Can Machine Learning be Moral?
Publikation: Konferencebidrag › Paper › Forskning
Standard
Can Machine Learning be Moral? / Sicart, Miguel; Shklovski, Irina; Jones, Mirabelle.
2021. Paper præsenteret ved 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Virtuel.Publikation: Konferencebidrag › Paper › Forskning
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - CONF
T1 - Can Machine Learning be Moral?
AU - Sicart, Miguel
AU - Shklovski, Irina
AU - Jones, Mirabelle
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
M3 - Paper
T2 - 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Y2 - 6 December 2021 through 14 December 2021
ER -
ID: 333623470