Many of the papers and more-than-textual proposals submitted for this special issue included machine vision technologies and other data- and AI- mediated practices. To provide a critical per­spective on data-driven (design) research, we decided to explore the emerging field of data fem­inism through online interviews with three scholars and practitioners who apply intersectional feminist theory and practice to the realm of data-driven work: Catherine D’Ignazio, Lauren Klein, and Maya Livio. With Catherine D’Ignazio and Lauren Klein, authors of the book Data Feminism (2020), we touch upon the idea of data feminism as a way of thinking about (and acting upon) data and data science, informed by intersectional feminist thinking. From examining and challenging power structures in the data collection process to embracing pluralism beyond binaries and hierarchies, they outline a research program that clarifies why and how data science needs intersectional feminism. With them, we discuss how art and (speculative) design practices can make power imbalances visible. We also discuss the limitations and advantages of participatory data practices and the responsibil­ity that lies upon data collectors when making visible an issue through data can cause more harm than good to those affected by it. We discuss how sometimes one needs to reject ground rules of data visualization to pursue higher political goals beyond simple analytical needs. We conclude this conversation with an invitation to embrace complexity when applying feminist principles to data work, while being aware of our personal standpoints and limitations. With Maya Livio, researcher and curator at the University of Colorado Boulder, we discuss how an intersectional feminist approach to data science can also consider more-than-human beings. We talk about her work on animal interfaces, in which she explores how the contact points between the human and more-than-human worlds are permeated with technology. Maya Livio then takes us through her experiences in feminist labs, explaining how the first step of incorporating a fem­inist practice is to take stock of and codify the work being done, cultivating attention towards (of­ten unspoken or unwritten) methods and practices. We also discuss how she and her colleagues developed a framework for operationalizing the art of noticing as a methodological contribution. Finally, we touch upon her personal research approach, characterized by a mix of experimental multidisciplinary practices, moving from writing to curating to design and art-making.

Feminist Data Practices: Conversations with Catherine D’Ignazio, Lauren Klein, and Maya Livio

Gabriele Colombo
2021-01-01

Abstract

Many of the papers and more-than-textual proposals submitted for this special issue included machine vision technologies and other data- and AI- mediated practices. To provide a critical per­spective on data-driven (design) research, we decided to explore the emerging field of data fem­inism through online interviews with three scholars and practitioners who apply intersectional feminist theory and practice to the realm of data-driven work: Catherine D’Ignazio, Lauren Klein, and Maya Livio. With Catherine D’Ignazio and Lauren Klein, authors of the book Data Feminism (2020), we touch upon the idea of data feminism as a way of thinking about (and acting upon) data and data science, informed by intersectional feminist thinking. From examining and challenging power structures in the data collection process to embracing pluralism beyond binaries and hierarchies, they outline a research program that clarifies why and how data science needs intersectional feminism. With them, we discuss how art and (speculative) design practices can make power imbalances visible. We also discuss the limitations and advantages of participatory data practices and the responsibil­ity that lies upon data collectors when making visible an issue through data can cause more harm than good to those affected by it. We discuss how sometimes one needs to reject ground rules of data visualization to pursue higher political goals beyond simple analytical needs. We conclude this conversation with an invitation to embrace complexity when applying feminist principles to data work, while being aware of our personal standpoints and limitations. With Maya Livio, researcher and curator at the University of Colorado Boulder, we discuss how an intersectional feminist approach to data science can also consider more-than-human beings. We talk about her work on animal interfaces, in which she explores how the contact points between the human and more-than-human worlds are permeated with technology. Maya Livio then takes us through her experiences in feminist labs, explaining how the first step of incorporating a fem­inist practice is to take stock of and codify the work being done, cultivating attention towards (of­ten unspoken or unwritten) methods and practices. We also discuss how she and her colleagues developed a framework for operationalizing the art of noticing as a methodological contribution. Finally, we touch upon her personal research approach, characterized by a mix of experimental multidisciplinary practices, moving from writing to curating to design and art-making.
2021
Muchos de los artículos y las propuestas más-que-textuales que se presentaron para este número especial incluían tecnologías de visión artificial y otras prácticas mediadas por datos e inteligencia artificial (IA). Con el propósito de ofrecer una perspectiva crítica sobre la investigación (de diseño) basada en datos, decidimos explorar el campo emergente del feminismo de datos a través de en­trevistas en línea con tres académicas y profesionales que aplican la teoría y la práctica feminista interseccional al trabajo basado en datos: Catherine DʼIgnazio, Lauren Klein y Maya Livio. Con Catherine DʼIgnazio y Lauren Klein, autoras del libro Data Feminism (2020), abordamos la idea del feminismo de datos como una manera de pensar (y actuar) sobre los datos y la ciencia de datos, la que se caracteriza por estar informada por el pensamiento feminista interseccional. Desde la necesidad de examinar y desafiar las estructuras de poder en el proceso de recopilación de da­tos hasta la necesidad de abrazar el pluralismo más allá del pensamiento binario y las jerarquías, DʼIgnazio y Klein esbozan un programa de investigación que aclara por qué y cómo la ciencia de datos necesita el feminismo interseccional. Con ellas discutimos cómo el arte y las prácticas de diseño (especulativo) pueden hacer visibles los desequilibrios de poder. También discutimos las limitaciones y ventajas de las prácticas participativas de datos y la responsabilidad que recae sobre quienes recolectan datos cuando usar datos para hacer visible un tema puede causar más daño que beneficios a los afectados. Discutimos cómo, a veces, es necesario rechazar las reglas básicas de la visualización de datos para alcanzar objetivos políticos más elevados que las simples necesidades analíticas. Concluimos esta conversación con una invitación a abrazar la complejidad al momento de aplicar los principios feministas al trabajo con datos, siendo conscientes de nuestros puntos de vista y limitaciones personales. Con Maya Livio, investigadora y curadora de la Universidad de Colorado Boulder, hablamos de la manera en que un enfoque feminista interseccional de la ciencia de datos puede tener en cuenta también a los seres más-que-humanos. Conversamos sobre su trabajo con interfaces animales, en el que explora cómo los puntos de contacto entre los mundos humano y más-que-humano están im­pregnados de tecnología. A continuación, Maya Livio nos lleva a sus experiencias en los labora­torios feministas, para explicarnos que el primer paso para incorporar una práctica feminista es hacer un balance o inventario y codificar el trabajo que se está realizando, cultivando asimismo la atención hacia los métodos y las prácticas (a menudo tácitos o no escritos). También discutimos cómo ella y sus colegas desarrollaron un marco para operacionalizar el “arte de notar” como una contribución metodológica. Por último, nos referimos a su enfoque personal de investigación, ca­racterizado por una mezcla de prácticas multidisciplinares experimentales, que van desde la es­critura hasta la curatoría, pasando por el diseño y la creación artística.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1191480
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