Principle: Challenge Power

»Data feminism commits to challenging unequal power structures and working toward justice«

›Data Ethics‹ und ›Data Justice‹

Table 2.1, Seite 60

“In the left-­hand column, we list some of the major concepts that are currently circulating in conversations about the uses of data and algorithms in public (and private) life. These are a step forward, but they do not go far enough. On the right-­hand side, we list adjacent concepts that emerge from a grounding in intersectional feminist activism and critical thought. The gap between these two columns represents a fundamental difference in view of why injustice arises and how it operates in the world. The concepts on the left are based on the assumption that injustice arises as a result of flawed individuals or small groups (“bad apples,” “racist cops,” “brogrammers”) or flawed technical systems (“the algorithm/dataset did it”). Although flawed individuals and flawed systems certainly exist, they are not the root cause of the problems that occur again and again in data and algorithms.” 61

⇒ Kategorien der rechten Spalte beachten die „Matrix of Domination“ aus Kapitel 1 (S.61)
⇒ „[…] the concepts on the left are inadequate on their own to account for the root causes of structural oppression“ (S.61)

→ Diskussion eines Anwendungsbeispiels?!
z.B. den ZEIT-Artikel, mit dem wir ins Seminar eingestiegen sind
Was beinhalten die einzelnen Stichwörter?
Wo und wie werden die Aspekte links in der Tabelle angewendet oder vorgeschlagen?
Diskutieren, ob und wie diese Ansätze (nicht) helfen.

→ Wir haben die Tabelle und die jeweiligen Paare von Datenethik vs. Datengerechtigkeit, die für die Autorinnen die Sicherung der Macht der Herausforderung von Macht gegenüberstellt, diskutiert, verglichen und beschrieben.

Taking Action:
1) Collect (Missing Data/ Countardata)
2) Analyse (Demonstrating inequitable outcomes across groups)
3) Imagine (Co-liberation instead of „fairness“)
4) Teach (Identities of new data scientists ⇒ engage & empower newcomers in data feminism) (S.53)

“Taking action can itself take many forms, and in this chapter we offer four starting points: (1) Collect: Compiling counterdata—in the face of missing data or institutional neglect—offers a powerful starting point…” (S. 53)

Die Autorinnen weisen darauf hin, dass counterdata ein erster Schritt ist, um Machtstrukturen zu bekämpfen. Ich fände es spannend, eine Sammlung von Quellen zu erstellen, die möglicherweise als counterdata genutzt werden können.
Dazu fällt mir das Magazin Katapult ein, die Daten zugänglich aufbereiten und Themen beleuchten, die weniger präsent im medialen Diskurs sind.

• „Who makes maps and who gets mapped?“ (S.52)
• „Who is it, exactly, that needs to be shown the harms of such differentials of power? And what kind of proof do they require to believe that oppression is real?“ (S.57)
• „On whom is the burden of proof is placed?“ (S.58)

Redlining:„a term used to describe how banks rated the risk of granting loans to potential homeowners on the basis of neighborhood demographics (specifically race and ethnicity), rather than individual creditworthines“ (S.50)
Risk assessment algorithms (S.53)
Jim Crow Code: „software code and a false sense of objectivity come together to contain and control the lives of Black people, and of other people of color“ (Ruha Benjamin, S.55)
Racial Capitalism (Cedric Robinson) (S.52)
Ben Green: “Although most people talk about machine learning’s ability to predict the future, what it really does is predict the past.” (S.55)
Restorative Justice (Sasha Costanza-Chock) ⇒ „meaning that decisions should be made in ways that recognize and rectify any harms of the past“ (S.61)⇒ „ that any notion of algorithmic fairness must also acknowledge the systematic nature of the unfairness that has long been perpetrated by certain groups on others“ (S.62)

Equity & Equality: „Equity is justice of a specific flavor, and it is different than equality. Equality is measured from a starting point in the present: t = 0, where t equals time and 0 indicates that no time has elapsed since now. Based on this formula, the principle of equality would hold that resources and/or punishments should be doled out according to what is happening in the present moment the time when t = 0. But this formula for equal treatment means that those who are ahead in the present can go further, achieve more, and stay on top, whereas those who start out behind can never catch up“ (S.62)
New Racism (Robin DiAngelo): „the belief that racism is due to individual bad actors, rather than structures or systems“ (S.63)
Co-Liberation „The key to co-liberation is that it requires a commitment to and a belief in mutual benefit, from members of both dominant groups and minoritized groups; that’s the co in the term“ (S.63)

Die Autorinnen schreiben:
“we must look to understand and design systems that address the source of the bias: structural oppression. In truth, oppression is itself an outcome, one that results from the matrix of domination. In this model, majoritized bodies are granted undeserved advantages and minoritized bodies must survive undeserved hardships.” 63
und
“Starting from the assumption that oppression is the problem, not bias, leads to fundamentally different decisions about what to work on, who to work with, and when to stand up and say that a problem cannot and should not be solved by data and technology.” 63

→ vergleichen, konkretisieren, klären: Was ist gemeint, wie ist zu unterscheiden, wie plausibel ist, was sie schreiben zu
Vorurteile / Verzerrung – strukturelle Unterdrückung – Matrix der Vorherrschaft / Unterdrückung

“Proof can just as easily become part of an endless loop if not accompanied by other tools of community engagement, political organizing, and protest. Any data-based evidence can be minimized because it is not “big” enough, not “clean” enough, or not “newsworthy” enough to justify a meaningful response from institutions that have a vested interest in maintaining the status quo.” (S. 58)

Die Autorinnen beschreiben hier die Limitierungen von data science. Ausgehend davon fände ich es interessant noch einmal genauer zu beleuchten, wo die Grenzen von Daten als emanzipatorisches Werkzeug liegen, beziehungsweise welche Instrumente und Vorgehensweisen es noch braucht, um nicht in den loop zu geraten, den die Autorinnen ansprechen.

Die Autorinnen gehen, wie auch schon in den vorherigen Kapiteln, davon aus, dass sich Personen mit weniger Privilegien der bestehenden Ungerechtigkeiten bewusst sind(S.57).
Frage: Gehen die Autor*innen davon aus, dass dies immer so ist?

Welche „Vorkehrungen“ oder Strategien könnte es geben, um deficit narratives (S. 58) zu vermeiden? Auch vor dem Hintergrund, dass Tragweite und Ausmaß von Gewalt gegen minorisierte Gruppen – auch aufgrund mangelnder Datenlage und Dokumentation – häufig sogar noch unterschätzt werden?