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The technological landscape continues to evolve through the use of artificial intelligence and machine learning to support automated decision making. These technological advances have led to heightened concerns around ethics and equity, which has led to a push to better understand and support these concerns.in software engineering research and practice. There exists numerous interventions, from frameworks and guidelines to tooling, that aim to support ethical software development; however, it's not clear if and to what extent these considerations and interventions are being applied in practice.To better understand what, if any, ethical considerations or interventions are being applied in practice, we analyzed repositories on GitHub for the amount and relevance of ethical considerations in open source projects. We conducted a semi-automated analysis of repository artifacts for keywords pertaining to ethics, such as fairness, security, and social. We found that when these keywords did appear in discussions, more often than not these words were not used in the context of ethics. Based on a subset of data, keywords such as bias or accuracy are frequently used in technological discussions, while words like security or privacy are used in ethical considerations. In the future, we plan to analyze more data to.further determine the extent of ethical considerations in open source., Journal of Student-Scientists' Research, Vol. 3 (2021)

Both research and practice rely on data for advancement, which elevates concerns around ethics and equity in technological advances and research implications. Previous efforts have focused more on improving methodologies than confronting social systems and processes that lead to inequity in technological outcomes. Furthermore, few have studied how to support people with overlapping social identities through the social science framework of intersectionality in data science.To this end, we propose a toolkit and prototype tool for supporting explicit consideration of intersectionality in data-driven processes. With our prototype workflow support application, Quintessence, data scientists can integrate intersectional reflection into the data science process, from pipeline design to modeling and interpretation, with reflection questions at each stage. We also introduce a Python library, QuinterPy, that calculates and visualizes the representation of overlapping, user-selected categories in a dataset. The library also can calculate the change in the representation of these categories before and after a processing step, e.g., before and after data cleaning. The Python library can be used on its own or as part of the application.In the future, we plan to evaluate the workflow support application and library with practicing data scientists to determine which aspects data scientists consider most useful and how to best incorporate intersectionality into data science practice., Journal of Student-Scientists' Research, Vol. 3 (2021)

Although commercialized and un-marketed research solutions exist in the software community, not all developers integrate research into practice. Existing studies investigate the relationship between developers and their tools in specific domains, yet little work exists on the synergy between research and practice in software-engineering. Prior medical domain research suggests that issues with tool marketability, or access to and awareness of relevant research solutions, hinders research solution adoption and use. We designed a survey to evaluate this hypothesis in the context of software-engineering, and three research questions: How do software teams learn about research solutions? What factors influence a practitioner's decision to integrate research solutions? How can we increase usage of research solutions in software development? We piloted our survey with 4 professionals. From pilot participant feedback, we improved our survey for dissemination. One interesting insight from our pilot was that 2 professionals believed podcasts represent appealing ways to improve research accessibility, which we did not consider during our hypothesis process. Due to these results, we drafted a report to increase awareness of commercialized research solutions. Attempting to provide support for access to new and relevant research solutions, we also designed a browser plug-in called ToolRepo that we plan to implement and improve based on survey findings. Our study illustrates the need for improved developer-research communication, while our tool lessens the divide between research and practice., Journal of Student-Scientists' Research, Vol. 3 (2021)