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Perception of fairness in algorithmic decisions: Future developers' perspective.

Styliani Kleanthous1,2, Maria Kasinidou1, Pınar Barlas2

  • 1Cyprus Center for Algorithmic Transparency, Open University of Cyprus, Faculty of Pure & Applied Sciences, 33 Yiannou Kranidioti Avenue, 2220 Latsia, Nicosia, Cyprus.

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Summary
This summary is machine-generated.

Students perceive algorithmic fairness differently than system outcomes. Defining fairness as objective factors, they believe sensitive attributes cause unfairness, impacting trust in algorithmic decision-making.

Keywords:
algorithmic accountabilityalgorithmic decision-makingalgorithmic transparencyperceptions of algorithmic fairness

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Area of Science:

  • Computer Science
  • Social Science
  • Ethics

Background:

  • Algorithmic decision-making systems are increasingly prevalent.
  • Understanding perceptions of fairness, accountability, transparency, and ethics (FATE) is crucial.
  • Student perspectives in adjacent fields offer unique insights into algorithmic impacts.

Purpose of the Study:

  • To investigate how students adjacent to algorithm development perceive fairness, accountability, transparency, and ethics in algorithmic decision-making.
  • To explore definitions of algorithmic fairness and identify perceived causes of unfairness.
  • To assess the relationship between perceived fairness, trust, and system outcomes.

Main Methods:

  • Surveying 99 students using scenario-based agreement ratings on six fairness constructs.
  • Asking participants to define algorithmic fairness and identify causes of unfairness.
  • Examining views on transparency and accountability in algorithmic systems.

Main Results:

  • Agreement with a decision does not equate to deserving the outcome.
  • Appropriate decision-making factors do not guarantee a fair system decision.
  • Perceived unfairness significantly erodes trust in algorithmic systems.
  • Algorithmic fairness is primarily defined by objective factors.
  • Sensitive attributes are identified as the leading cause of algorithmic unfairness.

Conclusions:

  • Student perceptions highlight a disconnect between agreement and fairness, and between appropriate factors and fair outcomes.
  • Trust in algorithmic systems is directly undermined by perceived unfairness.
  • Objective factors are key to defining fairness, while sensitive attributes pose the greatest risk of unfairness.