Coercive environment as an excusing factor in responsibility ascription

2019 ◽  
pp. 101-137
Author(s):  
Francis K. T. Mok
Keyword(s):  
Responsibility Ascription

From Coder to Creator

2009 ◽  
pp. 635-650
Author(s):  
Andreas Matthias
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Processing Speed ◽  
Body Problem ◽  
Legal Responsibility ◽  
Complete Control ◽  
Responsibility Ascription ◽  
The Mind ◽  
Effective Supervision

Creation of autonomously acting, learning artifacts has reached a point where humans cannot any more be justly held responsible for the actions of certain types of machines. Such machines learn during operation, thus continuously changing their original behaviour in uncontrollable (by the initial manufacturer) ways. They act without effective supervision and have an epistemic advantage over humans, in that their extended sensory apparatus, their superior processing speed and perfect memory render it impossible for humans to supervise the machine’s decisions in real-time. We survey the techniques of artificial intelligence engineering, showing that there has been a shift in the role of the programmer of such machines from a coder (who has complete control over the program in the machine) to a mere creator of software organisms which evolve and develop by themselves. We then discuss the problem of responsibility ascription to such machines, trying to avoid the metaphysical pitfalls of the mind-body problem. We propose five criteria for purely legal responsibility, which are in accordance both with the findings of contemporary analytic philosophy and with legal practise. We suggest that Stahl’s (2006) concept of “quasi-responsibility” might also be a way to handle the responsibility gap.

scholarly journals Autonomous Weapons Systems and Meaningful Human Control: Ethical and Legal Issues

2020 ◽  
Vol 1 (4) ◽  
pp. 187-194
Author(s):  
Daniele Amoroso ◽  
Guglielmo Tamburrini
Keyword(s):  
Artificial Intelligence ◽  
Research Community ◽  
Legal Issues ◽  
Shared Control ◽  
Human Intervention ◽  
Human Control ◽  
Autonomous Weapons ◽  
Responsibility Ascription ◽  
Ethical And Legal Issues ◽  
Weapons Systems

Abstract Purpose of Review To provide readers with a compact account of ongoing academic and diplomatic debates about autonomy in weapons systems, that is, about the moral and legal acceptability of letting a robotic system to unleash destructive force in warfare and take attendant life-or-death decisions without any human intervention. Recent Findings A précis of current debates is provided, which focuses on the requirement that all weapons systems, including autonomous ones, should remain under meaningful human control (MHC) in order to be ethically acceptable and lawfully employed. Main approaches to MHC are described and briefly analyzed, distinguishing between uniform, differentiated, and prudential policies for human control on weapons systems. Summary The review highlights the crucial role played by the robotics research community to start ethical and legal debates about autonomy in weapons systems. A concise overview is provided of the main concerns emerging in those early debates: respect of the laws of war, responsibility ascription issues, violation of the human dignity of potential victims of autonomous weapons systems, and increased risks for global stability. It is pointed out that these various concerns have been jointly taken to support the idea that all weapons systems, including autonomous ones, should remain under meaningful human control (MHC). Main approaches to MHC are described and briefly analyzed. Finally, it is emphasized that the MHC idea looms large on shared control policies to adopt in other ethically and legally sensitive application domains for robotics and artificial intelligence.

scholarly journals A Game-Theoretic Account of Responsibility Allocation

2021 ◽  
Author(s):  
Christel Baier ◽  
Florian Funke ◽  
Rupak Majumdar
Keyword(s):  
Imperfect Information ◽  
Fundamental Problem ◽  
Multi Agent Systems ◽  
Extensive Form ◽  
Responsibility Attribution ◽  
Agent Interaction ◽  
Responsibility Ascription ◽  
Multi Agent ◽  
Game Theoretic ◽  
Extensive Form Game

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.

scholarly journals Learning robots and human responsibility

2006 ◽  
Vol 6 ◽  
pp. 46-51
Author(s):  
Dante Marino ◽  
Guglielmo Tamburrini
Keyword(s):  
Machine Learning ◽  
Moral Responsibility ◽  
Inductive Learning ◽  
Human Beings ◽  
Learning Methods ◽  
Human System ◽  
Machine Learning Methods ◽  
Responsibility Ascription ◽  
Human Responsibility ◽  
Supervised Inductive Learning

Epistemic limitations concerning prediction and explanation of the behaviour of robots that learn from experience are selectively examined by reference to machine learning methods and computational theories of supervised inductive learning. Moral responsibility and liability ascription problems concerning damages caused by learning robot actions are discussed in the light of these epistemic limitations. In shaping responsibility ascription policies one has to take into account the fact that robots and softbots – by combining learning with autonomy, pro-activity, reasoning, and planning – can enter cognitive interactions that human beings have not experienced with any other non-human system.

Social Interaction in Responsibility Ascription: The Case of Household Recycling

2010 ◽  
Vol 86 (4) ◽  
pp. 766-784 ◽  
Author(s):  
K. A. Brekke ◽  
G. Kipperberg ◽  
K. Nyborg
Keyword(s):  
Social Interaction ◽  
Responsibility Ascription
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