Month: September 2018

Intro to GIS and mapping with Scholars’ Lab

University of Virginia’s Scholars’ Lab is making intro classes to mapping available on their site:

The Scholars’ Lab GIS workshop sessions are designed to be accessible without prior knowledge or experience with GIS software and to give attendees hands-on experience with step-by-step instructions. Workshop topics are based on the how-to questions Kelly and Chris answer daily in their interactions with faculty and students in the Scholars’ Lab, in addition to emerging themes in geospatial scholarship.

“Feminist Modernist Studies” Special Issue on Digital Humanities

The 3rd issue of the new Feminist Modernist Studies journal focuses on the issue of feminist scholarship with and within DH practices:

In our CFP we asked, what might feminism offer DH? Across this cluster, essays agreed that feminist DH is not just “about women,” but entails a collaborative feminist practice of breaking down boundaries, enabling new syntheses based on situated knowledges, shifting subject positions and interpretations. Until recently, DH has been prominently associated with scientific neutrality, “big data,” quantification and the ensuing practices of distant reading or macroanalysis. However, as feminist theorists (and many modernist writers) have long observed, purportedly “objective” knowledge systems can and do inscribe exclusionary, hierarchical assumptions.

Question that Algorithm

The Data&Society Research Institute just published its primer on “Algorithmic Accountability,” originally presented to the Congressional Progressive Caucus on April 18, 2018 as “Tech Algorithm Briefing: How Algorithms Perpetuate Racial Bias and Inequality.” Here’s a synopsis of its contents:

Algorithmic Accountability: A Primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.

Currently, there are few consumer or civil rights protections that limit the types of data used to build data profiles or that require the auditing of algorithmic decision-making, even though algorithmic systems can make decisions on the basis of protected attributes like race, income,or gender–even when those attributes are not referenced explicitly–because there are many effective proxies for the same information.

This brief explores the trade-offs between and debates about algorithms and accountability across several key ethical dimensions, including:

  • Fairness and bias;
  • Opacity and transparency;
  • The repurposing of data and algorithms;
  • Lack of standards for auditing;
  • Power and control; and
  • Trust and expertise.