Reducing electronic waste is one of the key topics in Sustainable Interaction Design. However, research regarding the sustainable future of the long-term use of robots is limited. Our study employs a game-based workshop to investigate the factors influencing potential users’ sustainability choices in long-term human-robot interactions. We developed a board game called “Game of Life with Your Companion Robot” to help participants situate themselves in the context of cohabiting with companion robots of their choice. Through five workshops with seventeen participants, we explore (a) the factors mentioned by participants that influence their sustainability choices in long-term human-robot interactions, and (b) the connections between how participants frame their companion robots and their sustainability choices. We use four sustainable criteria to evaluate participants’ choices. Our findings show that different framings of robots can result in different sustainable outcomes.

There are currently multiple proposed security label designs for consumer products, with each prioritizing different security and privacy factors. These differences risk making product comparisons more confusing than informative. Standardized labels could potentially resolve this by informing consumers of a product's security features at the point of purchase. But which standard? This survey, of 500 participants, studied four label designs and measured comprehension, response time, acceptability, and cognitive load. We gauged understanding of participant perception and preferences using three smart devices: light bulbs, cameras, and thermostats. We identified preferences and behaviors before, during, and after label use for product selection. At first, participants believed more information-dense labels would better support their purchasing behavior; however, after they evaluated and compared products, participants gravitated towards less cognitively demanding designs. We identified how participants utilized and prioritized label elements to provide recommendations for US label design efforts.

Systems worldwide deploy CAPTCHAs as a security mechanism to protect from unauthorized automated access. Typically, the effectiveness of CAPTCHAs is evaluated based on their resilience against bots. User perceptions of the interactive experience and effectiveness of CAPTCHAs have received less attention, especially for comparing the variations of CAPTCHAs presented in different regions across the world. As the first step toward filling this gap, we conducted semi-structured interviews with ten participants fluent in Chinese and English to investigate whether user perceptions are affected by variations in CAPTCHAs presented in China and the United States, respectively. We found notable differences in the perceived user experience and effectiveness across the different CAPTCHA types, but not across regional variations of the same type. Our findings point to a number of avenues for making the CAPTCHA user experience more universal and inclusive.

In Chinese Internet spaces, "fishing" is a form of non-literal communication that tries to lure or bait others. We conducted a case study of fishing on a Chinese Q&A platform in which we deconstructed the linguistic structure of the fishing language and identified its features by employing analysis techniques informed by Critical Discourse Analysis (CDA). Additionally, we examined the relationship between fishing practices and the specifics of the underlying communication platform. We found that fishing can be characterized as a reaction to rigid norms of politeness and friendliness imposed on users by platform owners. Our analysis reveals that fishing is organically connected with other social interaction within the community and facilitates autonomous and active negotiation of boundaries and linguistic norms for online discussion of controversial topics. We challenge the characterization of non-literal communication as "abnormal," and contribute a more refined understanding of online linguistic norms, community cohesion, and civil engagement.

Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction.For example, a female character in a story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor").We formulate Controllable Debiasing, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals.We then introduce POWERTRANSFORMER as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates.One key challenge of our task is the lack of parallel corpora.To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models.Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks.Furthermore, we demonstrate the use of POWER-TRANSFORMER as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.

Gaze interaction provides an efficient way for users to communicate and control in virtual reality (VR) presented by head-mounted displays. In gaze-based text-entry systems, eye tracking and brain-computer interface (BCI) are the two most commonly used approaches. This paper presents a hybrid BCI system for text entry in VR by combining steady-state visual evoked potentials (SSVEP) and eye tracking. The user interface in VR designed a 40-target virtual keyboard using a joint frequency-phase modulation method for SSVEP. Eye position was measured by an eye-tracking accessory in the VR headset. Target-related gaze direction was detected by combining simultaneously recorded SSVEP and eye position data. Offline and online experiments indicate that the proposed system can type at a speed around 10 words per minute, leading to an information transfer rate (ITR) of 270 bits per minute. The results further demonstrate the superiority of the hybrid method over single-modality methods for VR applications.