feminist data ethics of care for machine learning: The what, why, who and how

This article conceptualises and provides a roadmap for operationalising a feminist data ethics of care framework for the subfield of artificial intelligence (‘AI’) known as ‘machine learning’. After outlining the principles and praxis that comprise our framework, and then using it to evaluate the current state of mainstream AI ethics content, we argue that this literature tends to be overly abstract and founded on a heteropatriarchal world view. We contend that because most AI ethics content fails to equitably and explicitly assign responsibility to actors in the machine learning economy, there is a risk of implicitly reinforcing the status quo of gender power relations and other substantive inequalities, which in turn contributes to the significant gap between AI ethics principles and applied AI ethics more broadly. We argue that our feminist data ethics of care framework can help to fill this gap by paying particular attention to both the ‘who’ and the ‘how’, as well as by outlining a range of methods, approaches, and best practices that societal actors can use now to make interventions into the machine learning economy. Critically, a feminist data ethics of care is unlikely to be achieved in this context, and beyond, unless all stakeholders, including women, men, non-binary and transgender people, take responsibility for this much needed work.

Quale learning per il XXI secolo?

Il tema dell'apprendimento è ormai al centro dei sistemi educativi. L'interesse per "come" la mente apprende ha sopravanzato il "cosa" apprende. Di qui l'enfasi sulle "competenze" che dovrebbero mettere in condizione il soggetto non solo di contribuire alla coesione sociale e alla propria crescita personale ma anche, come viene ribadito in molti documenti di importanti organizzazioni e decisori internazionali, allo sviluppo economico e alla crescita dell'occupazione nell'ambito di una learning economy. L'interesse che proviene da questi ambienti per una "nuova pedagogia" che fornisca "competenze2 utili alla crescente competizione nell'ambito del capitalismo cognitivo rischia, tuttavia, di enfatizzare solo la dimensione strumentale dell'apprendimento. In questo modo viene meno quella tensione fra soggettività e universalità che l'epoca della Bildung aveva impresso nel concetto di educazione e della quale oggi si sente un rinnovato bisogno. Il contributo intende affrontare queste tematiche proponendo tre "vertici" (creativo, estetico e etico) che intendono fornire un contributo per arricchire il concetto di apprendimento tenendo sullo sfondo proprio quello di universalità.

Yucatec Maya language planning and the struggle of the linguistic standardization process

This study on Yucatec Maya language planning analyzes the linguistic standardization process over a six-year period. The primary research site was the programa, a mandatory Yucatec Maya course for 1,600 Indigenous Education teachers in Mexico. Alongside this acquisition planning effort, other government agencies simultaneously produced an official standard Maya. Programa administrators who oppose official standardization made their own model of Maya in widely distributed government textbooks. Neither model was the main target of programa language teaching; the Maya of classrooms is characterized by vast variation. Although the government promulgated an official standard in 2014, standardization of Maya has not been attained. The difficulties of creating a popular standard by and for Indigenous language speakers are analyzed. Social networks upholding different models of Maya are examined through an economy of language planning framework that views language as social capital and integrates knowledge and learning economy concepts. This research presents the notion of social-linguistic orders to understand how different models of a language coexist and/or compete in a language planning endeavor.