The responsibility gap: Ascribing responsibility for the actions of learning automata

被引：426

作者：

Matthias A. ^{[1
]}

机构：

[1] Computing Centre, University of Kassel

来源：

Ethics and Information Technology | 2004年 / 6卷 / 3期

关键词：

Artificial intelligence; Autonomous robots; Learning machines; Liability; Moral responsibility;

D O I：

10.1007/s10676-004-3422-1

中图分类号：

学科分类号：

摘要：

Traditionally, the manufacturer/operator of a machine is held (morally and legally) responsible for the consequences of its operation. Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it. The society must decide between not using this kind of machine any more (which is not a realistic option), or facing a responsibility gap, which cannot be bridged by traditional concepts of responsibility ascription. © 2004 Kluwer Academic Publishers.

引用

页码：175 / 183

页数：8

共 21 条

[1]

Andriole S.J., Hopple G.W., Applied Artificial Intelligence: A Sourcebook, (1992)

[2]

Belew R.K., Mclnerney J., Schraudolph N.N., Evolving networks: Using the genetic algorithm with connectionist learning, Cognitive Computer Science Research Group, Computer Science & Engineering Department (C-014), (1990)

[3]

De Jong K.A., Schultz A.C., Using experience-based learning in game playing, Proceedings of the Fifth International Machine Learning Conference, pp. 284-290, (1988)

[4]

Fischer J.M., Ravizza M.S.J., Responsibility and Control. A Theory of Moral Responsibility, (1998)

[5]

Holland J., Adaptation in Natural and Artificial Systems, (1975)

[6]

Hornby G.S., Fujita M., Takamura S., Autonomous Evolution of Gaits with the Sony Quadruped Robot, (1999)

[7]

Llobet E., Hines E.L., Gardner J.W., Franco S., Non-destructive banana ripeness determination using a neural network-based electronic nose, Measurement Science & Technology, 10, pp. 538-548, (1999)

[8]

Moriarty D.E., Schultz A.C., Grefenstette J.J., Evolutionary algorithms for reinforcement learning, Journal of Artificial Intelligence Research, 11, pp. 199-229, (1999)

[9]

Morrison J.C., Nguyen T.T., On-board software for the mars pathfinder microrover, Jet Propulsion Laboratory Report IAA-L-0504P, (1996)

[10]

Nolfi S., Parisi D., Growing neural networks, Institute of Psychology National Research Council Technical Report PCIA-91-15, (1991)

← 1 2 3 →