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Critical data studies
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<p>Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with <a href="/facts/Big_data/XGtrVg4z">big data</a>. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of <a href="/facts/Critical_theory/2R9f1DNe">critical theory</a>, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. </p><p>Interest in this unique field of critical data studies began in 2011 with scholars <a href="/facts/Danah_boyd/Iwq3cjhE">danah boyd</a> and <a href="/facts/Kate_Crawford/GrUD5uaD">Kate Crawford</a> posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and <a href="/facts/Phoebe_Sengers/L2eX70XJ">Phoebe Sengers</a> focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. </p><p>Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. </p>