  The ethics of Machine Learning has become an unavoidable topic in the AI
Community. The deployment of machine learning systems in multiple social
contexts has resulted in a closer ethical scrutiny of the design, development,
and application of these systems. The AI/ML community has come to terms with
the imperative to think about the ethical implications of machine learning, not
only as a product but also as a practice (Birhane, 2021; Shen et al. 2021). The
critical question that is troubling many debates is what can constitute an
ethically accountable machine learning system. In this paper we explore
possibilities for ethical evaluation of machine learning methodologies. We
scrutinize techniques, methods and technical practices in machine learning from
a relational ethics perspective, taking into consideration how machine learning
systems are part of the world and how they relate to different forms of agency.
Taking a page from Phil Agre (1997) we use the notion of a critical technical
practice as a means of analysis of machine learning approaches. Our radical
proposal is that supervised learning appears to be the only machine learning
method that is ethically defensible.
