we have highlighted strategies that promote contextual confidence by setting new norms and expectations around the identification and protection of context. This discussion leaves out an important aspect of contextual confidence: It is important not only that there are norms, but that the norms are respected. Indeed, a norm cannot become a norm if there is no expectation that it is respected.

There are two primary stages to any enforcement strategy to protect norms: commitment strategies and accountability strategies.

Commitment strategies aim to create shared knowledge of the context that requires protecting. For example, a participant in a video call might “commit” to acknowledging that the meeting is being recorded, implicitly agreeing to the Terms of Service pertaining to that recording. Such a commitment engenders shared awareness among participants about two key aspects: the ongoing recording of the meeting and the terms governing the use of the information derived from it. Commitment in this example enhances the protection of contextual confidence from the default case in which participants may inappropriately record conversations to then reuse or repurpose content from those conversations. However, commitment alone does not enforce the protection of context for participants who do not respect their commitments.

Accountability strategies ensure that participants in a communicative exchange are held accountable if they violate their commitments. In some cases, the law naturally provides accountability. In the Terms of Service example above, a violation of Terms of Service may be treated as a breach of contract, which can potentially be litigated in court. In other cases, the law will not as readily provide accountability, and specific organizations and platforms will need to develop their own mechanisms for accountability.

Existing commitment and accountability strategies are sparse and require further development and experimentation. While some instant messaging platforms inform users if their communication partner has taken a screenshot of their chat, this simple commitment tool is far from standard or widespread. While some platforms have robust systems for reporting fake accounts, even these accountability technologies are often ineffective. While, as discussed above, prominent video calling services have deployed features that announce when the meeting is being recorded, individuals may not understand Terms of Service documents in full for each video call in which they participate. Thus, while there may be some commitment in the video calling case, this commitment may not be well understood.

4.2 Open Questions and Future Work

We hope this paper serves as a modest starting point for future collaborations between policymakers, AI model developers and researchers, focused on applying a contextual confidence perspective in particular domains. We outline here a few areas of future work that may be especially valuable in the near-term.

First, pragmatically defining what constitutes “context” is challenging in some domains. The concept of context itself is somewhat slippery, and certain elements of context are more important than others in particular domains. Identifying the “who, why, where, when, and how” in every information flow may be impractical at some scales of applications, and this paper offered little guidance as to how to prioritize the most important elements of context. It would be useful to build toward a standardization of what qualifies as a comprehensive contextual confidence evaluation that could be incorporated into safety reviews, or even model cards.

Second, we hope that this paper serves as a call to action for prioritizing the research and development of strategies that promote contextual confidence. Many of the strategies we discuss in this paper are in early stages of development and need a lot more research and development before they can be deployed. In addition, our enumeration of strategies is far from exhaustive. For example, we only offered cursory discussions of how AI models themselves can be used to strengthen strategies for promoting contextual confidence.

Third, it is critical to conduct empirical usability studies and surveys about whether and how the strategies we discussed indeed promote new norms in communication. Some of the strategies we discussed may have unforeseen consequence when applied in particular situations – like the surprising findings about how verification badges and fact checking tools may backfire in some contexts [38, 39]. In addition, it is important to gather data on the degree to which the strategies discussed here are differentially usable and accessible for different participants.

In this paper, we focused on the ways generative AI challenges contextual confidence. As communication technologies continue to evolve, challenges to contextual confidence will continue to emerge beyond generative AI. For instance, advancements in augmented reality and robotics may bring a whole other set of difficulties into the identification and protection of context in the physical world. It is our hope that framing challenges to effective communication in terms of contextual confidence will be useful in forthcoming stages of technological development.

Acknowledgements

We thank Glen Weyl, Danielle Allen, Allison Stanger, Miles Brundage, Sarah Kreps, Karen Easterbrook, Tobin South, Christian Paquin, Daniel Silver and Saffron Huang for comments and conversations that improved the paper.

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