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Past work has sought to design AI ethics interventions–such as checklists or toolkits–to help practitioners design more ethical AI systems. However, other work demonstrates how these interventions may instead serve to limit critique to that addressed within the intervention, while rendering broader concerns illegitimate. In this paper, drawing on work examining how standards enact discursive closure and how power relations affect whether and how people raise critique, we recruit three corporate teams, and one activist team, each with prior context working with one another, to play a game designed to trigger broad discussion around AI ethics. We use this as a point of contrast to trigger reflection on their teams’ past discussions, examining factors which may affect their “license to critique” in AI ethics discussions. We then report on how particular affordances of this game may influence discussion, and find that the hypothetical context created in the game is unlikely to be a viable mechanism for real world change. We discuss how power dynamics within a group and notions of “scope” affect whether people may be willing to raise critique in AI ethics discussions, and discuss our finding that games are unlikely to enable direct changes to products or practice, but may be more likely to allow members to find critically-aligned allies for future collective action. 

Corporate involvement in open source software (OSS) communities has increased substantially in recent years. Often this takes the form of company employees devoting their time to contribute code to the efforts of projects in these communities. Ideology has traditionally served to motivate, coordinate, and guide volunteer contributions to OSS communities. As employees represent an increasing proportion of the participants in OSS communities, the role of OSS ideology in guiding their commitment and code contributions is unknown. In this research, we argue that OSS ideology misfit has important implications for companies and the OSS communities to which their employees contribute, since their engagement in such communities is not necessarily voluntary. We conceptualize two different types of misfit: OSS ideology under-fit, whereby an employee embraces an OSS ideology more than their coworkers or OSS community do, and OSS ideology overfit, whereby an employee perceives that their coworkers or OSS community embrace the OSS ideology more strongly than the employee does. To develop a set of hypotheses about the implications of these two types of misfit for employee commitment to the company and commitment to the OSS community, we draw on selfdetermination theory. We test the hypotheses in a field study of 186 employees who participate in an OSS community. We find that OSS ideology under-fit impacts the company and the community in the same way: it decreases employee commitment to the company and commitment to the OSS community. In contrast, we find that OSS ideology over-fit increases commitment to the company but decreases commitment to the OSS community. Finally, we find that employees’ commitment to their company reinforces the impact of their commitment to the OSS community in driving ongoing code contributions. This provides a holistic view of OSS ideology and its impacts among an increasingly pervasive yet understudied type of participant in OSS research. It provides insights for companies that are considering assigning their employees to work in OSS communities as well as for OSS communities that are partnering with these companies. 

Across many domains, end-users need to compose computational elements into novel configurations to perform their day-to-day tasks. End-user composition is a common programming activity performed by such end-users to accomplish this composition task. While there have been many studies on end-user programming, we still need a better understanding of activities involved in end-user composition and environments to support them. In this paper we report a qualitative study of four popular composition environments belonging to diverse application domains, including: Taverna workflow environment for life sciences, Loni Pipeline for brain imaging, SimMan3G for medical simulations and Kepler for scientific simulations. We interview end-users of these environments to explore their experiences while performing common compositions tasks. We use “Content Analysis” technique to analyze these interviews to explore what are the barriers to end-user composition in these domains. Furthermore, our findings show that there are some unique differences in the requirements of naive end-users vs. expert programmers. We believe that not only are these findings useful to improve the quality of end-user composition environments, but they can also help towards development of better end-user composition frameworks. 

Software is increasingly important to the scientific enterprise, and science-funding agencies are increasingly funding software work. Accordingly, many different participants need insight into how to understand the relationship between software, its development, its use, and its scientific impact. In this article, we draw on interviews and participant observation to describe the information needs of domain scientists, software component producers, infrastructure providers, and ecosystem stewards, including science funders. We provide a framework by which to categorize different types of measures and their relationships as they reach around from funding, development, scientific use, and through to scientific impact. We use this framework to organize a presentation of existing measures and techniques, and to identify areas in which techniques are either not widespread, or are entirely missing. We conclude with policy recommendations designed to improve insight into the scientific software ecosystem, make it more understandable, and thereby contribute to the progress of science. 

Application Programming Interfaces (APIs) often define object protocols. Objects with protocols have a finite number of states and in each state a different set of method calls is valid. Many researchers have developed protocol verification tools because protocols are notoriously difficult to follow correctly. However, recent research suggests that a major challenge for API protocol programmers is effectively searching the state space. Verification is an ineffective guide for this kind of search. In this paper we instead propose Plaiddoc, which is like Javadoc except it organizes methods by state instead of by class and it includes explicit state transitions, state-based type specifications, and rich state relationships. We compare Plaiddoc to a Javadoc control in a between-subjects laboratory experiment. We find that Plaiddoc participants complete state search tasks in significantly less time and with significantly fewer errors than Javadoc participants. 

Scientific progress relies crucially on software, yet in practice there are significant challenges to scientific software production and maintenance. We conducted a case study of a bioinformatics software library called Biopython to investigate the promise of Google Summer of Code (GSoC), a program that pays students to work on open-source projects for the summer, for addressing these challenges. We find three positive outcomes of GSoC in the Biopython community: the addition of new features to the Biopython codebase, training, and personal development. We also find, however, that mentors face several challenges related to GSoC project selection and ranking. We believe that because GSoC provides an occasion to extend the software with capabilities that can be used to produce new knowledge, and to train successive generations of potential contributors to the software, it can play a vital role in the sustainability of open-source scientific software.