Explainable AI: The new 42?

Randy Goebel, Ajay Chander, Katharina Holzinger, Freddy Lecue, Zeynep Akata, Simone Stumpf, Peter Kieseberg, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to “explain” their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value.. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings
PublisherSpringer Verlag Heidelberg
Pages295-303
Number of pages9
ISBN (Print)9783319997391
DOIs
Publication statusPublished - 1 Jan 2018
Event2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018 - Hamburg, Germany
Duration: 27 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11015 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018
CountryGermany
CityHamburg
Period27/08/1830/08/18

Fingerprint

Reasoning
Expert systems
Learning systems
Expert System
Network architecture
Artificial intelligence
Large scale systems
Machine Learning
Neural networks
Internal
Probabilistic Reasoning
Network Architecture
Expertise
Classification Problems
Knowledge Base
Exploitation
Human
Well-defined
Artificial Intelligence
Complex Systems

Keywords

  • Artificial intelligence
  • Explainability
  • Explainable AI
  • Machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., ... Holzinger, A. (2018). Explainable AI: The new 42? In Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings (pp. 295-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11015 LNCS). Springer Verlag Heidelberg. https://doi.org/10.1007/978-3-319-99740-7_21

Explainable AI : The new 42? / Goebel, Randy; Chander, Ajay; Holzinger, Katharina; Lecue, Freddy; Akata, Zeynep; Stumpf, Simone; Kieseberg, Peter; Holzinger, Andreas.

Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Springer Verlag Heidelberg, 2018. p. 295-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11015 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Goebel, R, Chander, A, Holzinger, K, Lecue, F, Akata, Z, Stumpf, S, Kieseberg, P & Holzinger, A 2018, Explainable AI: The new 42? in Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11015 LNCS, Springer Verlag Heidelberg, pp. 295-303, 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018, Hamburg, Germany, 27/08/18. https://doi.org/10.1007/978-3-319-99740-7_21
Goebel R, Chander A, Holzinger K, Lecue F, Akata Z, Stumpf S et al. Explainable AI: The new 42? In Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Springer Verlag Heidelberg. 2018. p. 295-303. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-99740-7_21
Goebel, Randy ; Chander, Ajay ; Holzinger, Katharina ; Lecue, Freddy ; Akata, Zeynep ; Stumpf, Simone ; Kieseberg, Peter ; Holzinger, Andreas. / Explainable AI : The new 42?. Machine Learning and Knowledge Extraction - Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings. Springer Verlag Heidelberg, 2018. pp. 295-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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