ChatGPT’s memory feature is designed to provide users with greater control and more helpful responses. Yet, it remains unclear how users perceive this feature in relation to privacy. To address this gap, we conducted interviews with 20 ChatGPT users from diverse backgrounds. Our findings revealed four major characteristics that distinguish ChatGPT’s memory from human memory: perceived unforgetfulness, detailedness, accuracy, and lack of emotions, highlighting the machine-like nature of AI memory. Moreover, both ChatGPT’s memory and human memory were perceived as beneficial for relationship building. Notably, most participants experienced negative expectancy violations after learning what ChatGPT remembered about them. They expressed a strong need for greater visibility, accessibility, transparency, and user control in the design of future memory features. Drawing on users’ suggestions and theoretical frameworks on privacy management, we provide design implications for developing a more transparent, responsible, and user-aligned memory experience that helps them navigate privacy-personalization trade-offs when interacting with LLM-based memories.

Active listening is a well-known skill applied in human communication to build intimacy and elicit self-disclosure to support a wide variety of cooperative tasks. When applied to conversational UIs, active listening from machines can also elicit greater self-disclosure by signaling to the users that they are being heard, which can have positive outcomes. However, it takes considerable engineering effort and training to embed active listening skills in machines at scale, given the need to personalize active-listening cues to individual users and their specific utterances. A more generic solution is needed given the increasing use of conversational agents, especially by the growing number of socially isolated individuals. With this in mind, we developed an Amazon Alexa skill that provides privacy-preserving and pseudo-random backchanneling to indicate active listening. User study (N = 40) data show that backchanneling improves perceived degree of active listening by smart speakers. It also results in more emotional disclosure, with participants using more positive words. Perception of smart speakers as active listeners is positively associated with perceived emotional support. Interview data corroborate the feasibility of using smart speakers to provide emotional support. These findings have important implications for smart speaker interaction design in several domains of cooperative work and social computing.

"Always-on" smart speakers have raised privacy and security concerns, to address which vendors have introduced customizable privacy settings. But, does the act of customizing one's privacy preferences have any effects on user experience and trust? To address this question, we developed an app for Amazon Alexa and conducted a user study (N = 90). Our data show that the affordance to customize privacy settings enhances trust and usability for regular users, while it has adverse effects on power users. In addition, only enabling privacy-setting customization without allowing content customization negatively affects trust among users with higher privacy concerns. When they can customize both content and privacy settings, user trust is highest. That is, while privacy customization may cause reactance among power users, allowing privacy-concerned individuals to simultaneously customize content can help to alleviate the resultant negative effect on trust. These findings have implications for designing more privacy-sensitive and trustworthy smart speakers.

MModern day voice-activated virtual assistants allow users to share and ask for information that could be considered as personal through different input modalities and devices. Using Google Assistant, this study examined if the differences in modality (i.e., voice vs. text) and device (i.e., smartphone vs. smart home device) affect user perceptions when users attempt to retrieve sensitive health information from voice assistants. Major findings from this study suggest that voice (vs. text) interaction significantly enhanced perceived social presence of the voice assistant, but only when the users solicited less sensitive health-related information. Furthermore, when individuals reported less privacy concerns, voice (vs. text) interaction elicited positive attitudes toward the voice assistant via increased social presence, but only in the low (vs. high) information sensitivity condition. Contrary to modality, the device difference did not exert any significant impact on the attitudes toward the voice assistant regardless of the sensitivity level of the health information being asked or the level of individuals' privacy concerns.

Industry classification has been rigorously utilized in academic research and business analytics. The existing classification schemes, however, have been constructed and maintained manually by domain experts, which require exhaustive time and human effort while vulnerable to subjectivity. Hence, the existing classification systems do not properly reflect the fast-changing trends of the firms and the capital market. As a remedy to such shortcomings, this paper proposes a new classification scheme, Business Text Industry Classification (BTIC), namely, that automatically clusters securities based on the textual information from the corporate disclosures. BTIC exploits the business section of the Form 10-Ks, in which firms provide their self-identities in a rich context. We employ doc2vec for document embedding and apply Ward's hierarchical clustering method to categorize securities into BTIC groups. Evaluation results using 12 financial ratios commonly found in financial research show that BTIC performs just as good as SIC and GICS in terms of inter- and intra-industry homogeneity, especially for the higher level of clustering. Given that, we claim that BTIC outperforms SIC and GICS in four aspects: process automation, objectivity, clustering flexibility, and result interpretability.