Automated Ableism: A Systematic Review on AI and Discrimination against People with Disabilities - Lopes, Janaina Nogueira de Souza; Reis, Valéria Quadros dos; Castro Junior, Amaury Antônio de; Lima, Anderson Corrêa de (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248567, 2026)
Research Context: Artificial Intelligence (AI) systems have expanded in decision-making processes but raise concerns about algorithmic discrimination, with ableism remaining an underexplored dimension in Information Systems (IS). Scientific and/or Practical Problem: Despite advances in the literature on algorithmic bias, discrimination against people with disabilities (PwD) in AI systems remains underexplored. This gap limits the development of fairer and more inclusive AI systems. Proposed Solution and/or Analysis: This study presents a Systematic Literature Review (SLR) on how AI systems can reproduce, intensify, or mitigate ableist practices. Related IS Theory: It is grounded in algorithmic justice, fairness in machine learning, and socio-technical perspectives on inclusion. Research Method: Following PRISMA and PICOC, 26 articles published between 2020 and 2025 were analyzed and assessed against quality criteria. Summary of Results: Three main trends were identified: AI that reinforces ableism, mitigation initiatives, and ethical-regulatory debates. The problem has been discussed mainly by researchers from the United States and Europe, highlighting the need for studies that consider different languages as well as diverse social and cultural behaviors. Contributions and Impact to IS area: The study addresses a neglected dimension of algorithmic bias and provides insights for researchers, practitioners, and policymakers committed to developing inclusive AI.