Assessing deep learning: a work program for the humanities in the age of artificial intelligence

Abstract

Abstract Following the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of artificial intelligence (AI) in daily life, influencing the way we act, think, and organize our lives. However, much still remains a mystery when it comes to how these systems achieve such high performance and why they reach the outputs they do. This presents us with an unusual combination: of technical mastery on the one hand, and a striking degree of mystery on the other. This conjunction is not only fascinating, but it also poses considerable risks, which urgently require our attention. Awareness of the need to analyze ethical implications, such as fairness, equality, and sustainability, is growing. However, other dimensions of inquiry receive less attention, including the subtle but pervasive ways in which our dealings with AI shape our way of living and thinking, transforming our culture and human self-understanding. If we want to deploy AI positively in the long term, a broader and more holistic assessment of the technology is vital, involving not only scientific and technical perspectives, but also those from the humanities. To this end, we present outlines of a work program for the humanities that aim to contribute to assessing and guiding the potential, opportunities, and risks of further developing and deploying DL systems. This paper contains a thematic introduction (Sect. 1), an introduction to the workings of DL for non-technical readers (Sect. 2), and a main part, containing the outlines of a work program for the humanities (Sect. 3). Readers familiar with DL might want to ignore 2 and instead directly read 3 after 1.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.