Feminismo/s

El número 40 (julio 2022) contiene:

• Un dosier monográfico titulado "Black Women's Writing and Arts Today: A Tribute to Toni Morrison" coordinado por Mar Gallego
• Una sección de miscelánea.
• Una sección de reseñas.

• Nº 41 (enero 2023). Rethinking motherhood in the 21st century: New Feminist Approaches (Coords. María Dolores Serrano Niza, Inmaculada Blasco Herranz, Universidad de La Laguna).
  Plazo de recepción de artículos: cerrado

• Nº 42 (julio 2023). Women, data and power– Insights into the platform economy (Coord. Miren Gutiérrez, Universidad de Deusto).
  Plazo de recepción de artículos: hasta el 31/07/2022, a través de la plataforma de la revista.
  
  Intersectional feminism is currently informing new ways of thinking about data theory and practice. Today, data science –an inter-disciplinary field that uses scientific methods and algorithms to extract insights from data — is a form of power. Corporations, organizations, and individuals with the tools, knowledge, and opportunity to exploit the data infrastructure rule the world. Here, the data infrastructure is understood as the software, hardware, and procedures to turn data into value. It has been used to watch individuals (van Dijck 2014), manipulate elections (Pegg and Cadwalladr 2018; Cadwalladr and Graham-Harrison 2018), and discriminate against communities and support ethnic cleansing (Eisenstat 2019; Tolan 2019; Reuters 2018). However, it also is an instrument in social change and justice (Gutierrez 2018; Weizman 2017), and has an extraordinary potential for exposing injustices, improving wellbeing, and resisting authoritarianism. As datafication turns most social aspects into data, casting a feminist gaze on dominant white and male data narratives seems imperative.
  
  As essential parts of the data infrastructure, platforms and their algorithms are at the heart of this debate. A consequence of the process of datafication, the platformization of online content —the upsurge of the platform as the “dominant infrastructural and economic model of the social web” (Helmond 2015, 1)— further transforms the social. On the one hand, inequality traditionally embedded in advertising, film, music videos, and television take on a new life when the platforms make biased algorithmic decisions, potentially multiplying prejudice and establishing a vicious cycle that is not apparent (Gutierrez 2020; Flexer et al. 2018; Gutierrez 2020; Hajian, Bonchi, and Castillo 2016). Biased algorithmic decision-making can have serious social consequences. For example, an algorithm-based system in The Netherlands led to wrongfully accusing thousands of families of fraud by the tax authorities, often based on ethnicity, and the resignation of the government (van den Berg 2021). On the other hand, platforms offer a wealth of data for social change and data activism. For example, open-source intelligence (OSINT) takes advantage of publicly available data on platforms for research. Increasingly, people and organizations use this method to find versions of reality that contradict the official ones. One example is the Forensic Architecture’s investigation of the death of 80-year-old Zineb Redouane in 2018 in Marseille, France. Redouane was struck in the face by a tear gas grenade shot by the riot police as she was standing at the window of her fourth-floor apartment. Using elements of the official report. videos available on sharing platforms, and other data, Forensic Architecture reconstructs the precise sequence of events (Forensic Architecture 2020).
  
  Feminism in data science is a growing space for exploration. Data feminism finds that data systems are not neutral or objective, as they are the products of unequal social relations. Algorithmic decision-making can even exacerbate real-life inequality (Hajian, Bonchi, and Castillo 2016; Langston 2015; Wachter-Boettcher 2017; Zhao et al. 2017). Drawing from intersectional feminist, Catherine D’Ignazio and Lauren Klein illustrate data feminism in action (D’Ignazio and Klein 2019). In D’Ignazio and Klein (2020), these authors also offer seven intersectional feminist principles for equitable and actionable COVID-19 data: examine the power behind algorithmic systems; challenge unfair power structures; elevate emotion and embodiment as sources of knowledge; rethink binaries and hierarchies; embrace pluralism; consider context, and make labor visible (D’Ignazio and Klein 2020). Gutierrez (2020) explores the roles of image and sound from cinema, music videos, social sharing platforms, and advertising in algorithmic gender bias, offering six areas for further research and development: looking at the objectives of data projects first; considering women as users of algorithmic systems; employing more than historical data –which can be biased— to train algorithms; integrating social sciences in designing algorithmic systems; including more women in designing and as testers of algorithmic systems; and incentivating equality through regulation (Gutierrez 2020). Data2x (Data2x 2021) has produced a report specifically focused on how data impact womens’ lives (Vaitla and et al. 2017). Besides, gender algorithmic bias stories are increasingly seen in news media (e.g., Rodriguez Martinez and Gaubert 2020; Eisenstat 2019; Wang 2018; Hao 2019; Eisenstat 2019; Crawford 2013; Knight 2016).
  
  However, this is a relatively new area of inquiry. The data infrastructure’s potential for harm and good makes it critical to continue asking: How is data science operating from the perspective of feminism? How can data science work for equality? Where are the resistances in algorithmic regimes to women’s equality?
  
  This monographic issue invites original, inter-disciplinary articles on the following topics:
  
  • Data feminism and activism
  • Algorithmic gender bias
  • Feminism and data science
  • Counter data
  • Data counter-narratives
  • Data activism
  • Data collection
  • Data post-colonialism
  • Southern approaches to data
  • Digital content and women
  
  The issue will include empirical and theoretical articles that contribute to further understanding resistance to equality, algorithmic gender biases, power relations in the platform economy, dominant and alternative data narratives, and women’s role in algorithmic design.