Real Big Data: How We Know Who We Know in Youth Work

Abstract

As the generation and use of big data becomes more prevalent in youth work, young people grow up in a world that "knows" more about their lives than ever before. Beyond school attendance and grades, these systems know about out-of-school program participation, social service resources, therapeutic interventions, and more. Though data historically was used to understand and improve program achievements, communicate with funders, and track participants, it is increasingly used to suggest and even perform interventions in young peoples' lives. Young people are rarely asked how they feel about these systems. This study, presented as a counter-narrative from their perspective, differentiates the big data collected and analyzed about them from the "big data" -or stuff that they feel really matters about who they are and the challenges they face. It concludes by offering four questions to help youth-serving organizations consider the ways they generate and use data, in light of the many issues young people raise about new big data trends.

Surfacing Human Service Organizations’ Data Use Practices: Toward a Critical Performance Measurement Framework

“…However, it also integrates perspectives from critical (data) studies, which are concerned with the biases and prejudices of data collection, the use of data for surveillance, the ways data processes disempower clients and front-line workers, the lack of consent in most data work (Wilbanks & Friend, 2016) and the deficit focus of most data (Yosso, 2005;Tuck, 2009). It addresses concerns about the datafication of young people by involving them in the process and focusing on data that exists within the context of interpersonal relationships with front-line workers (van Dijck, 2014;Fink & Brito, 2020). It potentially upends traditional models of data collection in YSOs, which involve tracking young people (Eubanks, 2017), replacing it with environmental-level or social analysis that study the worlds young people struggle through (Couldry & Powell, 2014).…”

“…Further, they recognize the ways that presented data may cause us to interact differently with a client, forcing us to see and respond to this client in particular ways. They recognize that "performance measurement" is not only a strategy for accountability, but significantly alters the everyday practice of working with young people, in ways that may be harmful and helpful (Fink, 2018;Fink & Brito, 2020;Gillborn, Warmington, & Demack, 2018). This includes the ways a focus on labels like "at-risk youth" distract from environmental, community-level, and systemic problems, like gentrification, racism, and socioeconomic differences (Cahill, 2006;Yosso, 2005).…”

Decentralised content moderation

“…These often intersect with regulatory requirements, including the General Data Protection Regulation (GDPR) in the European Union, which governs data generally, and Health Insurance Portability and Accountability Act (HIPAA) in the United States, which manages health care data. Developing systems that allow members more granular control over data sharing at any time increases member consent (Fink & Brito, 2021), while simultaneously decreasing queriers barriers to combining data across multiple institutions, which otherwise might require separate consent agreements across each institution (Hafen et al, 2014). Queriers algorithms must be vetted to ensure member safety and consent, as well as to prevent bias in algorithms .…”

Emerging tensions in data work: Staff and youth perspectives in youth-serving organizations

“…Elsewhere(Fink and Brito 2021), we have written about contrasting analytic data with relational data. The former are decontextualized, top down, from an organizational perspective.…”