How to make creatively accepted AI in the cultural industry?

How can AI be creatively accepted within the European cultural industry? This presentation draws on empirical data from workshops with culture creators across Europe, testing various machine learning approaches and visualizations. It aims to contribute to the debate on socially responsible development of AI. Much of the literature focuses on bias (Cheng et al., 2021) and privacy (Stahl & Wright, 2018), but there are also those who argue that accountability is primarily about making the technology meaningful to the people who are affected by it. Floridi et al. (2018) thus point out that the potentials of AI turn into risks when the algorithms no longer support people's room for action and competences, but instead remove self-determination and control from those who should make the decisions. How should AI then be developed if it is to make sense for a group of professionals who require room for manoeuvre and control over their own creative process.

The presentations take a socio-technical perspective (Akrich, 1992; Callon, 2004) on the development of socially responsible AI and uses Participatory Data Design (Jensen et al., 2021) to involve the content producers of the cultural industry in decisions about datafication, training and parameterization of machine learning. The technology thus becomes an elicitation device in an anthropological fieldwork that must provide concrete knowledge about certain groups of people's expectations of AI as a usable and acceptable part of their work practice (Pedersen, 2021; Munk et al., 2022; Albris et al., 2021).