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As robots become more autonomous, a "retribution gap" may emerge, leaving no one to blame for harm. This study argues we must manage our desire for retribution, similar to implicit biases, rather than seeking blame where none exists.

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Area of Science:

  • Ethics
  • Philosophy of Technology
  • Robotics

Background:

  • Increasing robot autonomy presents challenges for moral responsibility and blame.
  • The concept of a "retribution gap" arises when autonomous systems cause harm without clear candidates for blame.
  • John Danaher's work highlights the social and moral implications of this gap.

Purpose of the Study:

  • To analyze the nature of retributive intuitions in the context of robot-caused harm.
  • To argue against using retributive intuitions to justify punishment when blame is unclear.
  • To propose a framework for managing retributive intuitions in the age of autonomous robots.

Main Methods:

  • Deontological analysis of retributive intuitions.
  • Application of a debunking argument to retributive punishment.
  • Drawing parallels with research on implicit bias and moral responsibility.

Main Results:

  • Retributive intuitions fueling retribution gaps are best understood as deontological.
  • Retributive intuitions do not justify punishment for robot harm without clear blame.
  • A new approach is needed to address these intuitions.

Conclusions:

  • We must take moral responsibility for our retributive intuitions, even when they are not justified by clear blame.
  • Managing these intuitions, akin to controlling implicit biases, is crucial for societal well-being.
  • This research offers a path forward for ethical considerations in advanced robotics.