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Enterprise social network messaging sites are becoming increasingly popular for team communication in engineering and product design. These digital communication platforms capture detailed messages between members of the design team and are an appealing data set for researchers who seek to better understand communication in design. This exploratory study investigates whether we can use enterprise social network messages to model communication patterns throughout the product design process. We apply short text topic modelling (STTM) to a data set comprising 250,000 messages sent by 32 teams enrolled in a 3-month intensive product design course. Many researchers describe the engineering design process as a series of convergent and divergent thinking stages, such as the popular double diamond model, and we use this theory as a case study in this work. Quantitative and qualitative analysis of STTM results reveals several trends, such as it is indeed possible to see evidence of cyclical convergence and divergence of topics in team communication; within the convergence–divergence pattern, strong teams have fewer topics in their topic models than weaker teams; and teams show characteristics of product, project, course, and other themes within each topic. We provide evidence that the analysis of enterprise social networking messages, with advanced topic modelling techniques, can uncover insights into design processes and can identify the communication patterns of successful teams.

The data created from virtual communication platforms presents the opportunity to explore automated measures for monitoring team performance. In this work, we explore one important characteristic of successful teams - Psychological Safety - or the belief that a team is safe for interpersonal risk-taking. To move towards an automated measure of this phenomenon, we derive virtual communication characteristics and message keywords related to elements of Psychological Safety from the literature. Using a mixed methods approach, we investigate whether these characteristics are present in the Slack messages from two design teams - one high in Psychological Safety, and one low. We find that some usage characteristics, such as replies, reactions, and user mentions, might be promising metrics to indicate higher levels of Psychological Safety, while simple keyword searches may not be nuanced enough. We present the first step towards the automated detection of this important, yet complex, team characteristic., Comment: Published as an extended abstract in the ACM Conference on Human Factors in Computing Systems (CHI) 2024

An essential aspect of evaluating Large Language Models (LLMs) is identifying potential biases. This is especially relevant considering the substantial evidence that LLMs can replicate human social biases in their text outputs and further influence stakeholders, potentially amplifying harm to already marginalized individuals and communities. Therefore, recent efforts in bias detection invested in automated benchmarks and objective metrics such as accuracy (i.e., an LLMs output is compared against a predefined ground truth). Nonetheless, social biases can be nuanced, oftentimes subjective and context-dependent, where a situation is open to interpretation and there is no ground truth. While these situations can be difficult for automated evaluation systems to identify, human evaluators could potentially pick up on these nuances. In this paper, we discuss the role of human evaluation and subjective interpretation to augment automated processes when identifying biases in LLMs as part of a human-centred approach to evaluate these models., Comment: HEAL Workshop at CHI Conference on Human Factors in Computing Systems, May 12, 2024, Honolulu, HI, USA

While distributed workers rely on scheduled meetings for coordination and collaboration, these meetings can also challenge their ability to focus. Protecting worker focus has been addressed from a technical perspective, but companies are now attempting organizational interventions, such as meeting-free weeks. Recognizing distributed collaboration as a sociotechnical challenge, we first present an interview study with distributed workers participating in meeting-free weeks at an enterprise software company. We identify three orientations workers exhibit during these weeks: Focus, Collaborative, and Time-Bound, each with varying levels and use of unstructured time. These different orientations result in challenges in attention negotiation, which may be suited for technical interventions. This motivated a follow-up study investigating attention negotiation and the compensating mechanisms workers developed during meeting-free weeks. Our framework identified tensions between the attention-getting and attention-delegation strategies. We extend past work to show how workers adapt their virtual collaboration mechanisms in response to organizational interventions, Comment: Published at the ACM Conference on Human Factors in Computing Systems (CHI) 2024

As large language models (LLMs) advance to produce human-like arguments in some contexts, the number of settings applicable for human-AI collaboration broadens. Specifically, we focus on subjective decision-making, where a decision is contextual, open to interpretation, and based on one's beliefs and values. In such cases, having multiple arguments and perspectives might be particularly useful for the decision-maker. Using subtle sexism online as an understudied application of subjective decision-making, we suggest that LLM output could effectively provide diverse argumentation to enrich subjective human decision-making. To evaluate the applicability of this case, we conducted an interview study (N=20) where participants evaluated the perceived authorship, relevance, convincingness, and trustworthiness of human and AI-generated explanation-text, generated in response to instances of subtle sexism from the internet. In this workshop paper, we focus on one troubling trend in our results related to opinions and experiences displayed in LLM argumentation. We found that participants rated explanations that contained these characteristics as more convincing and trustworthy, particularly so when those opinions and experiences aligned with their own opinions and experiences. We describe our findings, discuss the troubling role that confirmation bias plays, and bring attention to the ethical challenges surrounding the AI generation of human-like experiences., Comment: Presented at the Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop at CHI 2024

The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directions for future work. One factor that has been shown to predict persistence in engineering is belonging; we study belonging through the lens of confidence, and discuss how attention to social belonging confidence may help to increase diversity in the profession. In this research paper, we conduct a small set of interviews with students in ML/AI courses. Thematic analysis of these interviews revealed initial differences in how students see a career in ML/AI, which diverge based on interest and programming confidence. We identified how exposure and initiation, the interpretation of ML and AI field boundaries, and beliefs of the skills required to succeed might influence students' intentions to persist. We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship. We motivate further persistence research in ML/AI with particular focus on social belonging and close mentorship, the role of intersectional identity, and introductory ML/AI courses., Comment: Published in the 2023 Annual Conference of the American Society for Engineering Education

Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diversity among those in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI by surveying students in ML/AI courses. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from other STEM fields, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence is associated with academic enrollment factors such as major and level of study. Long-term intentional persistence is correlated with measures of professional role confidence. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence, and women generally care more about this. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field., Comment: Presented at the 2022 Annual Conference of the American Society for Engineering Education

Purpose – The purpose of this paper is to provide first-hand reflective narratives from participants of their involvement in the overall process, with particular reference to the benefits and challenges of engagement. Design/methodology/approach – Five participants agreed to write a reflective piece of approximately 500 words on their involvement in the PhotoVoice project. Findings – The reflective narratives in this paper demonstrate the personal and professional benefits of sustained and meaningful engagement, while challenges such as power imbalances, identity management, time and cost commitments are discussed. Practical implications – PhotoVoice is a methodology that has the potential to democratise knowledge production and dissemination. Originality/value – There are scant examples in the PhotoVoice literature of the inclusion of participants involvement in dissemination activities. The reflective narratives in this paper demonstrate the personal and professional benefits of sustained and meaningful engagement, while challenges such as power imbalances, identity management, time and cost commitments are discussed. Keywords Mental health, Advocacy, Alternative interventions, PhotoVoice, Service user engagement, Public and patient involvement Paper type Case study.

Purpose – The purpose of this paper is to provide first-hand reflective narratives from participants of their involvement in the overall process, with particular reference to the benefits and challenges of engagement. Design/methodology/approach – Five participants agreed to write a reflective piece of approximately 500 words on their involvement in the PhotoVoice project. Findings – The reflective narratives in this paper demonstrate the personal and professional benefits of sustained and meaningful engagement, while challenges such as power imbalances, identity management, time and cost commitments are discussed. Practical implications – PhotoVoice is a methodology that has the potential to democratise knowledge production and dissemination. Originality/value – There are scant examples in the PhotoVoice literature of the inclusion of participants involvement in dissemination activities. The reflective narratives in this paper demonstrate the personal and professional benefits of sustained and meaningful engagement, while challenges such as power imbalances, identity management, time and cost commitments are discussed. Keywords Mental health, Advocacy, Alternative interventions, PhotoVoice, Service user engagement, Public and patient involvement Paper type Case study.

4.1 Introduction: PhotoVoice is a participatory methodology in which marginalized communities represent their lived experiences through photography. While the methodology aims to foster partnerships throughout all phases, the literature suggests that in the field of mental health, some phases are often completed without participant involvement. 4.2 Aims: This paper elaborates on how the PhotoVoice method was used to engage service users and family members around their experience of involvement in a co-produced and co-facilitated mental health education intervention, in order to enhance public and policymakers knowledge of the project. 4.3 Methods: Ten participants were recruited and trained in the PhotoVoice method. Participants documented, through photography, their experiences of involvement in the education intervention. Following this, participants came together to co-produce and disseminate the photography exhibition to the public. 4.4 Results: PhotoVoice proved to be a flexible and creative method by which to include marginalized groups. By adhering to the collaborative principles of the methodology, service users and family members can retain decision-making power from the project's inception to its conclusion. 4.5 Implications for Practice: PhotoVoice is ideal for use within mental health nursing as it coincides with the recovery principle of promoting collaborative partnership between service users, family members and clinicians.

4.1 Introduction: PhotoVoice is a participatory methodology in which marginalized communities represent their lived experiences through photography. While the methodology aims to foster partnerships throughout all phases, the literature suggests that in the field of mental health, some phases are often completed without participant involvement. 4.2 Aims: This paper elaborates on how the PhotoVoice method was used to engage service users and family members around their experience of involvement in a co-produced and co-facilitated mental health education intervention, in order to enhance public and policymakers knowledge of the project. 4.3 Methods: Ten participants were recruited and trained in the PhotoVoice method. Participants documented, through photography, their experiences of involvement in the education intervention. Following this, participants came together to co-produce and disseminate the photography exhibition to the public. 4.4 Results: PhotoVoice proved to be a flexible and creative method by which to include marginalized groups. By adhering to the collaborative principles of the methodology, service users and family members can retain decision-making power from the project's inception to its conclusion. 4.5 Implications for Practice: PhotoVoice is ideal for use within mental health nursing as it coincides with the recovery principle of promoting collaborative partnership between service users, family members and clinicians.

Disruptive school behaviors in the black male child are of much concern to educators. While not developed specifically for use with black males, the Getting Away Clean program was designed to help children develop productive thinking skills, and to empower children to negotiate the social environment. Decision-making skills are strengthened, and children learn to counteract negative peer pressure and to relate positively to others. Selected fifth and sixth grade black male students from two elementary schools in a metropolitan Virginia school system were identified for the study. Eight students who were identified as those who had exhibited disruptive behaviors in the school setting, or those who needed skills to counteract negative peer pressure,were assigned to the treatment group. A group of eight students from the elementary schools was identified for the control group. The Child Behavior Checklist (CBCL, Edelbrock and Achenbach, 1983) was used to obtain data from parents, and the Teacher's Report Form (TRF, Edelbrock and Achenbach, 1983) was used to obtain data from teachers. In order to obtain pre-test data, teachers of the treatment and control group subjects for the 1987-88 school year completed the TRF. At the end of the treatment post-test data were obtained by requesting that teachers of the 1988-89 school year complete the TRF for treatment and control group subjects. The CBCL was also completed by parents of subjects in the treatment group. The statistical analyses used to determine differences between the two measures were the Rank Tranform Analysis of Covariance, Mann-Whitney Wilcoxon nonparametric tests and Wilcoxon Signed Rank Tests. Results indicated positive, but nonstatistically significant, differences between the pre and post measures. In addition, four subscales (Anxious, Social Withdrawal, Inattentive, and Aggressive) were statistically analyzed with the Multivariate Tests of Significance. Positive, but nonstatistically significant, differenc