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.
This is a preview of subscription content, log in via an institution to check access.
Access this article Subscribe and saveSpringer+ Basic
€34.99 /Month
Price includes VAT (Germany)
Instant access to the full article PDF.
Similar content being viewed by others Explore related subjectsDiscover the latest articles and news from researchers in related subjects, suggested using machine learning. ReferencesS.J. Andriole G.W. Hopple (1992) Applied Artificial Intelligence: A Sourcebook McGraw-Hill New York
R.K. Belew, J. McInerney and N.N. Schraudolph. Evolving Networks: Using the Genetic Algorithm with Connectionist Learning. Cognitive Computer Science Research Group, Computer Science & Engineering Department (C-014), University of California at San Diego. CSE Technical Report #CS90–174, June, 1990.
K.A. De Jong and A.C. Schultz. Using Experience-Based Learning in Game Playing. Proceedings of the Fifth International Machine Learning Conference, (pp. 284–290). Ann Arbor, Michigan, June 12–14, 1988
J.M. Fischer M.S.J. Ravizza (Eds) (1998) Responsibility and Control. A Theory of Moral Responsibility Cambridge University Press Cambridge
J. Holland (Eds) (1975) Adaptation in Natural and Artificial Systems University of Michigan Press Ann Arbor
G.S. Hornby, M. Fujita, S. Takamura and others. Autonomous Evolution of Gaits with the Sony Quadruped Robot. Group 1, D-21 Laboratory, Sony Corporation Ph: 81-3-5448-5901. Tokyo, Japan, 1999.
E. Llobet E.L. Hines J.W. Gardner S. Franco (1999) ArticleTitleNon-Destructive Banana Ripeness Determination Using a Neural Network-Based Electronic Nose. Measurement Science Technology 10 538–548
D.E. Moriarty A.C. Schultz J.J. Grefenstette (1999) ArticleTitleEvolutionary Algorithms for Reinforcement Learning. Journal of Artificial Intelligence Research 11 199–229
J.C. Morrison and T.T. Nguyen. On-Board Software for the Mars Pathfinder Microrover. Jet Propulsion Laboratory report IAA-L-0504P, 1996
S. Nolfi and D. Parisi. Growing Neural Networks. Institute of Psychology, National Research Council Technical Report PCIA-91-15, Rome, Italy, 1991. (Presented at Artificial Life III, Santa Fe, New Mexico, June 15–19, 1992.)
M.A.L. Oshana (2002) ArticleTitleThe Misguided Marriage of Responsibility and Autonomy. The Journal of Ethics 6 261–280
OTIS Elevators. Redefining Elevator Performance, Safety and Comfort: The OTIS Elevonic Class. Product Description, 2003. Available: http://www.otis.com
K. Sasaki, S. Markon and M. Makagawa. Elevator Group Supervisory Control System Using Neural Networks. Elevator World, 1, 1996
Schindler Elevator Corporation AITP: Artificial Intelligence Traffic Processor. Technical Product Description, 2003. Available: http://www.us.schindler.com
A.C. Schultz. Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation. In Proceedings of the Seventh International Symposium on Unmanned Untethered Submersible Technology, (pp. 213–215). University of New Hampshire Marine Systems Engineering Laboratory, New Hampshire, September 23–25, 1991
A.C. Schultz. Learning Robot Behaviors Using Genetic Algorithms. In. Proceedings of the International Symposium on Robotics and Manufacturing. Washington DC, August 14–18, 1994.
S.B. Stancliff and M.C. Nechyba. Learning to Fly: Modeling Human Control Strategies in an Aerial Vehicle. Machine Intelligence Laboratory, Electrical and Computer Engineering, University of Florida, 2000. Available: http://www.mil.ufl.edu/publications.
W. Stolzmann M.V. Butz J. Hoffmann D.E. Goldberg (Eds) (2000) First Cognitive Capabilities in the Anticipatory Classifier System University of Illinois Urbana
P. Strawson. Freedom and Resentment. Proceedings of the British Academy, p. 48 Britain, 1962.
B.-T. Zhang and Y.-W. Seo. Personalized Web-Document Filtering Using Reinforcement Learning. AI Lab, School of Computer Science and Engineering, Seoul National University, Korea, 2000. Available: http://www.scai. snu.ac.kr
Z.H. Zhou Y. Jiang Y.B. Yang S. F. Chen (2002) ArticleTitleLung Cancer Cell Identification Based on Artificial Neural Network Ensembles. Artificial Intelligence in Medicine 24 IssueID1 25–36
Computing Centre, University of Kassel, D-34109, Kassel, Germany
Andreas Matthias
Correspondence to Andreas Matthias.
About this article Cite this articleMatthias, A. The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics Inf Technol 6, 175–183 (2004). https://doi.org/10.1007/s10676-004-3422-1
Issue Date: September 2004
DOI: https://doi.org/10.1007/s10676-004-3422-1
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4