Dear colleagues,
I am working on the following special issue along with colleagues in human resources management, community psychology, and digital humanities for the International Journal on Human Resources Management (ranked A in the ABDC list). I would love to see some contributions from the INFORMS community to it.
CFP: Interdisciplinary Narratives on Artificial Intelligence & Personnel Selection Systems
Guest Editors
Neil Boyd, Melissa Intindola, Thiago Serra, John Hunter
Bucknell University
Summary
The purpose of the special issue is to stimulate interdisciplinary narratives and analyses on artificial intelligence (AI) and personnel selection systems. We seek papers that:
1) Place a greater emphasis on narratives and analyses from the humanities and social sciences that can help us understand this phenomenon through such lenses as philosophy, history, gender studies, intersectionality, social identity studies, race studies, psychology, sociology, and others.
2) Derive from perspectives in algorithmic fairness and transparency, business analytics, data privacy, and machine learning techniques that can help us interpret what is in the AI "Black Box," what limitations AI brings with it, and how organizations can best adhere to good hiring practices.
3) Span the aforementioned disciplines and intersect with academic and practice issues in personnel selection which can help us understand theoretical, functional, and practice implications in ways that we have yet to see in the literature.
Keywords:
Artificial Intelligence, Personnel Selection, Hiring, Interdisciplinary
Key themes/scope of focus:
The purpose of the proposed special issue topic is to stimulate interdisciplinary narratives in the intellectual domain of artificial intelligence (AI) and personnel selection systems (which appears to be a ubiquitous topic of interest across the globe). Most of the writing to date on AI and HR is centered on broad macro-oriented approaches to the use of AI in HR versus nuanced knowledge of algorithmic implications in specific functional areas of HR. Further, given that personnel selection systems are critical for such elements like bringing talent and diversity into organizations, and for signaling equitable and fair processes in the organization to prospective employees, it is interesting that we have such a large research gap in this area of HR scholarship.
Moreover, there are significant issues and changes to the regulatory environment that are largely ignored in this recent work. There is a robust literature on how AI-based systems, such as facial recognition systems, have replicated racial and gendered forms of bias and more work is needed to explore these risks in HR uses of AI. Responding to such concerns, the State of Illinois passed the Artificial Intelligence Video Interview Act (2020) requiring employers to notify candidates that AI is being used in their evaluation; New York City Council passed local law 1894-A requiring independent bias audits of all automated employment decision tools (2021); the
California assembly is presently debating the Workplace Technology Accountability Act (AB 1651) which would place limits on worker monitoring and other forms of surveillance. The European Union is debating an Artificial Intelligence Act which would place several common HR uses of AI on a list of "high-risk practices" and has further proposed the Artificial Intelligence Liability Directive to establish rules for fault-based civil claims against employers using AI systems.
In response to recent changes in the practice landscape, and the need for a greater academic understanding, our special issue aims to engage with the political, social, computational, and philosophical issues that are driving this intense external scrutiny of AI in HR. We also find that most of our current knowledge has been constructed by academics within the field of HR, which is fully understandable; since the academic and regulatory scrutiny these practices comes from outside the HR domain, however, we believe that the HR literature would significantly benefit from a greater presence of interdisciplinary knowledge on the use of AI in personnel selection, and the broader implications of such uses on the future of work itself.
Papers for this special issue should conform to one or more of the following topical areas:
Place a greater emphasis on narratives and analyses from the humanities and social sciences that can help us understand this phenomenon through such lenses as philosophy, history, gender studies, intersectionality, social identity studies, race studies, psychology, sociology, and others. A focus here would help HR scholars and practitioners see this phenomenon from new perspectives. We envision that past or current scholarship in these fields can inform us in new and unique ways that could facilitate the debate in the AI – Personnel Selection space.
Possible questions: What disciplinary lenses may help us better understand the human fascination with and deference to AI in the selection process? What do the well-documented bias problems with other AI systems mean for AI in HR? How do policymakers hold organizations accountable for their use of AI in the selection process, particularly given the proprietary nature of many machine learning algorithms? What do such assumptions regarding AI in selection mean for the future of work writ large - in a society where we willingly allow technology interface between human beings and potential employment opportunities, what are the consequences? Future directions?
Derive from perspectives in algorithmic fairness and transparency, business analytics, data privacy, and machine learning that can help us interpret what is in the AI "Black Box," how algorithmic technology specifically works, and how it can best adhere to good hiring practices.
Possible questions: What exactly is in the AI "Black Box?" What machine learning models are being used in algorithmic hiring systems, what do these various models do, and what are their implications for efficient and effective automation in personnel selection? How can we ensure algorithmic fairness in cases of proprietary algorithms? What questions should companies be asking the proprietors of AI platforms sold for selection purposes? What is the ethical responsibility of technical creators of selection algorithms to disclose potential biases?
Span the aforementioned disciplines and intersect with academic and practice issues in personnel selection to help us understand theoretical, functional, and practice implications in ways that we have yet to see in the literature.
Possible questions: What are we missing in neglecting an interdisciplinary approach to the use of AI in selection? How do we encourage HR scholars to work across disciplines to better understand AI's use in selection and its potential consequences? How can we advance HR practice?
We welcome submissions that are quantitative, qualitative, and/or conceptual in nature. We envision a special issue that includes a variety of methodological approaches.
Interested individuals may be selected for a pre-development workshop (PDW) based on a short proposal submission ahead of the formal submission deadline. Authors are encouraged to submit short proposals (500-750 words, excluding references) to Melissa Intindola (m.intindola@bucknell.edu) by September 30, 2023 to be considered for the PDW. We anticipate notifying selected PDW participations on or before October 15, 2023, with a workshop date in early November. However, participation in the pre-development workshop is not required to submit to the special issue, nor is participation in the pre-development workshop a guarantee
of acceptance for the special issue.
Submission Process
Authors can submit their full papers by April 30, 2024 to IJHRM for review. Papers should be prepared and submitted according to the journal's guidelines:
https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=rijh20 . All papers will be subject to the same double-blind peer review process as in regular issues of IJHRM. When submitting your paper, please designate it as a "Special Issue" article type, by selecting "yes" to the special issue question, select which SI the submission is for, and also to state the name of the intended SI in their cover letter.
Timeline
Full Papers Due from Contributors April 30, 2024
First Round of Blind Review and Editorial Decisions End of June 2024
Revised Manuscripts Back from Contributors September2024
Second Round of Blind Review and Editorial Decisions October/November 2024
Additional Round of Review (as Needed) February/March 2025
Final Recommendations for Publication April/May 2025
Publication of Special Issue Mid-202
If you have questions about a potential submission, please contact: Dr. Melissa Intindola at m.intindola@bucknell.edu
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Thiago Serra
Assistant Professor of Business Analytics, Bucknell University
INFORMS Computing Society Vice Chair / Chair-Elect
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