Paper accepted to Conference of the Canadian Society for the Study of Education (CSSE), in coauthorship with Tanmay Deshpande. Due to the Covid 19 pandemic, the event itself was cancelled. The abstract of this paper is presented here:
During this talk, the presenters will offer a case study about the conditions for, and outcomes of building a bridge between technical knowledge and social awareness within a data-driven company in the private sector through the creation and implementation of a carefully curated linguistic corpus. By describing the interdisciplinary and collaborative process behind the design and implementation of a search and recommendation algorithm based on linguistic input, this presentation will offer insight into the nature of Humanities and Social Science research in general (and literacy education in specific) outside of academia and its potential place in data-driven organizations, while demystifying the nature of algorithms. Ideally, this presentation will also shed some light on potentially transferable skills between Ph.D. studies and the private sector and will bring some degree of confidence to upcoming scholars in their ability to integrate into such environments.
During the last decade, the Humanities and Social Sciences (HSS) have been openly declared either dead or broken in popular and specialized media (e.g., David Hanson, 2014; McIntyre, 2019; Stover, 2017; Winterhalter, 2014), and the rise of big-data based economies has been often presented as the culprit (e.g., Bernstein, 2016). These kinds of statements are not necessarily new. Historically, prominent technological developments have come with a perceived price in desirable human traits (e.g., the walkman vs. interpersonal communication skills, smartphones vs. attention spans, videogames vs. playing outside, texting vs. “proper” grammar, etc.). From this dichotomic, techno-deterministic point of view, the overwhelming popularity of STEM-related professions in the private sector (Edge & Munro, 2015) is an unequivocal sign of the demise –and sometimes even irrelevance– of the HSS (e.g., Bernstein, 2016; Stover, 2017). Alternatively, humanists and social scientists are overwhelmingly predisposed to remain outside of such economies by striving towards occupying academic positions, so much so than failing to secure a professorship is commonly seen as a personal and professional defeat (Cassuto, n.d.; Oakley, 2019; Wood, n.d.). In essence, most practitioners and researchers within these disciplines see benefits that others perceive not only as a loss but a sign of the imminent death of such disciplines.
This is not just a difference of opinion between the private sector and academia about the reputation of the Humanities and Social Sciences. This situation hurts both the users of the data-driven companies which success has been interpreted as a sign of the demise of the HSS, and the humanists and social scientists that seem uninterested in the casual death pronouncements of our own disciplines or in pursuing careers in the private sector. On the one hand, the lack of involvement of humanists and social scientists in the production of algorithms intended for decision-making that greatly affects human lives has led to serious consequences. These are precisely the kinds of algorithms that O’Neil has referred to as “weapons of math destruction” (O’Neil, 2016). Technology ethicist and scholar Shannon Vallor (2016) has expressed this problem in very clear terms:
We can’t have designers who are simply resting on the knowledge they have as computer scientists or engineers, we need technologists who also have an understanding of history, of human social dynamics, of ethics and politics, because those are the only forms of knowledge that would help them make a distinction between the kinds of bias or the kinds of outputs that are unethical and we don’t want, and the kinds that are useful and we do want.” (McRaney, n.d.)
But right now we are not training technical experts to have that kind of understanding and we are not training students in the Humanities, who have that kind of understanding, to become interested in technical work. So that’s a big problem and that bridge has to be built before we are going to get a lot further.” (McRaney, n.d.).
On the other hand, recent studies show that less than 1 in every 3 Ph.D.s in HSS are employed as full-time professors (Edge & Munro, 2015), making non-academic jobs the norm, not the exception. Despite these grim prospects, most Ph.D.s in these disciplines would not consider a position in the private sector, potentially working for the companies that currently require their expertise the most. Scholars have suggested that academic institutions are at least partially at fault for this, by failing in preparing students for a career path different from academia (e.g., Boklashauk, 2018; Wood, 2019), implicitly fostering uncertainty in any other scenario.
Interestingly, scholars specialized in language and literacy education could be particularly well-positioned not just to tackle the issues of machine bias extensively denounced in media, but to do so in the way Dr. Vallor suggests, as many of the data that would have to be revised for machine learning purposes is 1. still in linguistic both in nature and format and 2. requires the kind of commitment with social justice that educators tend to abide to. To illustrate this point, in this talk the presenters will describe the role that linguistic corpora management wrapped in a socially responsible mandate (embodied in a team led by a language and literacy education scholar) played in the creation of an ethical algorithm. By describing the interdisciplinary and collaborative process behind the design and implementation of a search and recommendation algorithm based on linguistic input, this presentation will offer insight into the nature of Humanities and Social Science research in general (and literacy education in specific) outside of academia and its potential place in data-driven organizations, while demystifying the nature of algorithms. Ideally, this presentation will also shed some light on potentially transferable skills between Ph.D. studies and the private sector and will bring some degree of confidence to upcoming scholars in their ability to integrate into such environments.
Bernstein, M. (2016, January 21). Major trends: Math is hot; decline for social sciences. Retrieved August 26, 2019, from Princeton Alumni Weekly website: https://paw.princeton.edu/article/major-trends-math-hot-decline-social-sciences
Boklashauk, S. (2018, May 24). PhD students should prepare for academic and non-academic careers, U of S professor writes in new book. Retrieved August 29, 2019, from https://artsandscience.usask.ca/news/articles/2206/PhD_students_should_prepare_for_academic_and_non_academic_ca
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Jaschik, S. (2016, April 4). New data show tightening Ph.D. job market across disciplines. Retrieved August 26, 2019, from https://www.insidehighered.com/news/2016/04/04/new-data-show-tightening-phd-job-market-across-disciplines
McIntyre, L. (2019, June 13). To Fix the Social Sciences, Look to the “Dark Ages” of Medicine. Retrieved August 26, 2019, from The MIT Press Reader website: https://thereader.mitpress.mit.edu/social-sciences-dark-ages/
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