LGBTQ+ individuals are increasingly turning to chatbots powered by large language models (LLMs) to meet their mental health needs. However, little research has explored whether these chatbots can adequately and safely provide tailored support for this demographic. We interviewed 18 LGBTQ+ and 13 non-LGBTQ+ participants about their experiences with LLM-based chatbots for mental health needs. LGBTQ+ participants relied on these chatbots for mental health support, likely due to an absence of support in real life. Notably, while LLMs offer prompt support, they frequently fall short in grasping the nuances of LGBTQ-specific challenges. Although fine-tuning LLMs to address LGBTQ+ needs can be a step in the right direction, it isn’t the panacea. The deeper issue is entrenched in societal discrimination. Consequently, we call on future researchers and designers to look beyond mere technical refinements and advocate for holistic strategies that confront and counteract the societal biases burdening the LGBTQ+ community.

As dating websites are becoming an essential part of how people meet intimate and romantic partners, it is vital to design these systems to be resistant to, or at least do not amplify, bias and discrimination. Instead, the results of our online experiment with a simulated dating website, demonstrate that popular dating website design choices, such as the user of the swipe interface (swiping in one direction to indicate a like and in the other direction to express a dislike) and match scores, resulted in people racially biases choices even when they explicitly claimed not to have considered race in their decision-making. This bias was significantly reduced when the order of information presentation was reversed such that people first saw substantive profile information related to their explicitly-stated preferences before seeing the profile name and photo. These results indicate that currently-popular design choices amplify people’s implicit biases in their choices of potential romantic partners, but the effects of the implicit biases can be reduced by carefully redesigning the dating website interfaces.

Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles. Our unsupervised and active learning methods are not only able to recognize the major causes of HWC such as construction, pollution, and farming, but can also classify an unseen news article into its major cause with 90% accuracy. Moreover, our interactive visualizations of the knowledge base illustrate the spatial and temporal trend of human-wildlife conflicts across India for index by cities and animals. Based on our findings that most conflict zones include areas where human settlements are near forested areas, we extend our study to include satellite imagery to identify such proximity zones. We conduct a case study to use this method to identify human-elephant conflict hotspots in northern and western parts of the Indian state of West Bengal. We expect that our findings can inform the public of HWC hotspots and help in much more informed policymaking.

Productivity behavior change systems help us reduce our time on unproductive activities. However, is that time actually saved, or is it just redirected to other unproductive activities? We report an experiment using HabitLab, a behavior change browser extension and phone application, that manipulated the frequency of interventions on a focal goal and measured the effects on time spent on other applications and platforms. We find that, when intervention frequency increases on the focal goal, time spent on other applications is held constant or even reduced. Likewise, we find that time is not redistributed across platforms from browser to mobile phone or vice versa. These results suggest that any conservation of procrastination effect is minimal, and that behavior change designers may target individual productivity goals without causing substantial negative second-order effects.