Jul 2026· International Conference on Conversational User Interfaces· pp. 1-6· 0 citations· 51 references
Computer Science
TL;DR
It is argued that intelligence alone cannot determine appropriate goals, guide action under uncertainty, or ensure beneficial human outcomes, and proposed artificial wisdom as a needed corrective for conversational systems.
Abstract
This paper argues that intelligence is the wrong primary design goal for conversational systems. As agents become more capable, proactive, and socially embedded, their most consequential failures stem less from weak task performance than from poor judgment: misframing problems, mishandling uncertainty, overlooking stakeholder interests, and steering users in ways that undermine autonomy and well-being. I propose artificial wisdom as a needed corrective for conversational systems. By artificial wisdom, I mean context-sensitive, morally grounded, meta-cognitively regulated judgment oriented toward human flourishing under uncertainty. This perspective shifts attention from what systems can do to how they should act when advising, persuading, and shaping decisions. I argue that intelligence alone cannot determine appropriate goals, guide action under uncertainty, or ensure beneficial human outcomes. On this basis, I sketch a wisdom-oriented agenda for conversational AI centered on role awareness, meta-cognition, deliberation, self-explanation, and calibrated proactivity. Based in this, I examine its promise, difficulty, and risks.
It is argued that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.
Vijay Keswani, Breanna K. Nguyen, Cyrus Cousins et al.· 0 citations
This work presents the findings of a conceptual review that describes four concepts of agency, including personal agency, social agency, institutional agency, and artificial agency, and argues for shifts from individualistic toward holistic approaches to developing agentic conversational AI systems that sustain the authors' diverse and evolving forms of human agency in everyday life.
Amid Ayobi, Benjamin Lucas Searle, Khalid Aadan et al.· International Conference on...· 0 citations
It is argued that psychological competence should become a core consideration for model providers, deploying organizations, researchers, and regulators concerned with the real-world effects of human-facing AI systems.
M. Economides, Paul M. Sacher, Samuel Salzer et al.· 0 citations
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts grounded in 57 semi-structured interviews with AI professionals in 2021 and 2023--before and after the recent surge of public interest. We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges, such as assigning responsibility for societal impacts. We develop a framework of three primary debates across which frames are adopted and contested: (i) the $\textit{method}$ of AI development, between frames of top-down expert systems and bottom-up emergent capabilities, (ii) the $\textit{mind}$ of an AI system, ranging from a passive tool to a humanlike"digital mind,"and (iii) the $\textit{morality}$ of how AI is used, particularly the decision of whether to slow down or speed up AI development. As humanity enters the era of transformative AI, technologists and policymakers must account for the framing dynamics that will circumscribe our beliefs, values, and actions.
Jacy Reese Anthis, Erik Brynjolfsson, James A. Evans· 0 citations
This primer draws on fieldwork in a computational biology laboratory to examine what human oversight of AI agents requires in practice and shows that effective oversight has four components: adequate knowledge of system capabilities and limitations, sufficient observation of system actions, meaningful control of system behavior, and timely intervention in system failures.
The paper argues that practical imitation may bypass this barrier by relying on belief and perceived equivalence rather than authentic internalization rather than authentic internalization, and may help ensure that AI remains an auxiliary tool rather than becoming a governing influence over human thought and action.