Equal Fun for Everyone? the Influence of Artifical Intelligence on the Unequal Distribution of Leisure.

Social progress - in the area of media dissemination, access to education or technical advancements - does not unfold its beneficial effects for all members of society at the same time. There are differences in the speed of adaptation of new technical artifacts or informations (Rogers 2003), while at the same time capital resources are also decisive for self-empowerment and the improvement of one's own living conditions (Brumme 2022). This is well known for the exploitation of information advantages through changes in the media landscape (Tichenor/Donohue/Olien 1970) and also for the spread of modern digital technologies (Zillien 2006).

It is hardly disputed that the potential for improvements (technical or social) always puts members of privileged classes in advantageous positions and thus leads to an increase in social inequality rather than a reduction. Nevertheless, the emergence of each new generation of technology heralds the overcoming of this correlation. This also applies to the current discussion about the impact of AI (Gates 2023).

This article will take a closer look at the inequality-relevant consequences of the assumed potential of AI in the area of leisure activities, propose a systematization of the inequality and develop a possible reason for the recurring misinterpretation of the inequality-inducing effects of technology. First, along the dimensions of a) diffusion, b) capital, c) information, the expected usage disadvantages for marginalized groups will be made plausible and accessible for the adaptation of future AI applications. This is based on the assumption that access, capital and data bring about usage advantages and thus an improvement in quality of life. Finally, the impact of AI systems in the leisure sector is shown along the distinction of expected improvement in absolute living standards with a simultaneous increase in relative inequalities, whereby the blurring of these developments in common AI narratives is illustrated.