Corresponding Manager: Raffaele Silvestre (raffaele.silvestre@collaboratore.uniparthenope.it)
Track Manager(s): Raffaele Silvestre, Alessandro Sapio, Mauro Romanelli
Description
This track intends to examines how the pervasive integration of AI risks amplifying Age Discrimination analyzing its ramifications in organizational, economic, and political contexts.
AI, often trained on biased historical data, can perpetuate age prejudice. The aim is to stimulate a multi-level debate and propose robust solutions considering different sectors.
Organizational Context:To analyze how, and with what effects, AI generate biases that disadvantage senior and junior employees.
Economic Context:To investigate the macroeconomic fallout of algorithmic discrimination, including assessing the systemic costs associated with excluding experienced talent and the impact on the sustainability of welfare systems and the silver economy.
Political Context:To discuss the necessary legislative and policy responses for the ethical and efficacy governance of AI.
We invite to submit contributions addressing, but are not limited to, the following themes:
Algorithmic Bias:origin and technical mitigation of ageist bias in datasets and machine learning models used for human resource management.
Business Impact:Developing models to manage and quantify cost of losing experiential knowledge and know-how for age discrimination.
Public Policy:Analysis of intergenerational upskilling policies and the role of governments in counteracting technology-induced professional obsolescence.
Transparency and Accountability:Transparent governance and auditing for human rights-compliant AI deployment.
Keywords
Artificial intelligence; Age discrimination; Ageism; Economic, Business and organizational impacts; Policy Responses
Key References
1. COUNCIL DIRECTIVE 2000/78/EC, establishing a general framework for equal treatment in employment and occupation.. Official Journal of the European Communities, 27 November 2000. https://eur-lex.europa.eu/eli/dir/2000/78/oj
2. Drydakis, N., Paraskevopoulou, A., Bozani, V.: A field study of age discrimination in the workplace: the importance of gender and race‒pay the gap. Employee Relations, 45(2), pp.304–327, (2023). DOI:10.1108/ER-06-2021-0277
3. Carlsson, M., Eriksson, S.: Age discrimination in hiring decisions: Evidence from a field experiment in the labor market. Labour Economics, 59, pp. 173–183, (2019). DOI: 10.1016/j.labeco.2019.03.002
4. Guangmei Jia, Xiaoyan Luo, Lisa C. Wan: How the elderly tackle age discrimination from human or AI servers. Annals of Tourism Research, Volume 113, July 2025, 103975. DOI: 10.1016/j.annals.2025.103975