Datafied Ageism at Work: How AI-Based Performance Visibility Shapes Dignity and Late-Career Sustainability

Abstract

More and more organisations are turning work into data. Pace and location are monitored at the warehouse, talk time, adherence and resolution are measured at the call centre, activity and task switching are measured at productivity systems and platforms continuously rate workers to rank them. These systems expand managerial visibility and translate work into durable records that can shape scheduling, pay, training, discipline, redeployment, and exit (Ball, 2010; Kellogg et al., 2020; Ravid et al., 2020). It comes as part of the population ageing and the policies favouring longer working lives. But while chronological age is not a reliable predictor of overall job performance, workplace age stereotypes are still very present (Posthuma & Campion, 2009). Algorithmic systems may reduce arbitrary human judgment, but they can also institutionalize narrow or historically biased definitions of competent work. Validity, however, doesn't mean consistency. Just because there is an age difference in a performance score, it does not mean that this is evidence of ageism. Variances may be reflective of assignments, tenure, health, training, occupational knowledge or other strategies to effect quality and safety. However, if both age groups have an equal average score, but the pattern of scores from each to prediction differs or the consequences are different, then it is not fair. It is necessary, therefore, to differentiate descriptive disparity, measurement validity, causal system effects and downstream decisions in the analysis. We describe datafied ageism as the creation of age-stratified disadvantage as a result of the lack of age-inclusive evaluative validity of algorithmic performance systems. Its mechanisms include construct underrepresentation, age-proxy sensitivity, non-representative benchmarking, persistent scoring, and consequential decisions with limited contestability. These processes can contribute to a loss of dignity and to the loss of capability--the failure to convert experience, health, judgment, and effort into something the organization values and rewards. Contributions the article makes to the research include separating monitoring intensity from design that is age-invalid, combining dignity, capability, stereotype-embodiment, and life-course perspectives, conducting a transparent longitudinal simulation with staggered adoption, validity analysis, mediation, competing exits, attrition adjustment, and clustered robustness tests. It suggests that diagnosis of the datafied ageism can only be made when the disparate experiences documented through convergent evidence, predictive validity, system architecture, consequences, and worker outcomes.