The Guilty (Silicon) Mind: Blameworthiness and Liability in Human-Machine Teaming
October 10, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dr Brendan Walker-Munro, Dr Zena Assaad
arXiv ID
2210.04456
Category
cs.HC: Human-Computer Interaction
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
As human science pushes the boundaries towards the development of artificial intelligence (AI), the sweep of progress has caused scholars and policymakers alike to question the legality of applying or utilising AI in various human endeavours. For example, debate has raged in international scholarship about the legitimacy of applying AI to weapon systems to form lethal autonomous weapon systems (LAWS). Yet the argument holds true even when AI is applied to a military autonomous system that is not weaponised: how does one hold a machine accountable for a crime? What about a tort? Can an artificial agent understand the moral and ethical content of its instructions? These are thorny questions, and in many cases these questions have been answered in the negative, as artificial entities lack any contingent moral agency. So what if the AI is not alone, but linked with or overseen by a human being, with their own moral and ethical understandings and obligations? Who is responsible for any malfeasance that may be committed? Does the human bear the legal risks of unethical or immoral decisions by an AI? These are some of the questions this manuscript seeks to engage with.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted