
Contents
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13.1: Introduction 13.1: Introduction
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13.2: How Robots
ThinkBehave 13.2: How RobotsThinkBehave -
13.3: Leveraging the Human 13.3: Leveraging the Human
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13.4: Blind Brains and Moral Machines: Replicating Strength or Weakness? 13.4: Blind Brains and Moral Machines: Replicating Strength or Weakness?
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13.5: The Responsibility Gap and Human-Inspired LAWS 13.5: The Responsibility Gap and Human-Inspired LAWS
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13.5.1: Equivalence 13.5.1: Equivalence
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13.5.2: Trade-Off 13.5.2: Trade-Off
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13.5.3: Non-Inferiority 13.5.3: Non-Inferiority
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13.6: Policies Moving Forward 13.6: Policies Moving Forward
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Acknowledgment Acknowledgment
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Note Note
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Works Cited Works Cited
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13 Blind Brains and Moral Machines: Neuroscience and Autonomous Weapon Systems
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Published:April 2021
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Abstract
While the majority of neuroscience research promises novel therapies for treating dementia and post-traumatic stress disorder, among others, a lesser-known branch of neuroscientific research informs the construction of artificial intelligence inspired by human neurophysiology. For those concerned with the normative implications of autonomous weapons systems (AWS), however, a tension arises between the primary attraction of AWS, their theoretic capacity to make better decisions in armed conflict, and the relatively low-hanging fruit of modeling machine intelligence on the very thing that causes humans to make (relatively) bad decisions—the human brain. This chapter examines human cognition as a model for machine intelligence, and some of its implications for AWS development. It first outlines recent neuroscience developments as drivers for advances in artificial intelligence. This chapter then expands on a key distinction for the ethics of AWS: poor normative decisions that are a function of poor judgments given a certain set of inputs, and poor normative decisions that are a function of poor sets of inputs. It argues that given that there are cases in the second category of decisions in which we judge humans to have acted wrongly, we should likewise judge AWS platforms. Further, while an AWS may in principle outperform humans in the former, it is an open question of design whether they can outperform humans in the latter. Finally, this chapter then discusses what this means for the design and control of, and ultimately liability for AWS behavior, and sources of inspiration for the alternate design of AWS platforms.
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