A norm optimisation approach to SDGs: tackling poverty by acting on discrimination

A norm optimisation approach to SDGs: tackling poverty by acting on discrimination

Georgina Curto, Nieves Montes, Carles Sierra, Nardine Osman, Flavio Comim

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AI for Good - Projects. Pages 5228-5235. https://doi.org/10.24963/ijcai.2022/726

Policies that seek to mitigate poverty by acting on equal opportunity have been found to aggravate discrimination against the poor (aporophobia), since individuals are made responsible for not progressing in the social hierarchy. Only a minority of the poor benefit from meritocracy in this era of growing inequality, generating resentment among those who seek to escape their needy situations by trying to climb up the ladder. Through the formulation and development of an agent-based social simulation, this study aims to analyse the role of norms implementing equal opportunity and social solidarity principles as enhancers or mitigators of aporophobia, as well as the threshold of aporophobia that would facilitate the success of poverty-reduction policies. The ultimate goal of the social simulation is to extract insights that could help inform and guide a new generation of policy making for poverty reduction by acting on the discrimination against the poor, in line with the UN “Leave No One Behind” principle. An “aporophobia-meter” will be developed and guidelines will be drafted based on both the simulation results and a review of poverty reduction policies at regional levels.
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AI for Good Project Proposal: AI for Good Project Proposal