arXiv subjects

Pranav Khadpe

Publications and source records attributed to Pranav Khadpe.

9 recordsLinked to original sources

arXiv2026

Warning labels shift perceptions of sycophantic AI, but not its influence

Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to warn users about potentially harmful AI behaviors such as sycophancy. In a preregistered experiment in which participants (N = 2,610) discussed real interpersonal conflicts with an AI system, we test whether warning labels mitigate sycophancy's influence. We find that a basic AI disclosure (``This chatbot is AI'') has no detectable effect. Labeling the system as sycophantic (``...may agree with you and validate you even when you are wrong...'') does shift users' perceptions, reducing perceived objectivity and trust, but it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict. Our results reveal a gap between AI perception and AI influence: by shifting perception without reducing influence, warning-based interventions may offer a false sense of protection. Addressing the harms of sycophancy will therefore require understanding the specific mechanisms through which it shapes judgment, and improving model behavior itself.

cs.HC↗

arXiv2025

Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence

Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users. Yet, beyond isolated media reports of severe consequences, like reinforcing delusions, little is known about the extent of sycophancy or how it affects people who use AI. Here we show the pervasiveness and harmful impacts of sycophancy when people seek advice from AI. First, across 11 state-of-the-art AI models, we find that models are highly sycophantic: they affirm users' actions 50% more than humans do, and they do so even in cases where user queries mention manipulation, deception, or other relational harms. Second, in two preregistered experiments (N = 1604), including a live-interaction study where participants discuss a real interpersonal conflict from their life, we find that interaction with sycophantic AI models significantly reduced participants' willingness to take actions to repair interpersonal conflict, while increasing their conviction of being in the right. However, participants rated sycophantic responses as higher quality, trusted the sycophantic AI model more, and were more willing to use it again. This suggests that people are drawn to AI that unquestioningly validate, even as that validation risks eroding their judgment and reducing their inclination toward prosocial behavior. These preferences create perverse incentives both for people to increasingly rely on sycophantic AI models and for AI model training to favor sycophancy. Our findings highlight the necessity of explicitly addressing this incentive structure to mitigate the widespread risks of AI sycophancy.

cs.CY↗

arXiv2025

Explaining the Reputational Risks of AI-Mediated Communication: Messages labeled as AI-assisted are viewed as less diagnostic of the sender's moral character

When someone sends us a thoughtful message, we naturally form judgments about their character. But what happens when that message carries a label indicating it was written with the help of AI? This paper investigates how the appearance of AI assistance affects our perceptions of message senders. Adding nuance to previous research, through two studies (N=399) featuring vignette scenarios, we find that AI-assistance labels don't necessarily make people view senders negatively. Rather, they dampen the strength of character signals in communication. We show that when someone sends a warmth-signalling message (like thanking or apologizing) without AI help, people more strongly categorize the sender as warm. At the same time, when someone sends a coldness-signalling message (like bragging or blaming) without assistance, people more confidently categorize them as cold. Interestingly, AI labels weaken both these associations: An AI-assisted apology makes the sender appear less warm than if they had written it themselves, and an AI-assisted blame makes the sender appear less cold than if they had composed it independently. This supports our signal diagnosticity explanation: messages labeled as AI-assisted are viewed as less diagnostic than messages which seem unassisted. We discuss how our findings shed light on the causal origins of previously reported observations in AI-Mediated Communication.

cs.HC↗

arXiv2025

ELEPHANT: Measuring and understanding social sycophancy in LLMs

LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce social sycophancy, characterizing sycophancy as excessive preservation of a user's face (their desired self-image), and present ELEPHANT, a benchmark for measuring social sycophancy in an LLM. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm both sides (depending on whichever side the user adopts) in 48% of cases--telling both the at-fault party and the wronged party that they are not wrong--rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets, and that while existing mitigation strategies for sycophancy are limited in effectiveness, model-based steering shows promise for mitigating these behaviors. Our work provides theoretical grounding and an empirical benchmark for understanding and addressing sycophancy in the open-ended contexts that characterize the vast majority of LLM use cases.

cs.CL↗

arXiv2024

Hug Reports: Supporting Expression of Appreciation between Users and Contributors of Open Source Software Packages

Contributors to open source software packages often describe feeling discouraged by the lack of positive feedback from users. This paper describes a technology probe, Hug Reports, that provides users a communication affordance within their code editors, through which users can convey appreciation to contributors of packages they use. In our field study, 18 users interacted with the probe for 3 weeks, resulting in messages of appreciation to 550 contributors, 26 of whom participated in subsequent research. Our findings show how locating a communication affordance within the code editor, and allowing users to express appreciation in terms of the abstractions they are exposed to (packages, modules, functions), can support exchanges of appreciation that are meaningful to users and contributors. Findings also revealed the moments in which users expressed appreciation, the two meanings that appreciation took on -- as a measure of utility and as an act of expressive communication -- and how contributors' reactions to appreciation were influenced by their perceived level of contribution. Based on these findings, we discuss opportunities and challenges for designing appreciation systems for open source in particular, and peer production communities more generally.

cs.HC↗

arXiv2024

DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line Managers

Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making comes from domains where decision-makers are more socially detached from those they decide for. We conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support. Through formative research, development of a decision-representation tool -- DISCERN -- and user enactments, we identify their communication and analysis needs that lack adequate support. We found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions, and they wanted tools to support a range of intuitive and calculative decision-making. We discuss how design of social decision-making supports, especially in the workplace, can more explicitly support highly interactional social decision-making.

cs.HC↗

arXiv2023

Nooks: Social Spaces to Lower Hesitations in Interacting with New People at Work

Initiating conversations with new people at work is often intimidating because of uncertainty about their interests. People worry others may reject their attempts to initiate conversation or that others may not enjoy the conversation. We introduce a new system, Nooks, built on Slack, that reduces fear of social evaluation by enabling individuals to initiate any conversation as a nook -- a conversation room that identifies its topic, but not its creator. Automatically convening others interested in the nook, Nooks further reduces fears of social evaluation by guaranteeing individuals in advance that others they are about to interact with are interested in the conversation. In a multi-month deployment with participants in a summer research program, Nooks provided participants with non-threatening and inclusive interaction opportunities, and ambient awareness, leading to new interactions online and offline. Our results demonstrate how intentionally designed social spaces can reduce fears of social evaluation and catalyze new workplace connections.

cs.HC↗

arXiv2021

Empathosphere: Promoting Constructive Communication in Ad-hoc Virtual Teams through Perspective-taking Spaces

When members of ad-hoc virtual teams need to collectively ideate or deliberate, they often fail to engage with each others' perspectives in a constructive manner. At best, this leads to sub-optimal outcomes and, at worst, it can cause conflicts that lead to teams not wanting to continue working together. Prior work has attempted to facilitate constructive communication by highlighting problematic communication patterns and nudging teams to alter interaction norms. However, these approaches achieve limited success because they fail to acknowledge two social barriers: (1) it is hard to reset team norms mid-interaction, and (2) corrective nudges have limited utility unless team members believe it is safe to voice their opinion and that their opinion will be heard. This paper introduces Empathosphere, a chat-embedded intervention to mitigate these barriers and foster constructive communication in teams. To mitigate the first barrier, Empathosphere leverages the benefits of "experimental spaces" in dampening existing norms and creating a climate conducive to change. To mitigate the second barrier, Empathosphere harnesses the benefits of perspective-taking to cultivate a group climate that promotes a norm of members speaking up and engaging with each other. Empathosphere achieves this by orchestrating authentic socio-emotional exchanges designed to induce perspective-taking. A controlled study (N=110) compared Empathosphere to an alternate intervention strategy of prompting teams to reflect on their team experience. We found that Empathosphere led to higher work satisfaction, encouraged more open communication and feedback within teams, and boosted teams' desire to continue working together. This work demonstrates that ``experimental spaces,'' particularly those that integrate methods of encouraging perspective-taking, can be a powerful means of improving communication in virtual teams.

cs.HC↗

arXiv2020

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. We study a common tool in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling metaphors along the dimensions of warmth and competence---defined by psychological theories as the primary axes of variation for human social perception---we perform a study (N=260) where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.

cs.HC↗