AI Article Synopsis
- The study explores how students in algorithm-related fields view key concepts like fairness, accountability, transparency, and ethics in algorithmic decision-making.
- Participants (N = 99) rated their agreement with statements about fairness and justice in algorithms and shared their thoughts on fairness definitions and causes of unfairness.
- Findings reveal that agreement with outcomes does not imply fairness, misperceptions about decision factors affect trust, and participants believe that objective factors are fairer, while sensitive attributes lead to unfairness.
Article Abstract
In this work, we investigate how students in fields adjacent to algorithms development perceive fairness, accountability, transparency, and ethics in algorithmic decision-making. Participants (N = 99) were asked to rate their agreement with statements regarding six constructs that are related to facets of fairness and justice in algorithmic decision-making using scenarios, in addition to defining algorithmic fairness and providing their view on possible causes of unfairness, transparency approaches, and accountability. The findings indicate that "agreeing" with a decision does not mean that the person "deserves the outcome," perceiving the factors used in the decision-making as "appropriate" does not make the decision of the system "fair," and perceiving a system's decision as "not fair" is affecting the participants' "trust" in the system. Furthermore, fairness is most likely to be defined as the use of "objective factors," and participants identify the use of "sensitive attributes" as the most likely cause of unfairness.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767291 | PMC |
| http://dx.doi.org/10.1016/j.patter.2021.100380 | DOI Listing |
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