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The purpose of this paper is to triangulate qualitative and quantitative data with existing data to inform on the function and user experience of a newly created the “News Library,” and, further, to inform on the viability of “bring your own device spaces” (BYOD) in meeting the computing needs of Penn State University Park students through a multi-dimensional study.

This study leverages several methodologies for data collection, including observation, survey, flip chart prompts, interviews and focus groups.

Findings suggest that the News Library accommodates users’ social needs. However, it does not accommodate their communal needs well. The majority of students at the Penn State University Park campus, own laptops and bring them to the library when they intend to study. Personal device usage is preferable to library-provided computers per a familiarity with their personal device, access to personal files and independence of workspace.

As this is a case study, the findings are not generalizable. This study was conducted in one library, on one campus at a 24-campus institution with over 30 libraries.

The mixed-methods study provides multiple views into user behaviors and expectations. The authors propose guidelines for informing the design of BYOD spaces.

The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias.

Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias.

Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users.

This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems.

This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.