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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Automating Misrecognition: The Case of Disability.

Jackie Leach Scully1, Georgia van Toorn2, Sandra Gendera3

  • 1Disability Innovation Institute, UNSW Sydney, Goodsell Building, Kensington, NSW 2052, Australia. jackie.leach.scully@unsw.edu.au.

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Summary
This summary is machine-generated.

Automated decision-making (ADM) in healthcare can fail to recognize disabled individuals, perpetuating marginalization. This study highlights the critical need for AI systems that acknowledge and validate the experiences of people with disabilities.

Keywords:
Artificial intelligenceAutomated decision-makingDisabilityDiscriminationEpistemic justiceHuman in the loopRecognition

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Area of Science:

  • Bioethics
  • Artificial Intelligence Ethics
  • Disability Studies

Background:

  • Bioethics is increasingly addressing ethical concerns surrounding artificial intelligence (AI) and automated decision-making (ADM) in healthcare and research.
  • Existing research on AI justice highlights how AI systems can reinforce societal marginalization.
  • The intersection of AI, ADM, and disability remains underexplored in current literature.

Purpose of the Study:

  • To investigate the implications of ADM in the lives of people with disabilities in Australia.
  • To identify and analyze the ethical challenges posed by AI and ADM in the context of disability.
  • To bring attention to the issue of automated systems' failures of recognition, a concern frequently raised by disabled individuals.

Main Methods:

  • Empirically based research project.
  • Qualitative data collection through discussions with disabled participants in Australia.
  • Focus on lived experiences and perspectives of individuals with disabilities regarding ADM.

Main Results:

  • Disabled participants consistently raised concerns about automated systems' failures to recognize their unique needs and experiences.
  • These failures of recognition by ADM systems can lead to marginalization and perpetuate inequalities.
  • The findings underscore a significant gap in AI ethics literature concerning disability and recognition.

Conclusions:

  • Automated decision-making systems in healthcare and research exhibit critical failures in recognizing individuals with disabilities.
  • Addressing these failures of recognition is crucial for ensuring justice and equity in AI applications.
  • Further research and ethical guidelines are needed to develop inclusive AI that respects the diversity and experiences of people with disabilities.