Climate of the Field: Snowmass 2021

How are formal policies put in place to create an inclusive, equitable, safe environment? How do these differ between different communities of practice (institutions, labs, collaborations, working groups)? What policies towards a more equitable community are working? For those that aren't working, what external support is needed in order to make them more effective? We present a discussion of the current climate of the field in high energy particle physics and astrophysics (HEPA), as well as current efforts toward making the community a more diverse, inclusive, and equitable environment. We also present issues facing both institutions and HEPA collaborations, with a set of interviews with a selection of HEPA collaboration DEI leaders. We encourage the HEPA community and the institutions & agencies that support it to think critically about the prioritization of people in HEPA over the coming decade, and what resources and policies need to be in place in order to protect and elevate minoritized populations within the HEPA community.Comment: Contribution to Snowmass 202

Do explicit review strategies improve code review performance? Towards understanding the role of cognitive load

Code review is an important process in software engineering – yet, a very expensive one. Therefore, understanding code review and how to improve reviewers’ performance is paramount. In the study presented in this work, we test whether providing developers with explicit reviewing strategies improves their review effectiveness and efficiency. Moreover, we verify if review guidance lowers developers’ cognitive load. We employ an experimental design where professional developers have to perform three code review tasks. Participants are assigned to one of three treatments: ad hoc reviewing, checklist, and guided checklist. The guided checklist was developed to provide an explicit reviewing strategy to developers. While the checklist is a simple form of signaling (a method to reduce cognitive load), the guided checklist incorporates further methods to lower cognitive demands of the task such as segmenting and weeding. The majority of the participants are novice reviewers with low or no code review experience. Our results indicate that the guided checklist is a more effective aid for a simple review,while the checklist supports reviewers’ efficiency and effectiveness in a complex task. However, we did not identify a strong relationship between the guidance provided and code review performance. The checklist has the potential to lower developers’ cognitive load, but higher cognitive load led to better performance possibly due to the generally low effectiveness and efficiency of the study participants. Data and materials: https://doi.org/10.5281/zenodo.5653341. Registered report: https://doi.org/10.17605/OSF.IO/5FPTJ. © 2022, The Author(s)

Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases similar-to-me bias and stereotype bias in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place

Predictors of Well-being and Productivity of Software Professionals during the COVID-19 Pandemic - A Longitudinal Study

The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus' reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers who were working from home. Although software professionals are accustomed to working with digital tools, but not all of them remotely, in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study (N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, coping strategies, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify the variables that explained unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual's well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) longitudinal analyses did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Overall, we conclude that working from home was per se not a significant challenge for software engineers. Finally, our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provides tailored insights for software professionals

Tutors’ Experiences in Using Explicit Strategies in a Problem-Based Learning Introductory Programming Course

In programming education, explicit strategies are gaining traction. The reason for this study was to improve an introductory programming course based on a problem-based methodology, by using more explicit programming strategies. After analysing a previous run of this course for first year undergraduate students, we concluded that such strategies could improve learning transfer for students across the different weeks of the semester. We introduced four instructional strategies to tutors with close to no pedagogical background: explicit tracing, subgoal labeled worked examples, Parsons problems and explicit problem solving. These explicit programming strategies aim to decrease cognitive load. Tutors tested these four strategies in the course. Our goal was to explore how tutors could benefit in their tutoring from explicit strategies. Interviews with the tutors show that the easiest and most effective of the tested strategies were best used. For the more elaborate strategies, more time should be devoted to explain and model them or they can be misunderstood and misapplied. We conclude that four criteria are key to successfully using an explicit strategy: easy to understand, straightforward to apply, useful on the long term and supported by literature

Promoting Learning Transfer in Computer Science Education by Training Teachers to use Explicit Programming Strategies

Some Computer Science concepts, and programming in particular, are hard to learn. As CS is (re-)entering national school curricula throughout the world, qualified CS teachers need to be trained. In this PhD work we will propose a training that will help teachers teach those concepts effectively. Based on the educational framework of learning transfer and cognitive load theory, we will do this through evidence-based instructional strategies. These explicit programming strategies aim to decrease cognitive load and foster learning transfer. My PhD will advance the topic of CS teacher training by understanding how CS teachers apply those programming strategies in their teaching through qualitative studies and by designing a validated training that can be used with tutors and teacher trainers