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Motivating Transparent Communications about Bias in Healthcare Technology Development.

Anna Tovmasyan1,2, Alice Liefgreen3, Sandra Wachter2

  • 1School of Psychology and Clinical Language Sciences, University of Reading.

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Summary
This summary is machine-generated.

Motivational messaging does not directly increase AI developers' intention to train on bias transparency. However, both internal and external motivations are linked to greater intention for skill-building and ethical communication regarding AI bias.

Keywords:
healthcare AIinclusionmotivationtransparencyvalues

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Area of Science:

  • Computer Science
  • Artificial Intelligence Ethics

Background:

  • Healthcare artificial intelligence (AI) systems are increasingly sophisticated, yet their potential for bias, particularly concerning patient protected characteristics, is a growing concern.
  • Developers often fail to disclose these inherent limitations, raising questions about transparency in AI development.

Purpose of the Study:

  • To investigate whether supportive motivational messaging influences healthcare AI developers' willingness to communicate transparently about bias in their technology.
  • To explore the impact of different motivational framings (autonomy-supportive vs. controlling) and emphasis (personal benefits vs. legal implications) on developers' intentions.

Main Methods:

  • Two studies involving computer science students (N=271 and N=209) were conducted.
  • Participants were randomly assigned to receive communications framed in either an autonomy-supportive or controlling manner, emphasizing personal gain or legal consequences of non-transparency.
  • Behavioral intentions related to transparency training, skill development, ethical voice, and antagonism were measured.

Main Results:

  • Communication framing (autonomy-supportive vs. controlling) did not significantly affect the intention to enroll in a bias transparency educational course.
  • Both internal (self-directed) and external motivations were positively associated with the intention to acquire skills for transparent technology development.
  • Increased motivation correlated with a greater ethical voice and reduced antagonism towards transparency.

Conclusions:

  • A singular, brief training intervention is insufficient to foster transparent communication about AI bias.
  • Universities and workplaces should cultivate a broadly supportive motivational environment to encourage transparency and ethical practices among AI developers.