Chatbot apologies: Beyond bullshit (2501.09910v2)

Abstract: Apologies serve essential functions for moral agents such as expressing remorse, taking responsibility, and repairing trust. LLM-based chatbots routinely produce output that has the linguistic form of an apology. However, they do this simply because they are echoing the kinds of things that humans say. Moreover, there are reasons to think that chatbots are not the kind of linguistic or moral agents capable of apology. To put the point bluntly: Chatbot apologies are bullshit. This paper explores this concern and develops it beyond the epithet, drawing on the nature of morally serious apologies, the linguistic agency required to perform them, and the moral agency required for them to matter. We conclude by considering some consequences for how chatbots should be designed and how we ought to think about them.

• The paper argues LLM chatbots lack the necessary linguistic and moral agency to offer genuine apologies, classifying their outputs as rote despite linguistic resemblance.
• The analysis shows chatbot apologies fail key criteria for genuine apologies, such as sincerity, intent, and a commitment to change, due to the lack of true agency.
• Caution is advised for developers, as chatbot apologies can mislead users about AI's agency; careful presentation and informing users of their machine nature are recommended.

An Examination of Chatbot Apologies: Evaluating Linguistic and Moral Agency in Artificial Intelligence

The paper "Chatbot Apologies: Beyond Bullshit," authored by P.D. Magnus, Alessandra Buccella, and Jason D'Cruz, provides a critical analysis of the capacity of LLM-based chatbots to execute genuine apologies. While LLM chatbots routinely produce language that resembles human apologies, the paper postulates that these outputs are devoid of true linguistic and moral agency, rendering them incapable of fulfilling the customary roles required of a meaningful apology.

The focus of the paper falls on deconstructing the nature of chatbot-generated apologies, classifying them within the rubric established by Nick Smith's categorical apology framework, which sets a high benchmark through twelve features encompassing acknowledgment, responsibility, shared moral principles, emotional sincerity, and a commitment to reform. Through a foundational analysis, the paper demonstrates chatbots' categorical failure to meet these criteria, thus classifying their apologetic expressions as mere rote apologies devoid of substantive communicative intent.

One of the core arguments posited is the deficit of linguistic agency in chatbots. The debate hinges on whether chatbots might possess "belief-like" representations, but the paper articulates that even granting this potential, chatbots inherently lack the performative capacity intrinsic to a legitimate apology. The performative nature of an apology, as theorized by J.L. Austin and others, necessitates expressive acts indicative of intent, emotional states—such as guilt or remorse—and a commitment to rectification. These elements, argues the paper, are intrinsically tied to entities possessing self-awareness and moral agency, both characteristics absent in current AI systems.

Moreover, the paper provides a rationale on the limitation of extending notions of quasi-testimony to chatbot apologies, arguing that while users might unconsciously treat chatbot assertions as quasi-assertions, the failure lies in the absence of a prop-oriented or world-oriented value to imagined chatbot apologies. As such, these interactions do not provide tangible epistemic or moral value equivalent to what could conceivably be perceived in assertive outputs.

In evaluating the implications of these findings for AI design, the paper advises caution for developers. The tendency of chatbots to produce apology-like outputs could potentially mislead users, leading to misconceptions about the agency and ethical standings of these systems. Regulating the presentation of chatbots as possessing moral attributes and ensuring user awareness of their machine nature are pivotal recommendations to mitigate ethical ambiguities.

The paper concludes by articulating the philosophical and practical implications of chatbot apologies, resonating with the critiques of Harry Frankfurt regarding "bullshit"—though in an extended, applied sense. While the dynamic and natural-seeming language generated by chatbots may superficially appear substantive, the paper clearly delineates the limitations inherent in attributing genuine agency to LLMs.

Future developments in AI may enhance the structural complexity and contextual awareness in chatbot outputs, perhaps leading to more nuanced social interactions. However, without a concurrent foundation for moral and linguistic agency, the outputs of chatbots will continue to be limited in their resemblances to human interactions governed by intricate social contracts and ethical judgments. As AI evolves, the reconciliation between technological capability and ethical communication remains a critical frontier.