Data, Knowledge, Pedagogy: The Age of Machine Learning (short-term project Dec. 7-8, 2018)
Organized by Jeffrey Ahlman, History; Bozena Wellborne, Government
December 7-8, 2018
The advent of the Digital Age (or the “Information Age”) has thrown scholars across a variety of academic disciplines—especially those relying on textual and experiential evidence—into an existential crisis. Not only has this new era seemingly embraced everything quantitative and rooted in scientific materialism; it has also resulted in a rapid change in how societies define, gather, spread, and ultimately interpret information. Furthermore, the shift from analog to digital modalities has implications for the permanence and immutability of the information or “data” generated by scholars and laypeople alike. Once something is created within a digital platform or uploaded into a digital medium, it is infinitely malleable. While that offers up countless possibilities, it also raises the specter of “fake” information with godlike verisimilitude, which further begs what is then knowable.
Yet, the emergence of “data analytics” as a byproduct of advances in machine learning has also returned some of the value to text by essentially quantifying it and facilitating the speedy analysis of vast reams of textual evidence. In fact, with the spread and application of machine-learning algorithms across multiple disciplines—from biostatistics and finance to linguistic anthropology—many scholars may find that all you need is “data.” Theory is, then, often seen as optional, with the algorithm gaining the responsibility of “cutting through the noise.” The traditional categories of data, information, knowledge and wisdom sometimes appear to blur.
This short-term Kahn seeks to engage scholars across all three divisions in an exploration of what this growing emphasis on machine learning means for scholarship, pedagogy, and knowledge production more broadly. Key questions we intend to ask include:
- What is data in the Age of Machine Learning and what is our relationship to it?
- How has the rise of Big Data reshaped the types of questions we ask in our work?
- How might the turn to Big Data reinforce inequalities in our teaching and research or even create new forms of inequities in our work?
- How might Big Data force us to rethink key ethical concerns driving our teaching and research?
We have invited Heather Roff, senior research analyst at the National Security Analysis Department at the Johns Hopkins Applied Physics Laboratory, to present publicly and participate in this project. She will speak on “Artificial Epistemiology: Knowledge, Truth, and Value in Machine Learning” on December 7, and will join our group conversation on December 8.