In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism (...) is presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age. AI ageism can be defined as practices and ideologies operating within the field of AI, which exclude, discriminate, or neglect the interests, experiences, and needs of older population and can be manifested in five interconnected forms: (1) age biases in algorithms and datasets (technical level), (2) age stereotypes, prejudices and ideologies of actors in AI (individual level), (3) invisibility of old age in discourses on AI (discourse level), (4) discriminatory effects of use of AI technology on different age groups (group level), (5) exclusion as users of AI technology, services and products (user level). Additionally, the paper provides empirical illustrations of the way ageism operates in these five forms. (shrink)

In this paper I argue that the value attributed to coral reefs drives the characterisation of evidence for their regeneration or degradation. I observe that regeneration and degradation depend on an understanding of what an ecosystem looks like when undegraded (a baseline), and that many mutually exclusive baselines can be given for any single case. Consequently, facts about ecological processes are insufficient to usefully and non-arbitrarily characterise changes to ecosystems. By examining how baselines and the value of reefs interact in (...) coral and algal reef examples, I argue that considering the value of an ecosystem is a necessity when describing processes like regeneration and degradation. This connects value as studied in socio-ecological and economic research with values as discussed in the philosophy of science literature. It also explains why such a broad range of processes may be considered regenerative, including those which introduce significant novelty, as well as pointing towards ways to mediate related debates, such as those surrounding novel and ‘pristine’ ecosystems. (shrink)