%0 Conference Paper %1 prietl2019inequality %A Prietl, Bianca %B Proceedings of the Weizenbaum Conference 2019 "Challenges of Digital Inequality - Digital Education, Digital Work, Digital Life" %C Berlin %D 2019 %K Algorithmus Digitale_Spaltung Digitalisierung Diskurstheorie Erkenntnistheorie Geschlechtsspezifische_Faktoren Intersektionalität Michel_Foucault Soziale_Ungleichheit Technikfolgenabschätzung %R 10.34669/WI.CP/2.11 %T Big Data: Inequality by Design? %U https://www.ssoar.info/ssoar/handle/document/62586 %X This paper proposes to tackle the problem of digital inequality by introducing digital technologies of knowledge generation and decision-making to a feminist critique of rationality that is informed by discourse theory and intersectional perspectives on gender and gendered relations of inequality. Therefore, it takes a closer look at the epistemological foundations of Big Data as one prominent representation of digital technologies. While Big Data and Big Data-based results and decisions are generally believed to be objective and neutral, numeral cases of algorithmic discrimination have lately begged to differ. This paper argues that algorithmic discrimination is neither random nor accidental; on the contrary, it is - amongst others - the result of the epistemological foundation of Big Data - namely: data fundamentalism, post-explanatory anticipatory pragmatics, and anti-political solutionism. As a consequence, a critical engagement with the concepts and premises that become materialized in the design of digital technologies is needed, if they are not to silently (re)produce social inequalities..

@inproceedings{prietl2019inequality, abstract = {This paper proposes to tackle the problem of digital inequality by introducing digital technologies of knowledge generation and decision-making to a feminist critique of rationality that is informed by discourse theory and intersectional perspectives on gender and gendered relations of inequality. Therefore, it takes a closer look at the epistemological foundations of Big Data as one prominent representation of digital technologies. While Big Data and Big Data-based results and decisions are generally believed to be objective and neutral, numeral cases of algorithmic discrimination have lately begged to differ. This paper argues that algorithmic discrimination is neither random nor accidental; on the contrary, it is - amongst others - the result of the epistemological foundation of Big Data - namely: data fundamentalism, post-explanatory anticipatory pragmatics, and anti-political solutionism. As a consequence, a critical engagement with the concepts and premises that become materialized in the design of digital technologies is needed, if they are not to silently (re)produce social inequalities..}, added-at = {2022-07-14T17:19:30.000+0200}, address = {Berlin}, author = {Prietl, Bianca}, biburl = {https://www.bibsonomy.org/bibtex/20c44624d2bb3cc7d493d91e40468ac60/meneteqel}, booktitle = {Proceedings of the Weizenbaum Conference 2019 "Challenges of Digital Inequality - Digital Education, Digital Work, Digital Life"}, copyright = {CC BY 4.0}, doi = {10.34669/WI.CP/2.11}, interhash = {c22f5d8019cbb5f39a19925520c590af}, intrahash = {0c44624d2bb3cc7d493d91e40468ac60}, keywords = {Algorithmus Digitale_Spaltung Digitalisierung Diskurstheorie Erkenntnistheorie Geschlechtsspezifische_Faktoren Intersektionalität Michel_Foucault Soziale_Ungleichheit Technikfolgenabschätzung}, language = {en}, organization = {WI - Weizenbaum Institute for the Networked Society}, timestamp = {2022-07-14T17:19:30.000+0200}, title = {Big Data: Inequality by Design?}, url = {https://www.ssoar.info/ssoar/handle/document/62586}, year = 2019 }