{'fraction_non_alphanumeric': 0.01861547411285631, 'fraction_numerical': 0.0, 'mean_word_length': 5.323529411764706, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Blind Brains and Moral Machines: Neuroscience and Autonomous Weapon Systems

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.