Skip to main content

Advertisement

Springer Nature Link
Log in

Please delete that! Why should I?

Explaining learned irrelevance classifications of digital objects

  • Technical Contribution
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Dare2Del is an assistive system which facilitates intentional forgetting of irrelevant digital objects. For an assistive system to be helpful, the user has to trust the system’s decisions. Explanations are a crucial component in establishing this trust. We will introduce different types of explanations which can vary along different dimensions such as level of detail and modality suitable for different application contexts. We will outline the cognitive companion system Dare2Del which is intended to support users managing digital objects in a working environment. Core of Dare2Del is an interpretable machine learning mechanism which induces decision rules to classify whether a digital objects is irrelevant. In this paper, we focus on irrelevance of files. We formalize the decision making process as logic inference. Finally, we present a method to generate verbal explanations for irrelevance decisions and point out how such explanations can be constructed on different levels of details. Furthermore, we show how verbal explanations can be related to the path context of the file. We conclude with a short discussion of the scope and restrictions of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Chair of Work and Organizational Psychology, Friedrich-Alexander Universität Erlangen-Nürnberg, https://www.work-and-organizational-psychology.phil.fau.eu.

  2. If \(C\theta\) is not ground we may substitute every variable in \(C\theta\) with a unique constant.

  3. In order to realize coloured highlighting we designed the string templates to produce valid HTML strings. These are rendered by the displaying component.

References

  1. Baader F, Nutt W (2003) Basic description logics. In: Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (eds) The description logic handbook. Cambridge University Press, Cambridge, pp 43–95

    Google Scholar 

  2. Biundo S, Wendemuth A (2016) Companion-technology for cognitive technical systems. Künstliche Intell 30(1):71–75

    Article  Google Scholar 

  3. Bjork EL, Bjork RA, Anderson MC (1998) Varieties of goal-directed forgetting. In: Golding JM, MacLeod CM (eds) Intentional forgetting: interdisciplinary approaches, vol 103. Lawrence Erlbaum, Mahwah

    Google Scholar 

  4. Clancey WJ (1983) The epistemology of a rule-based expert system—a framework for explanation. Artif Intell 20(3):215–251

    Article  Google Scholar 

  5. Cropper A, Muggleton SH. Metagol system. https://github.com/metagol/metagol

  6. De Raedt L (2008) Logical and relational learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68856-3

    Book  MATH  Google Scholar 

  7. Eppler MJ, Mengis J (2004) The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society 20(5):325–344. https://doi.org/10.1080/01972240490507974

    Article  Google Scholar 

  8. Fails JA, Olsen DR Jr (2003) Interactive machine learning. In: Proceedings of the 8th international conference on Intelligent User Interfaces. ACM, New York, pp 39–45

    Google Scholar 

  9. Forbus KD, Hinrichs TR (2006) Companion cognitive systems—a step toward human-level AI. AI Mag 27(2):83–95

    Google Scholar 

  10. Fürnkranz J, Kliegr T, Paulheim H (2018) On cognitive preferences and the interpretability of rule-based models. arXiv:1803.01316[cs.LG] (Preprint)

  11. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27. Curran Associates, Inc., pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

  12. Gulwani S, Hernandez-Orallo J, Kitzelmann E, Muggleton SH, Schmid U, Zorn B (2015) Inductive programming meets the real world. Commun ACM 58(11):90–99

    Article  Google Scholar 

  13. Hengstler M, Enkel E, Duelli S (2016) Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol Forecast Soc Change 105:105–120

    Article  Google Scholar 

  14. Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65

    Article  Google Scholar 

  15. Huth EJ (1989) The information explosion. Bull N Y Acad Med 65(6):647–672

    Google Scholar 

  16. Jameson A, Schäfer R, Weis T, Berthold A, Weyrath T (1999) Making systems sensitive to the user’s changing resource limitations. Knowl Based Syst 12(8):413–425

    Article  Google Scholar 

  17. Kruschke JK (2008) Models of categorization. In: Sun R (ed) The Cambridge handbook of computational psychology. Cambridge University Press, Cambridge, pp 267–301

    Chapter  Google Scholar 

  18. Lakkaraju H, Bach SH, Leskovec J (2016) Interpretable decision sets: a joint framework for description and prediction. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 1675–1684

    Chapter  Google Scholar 

  19. Lombrozo T (2016) Explanatory preferences shape learning and inference. Trends Cogn Sci 20(10):748–759

    Article  Google Scholar 

  20. Lombrozo T, Vasilyeva N (2017) Causal explanation. In: Waldmann M (ed) Oxford handbook of causal reasoning. Oxford University Press, Oxford, pp 415–432

    Google Scholar 

  21. Loza Mencía E, Fürnkranz J (2018) Interpretable machine learning. In: ECDA (ed) Book of abstracts, 5th European conference on data analysis, pp 56–60. http://groups.uni-paderborn.de/eim-i-fg-huellermeier/ecda2018/downloads/ECDA2018-BoA.pdf

  22. Marcus G (2018) Deep learning: a critical appraisal. arXiv:1801.00631v1 [cs.AI] (Preprint)

  23. Markman AB, Gentner D (1996) Commonalities and differences in similarity comparisons. Mem Cogn 24(2):235–249

    Article  Google Scholar 

  24. Michie D (1988) Machine learning in the next five years. In: Proceedings of the third European working session on learning. Pitman, New York, pp 107–122

    Google Scholar 

  25. Muggleton S (1995) Inverse entailment and Progol. New Gener Comput 13(3–4):245–286

    Article  Google Scholar 

  26. Muggleton S, De Raedt L (1994) Inductive logic programming: theory and methods. J Logic Programm 19–20:629–679

    Article  MathSciNet  MATH  Google Scholar 

  27. Muggleton SH, Lin D, Tamaddoni-Nezhad A (2015) Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach Learn 100:49–73. https://doi.org/10.1007/s10994-014-5471-y

    Article  MathSciNet  MATH  Google Scholar 

  28. Muggleton SH, Schmid U, Zeller C, Tamaddoni-Nezhad A, Besold T (2018) Ultra-strong machine learning: comprehensibility of programs learned with ILP. Mach Learn 107(7):1119–1140. https://doi.org/10.1007/s10994-018-5707-3

    Article  MathSciNet  MATH  Google Scholar 

  29. Niessen C, Göbel K, Siebers M, Schmid U Time to forget: a review and conceptual framework of intentional forgetting in the digital world of work. Z Arbeits Org [German Journal of Work and Organizational Psychology] (to appear)

  30. Potter J, Wetherell M (1987) Discourse and social psychology: beyond attitudes and behaviour. Sage, Thousand Oaks

    Google Scholar 

  31. Pu P, Chen L (2007) Trust-inspiring explanation interfaces for recommender systems. Knowl Based Syst 20(6):542–556

    Article  Google Scholar 

  32. Rabold J, Siebers M, Schmid U (2018) Explaining black-box classifiers with ILP—empowering LIME with Aleph to approximate non-linear decisions with relational rules. In: Riguzzi F, Bellodi E, Zese R (eds) Proceedings of the 28th international conference on inductive logic programming, pp 105–117

  33. Reed SK, Bolstad CA (1991) Use of examples and procedures in problem solving. J Exp Psychol Learn Mem Cogn 17(4):753–766

    Article  Google Scholar 

  34. Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135–1144. ACM. http://arxiv.org/abs/1602.04938

  35. Roth-Berghofer T, Richter MM (2008) Schwerpunkt: Erklärungen [special issue: explanations]. Künstliche Intell 22(2)

  36. Sadoski M, Paivio A (2013) Imagery and text: a dual coding theory of reading and writing. Routledge, Abingdon

    Book  Google Scholar 

  37. Schmid U (1994) Programmieren lernen: Unterstützung des Erwerbs rekursiver Programmiertechniken durch Beispielfunktionen und Erklärungstexte [Learning programming: Acquisition of recursive programming skills from examples and explanations]. Kognitionswissenschaft 4(1):47–54

    Google Scholar 

  38. Schmid U (2018) Inductive programming as approach to comprehensible machine learning. In: Beierle C, Kern-Isberner G, Ragni M, Stolzenburg F, Thimm M (eds) Proceedings of the 7th workshop on dynamics of knowledge and belief (DKB-2018) and the 6th workshop KI & Kognition (KIK-2018), co-located with 41st German conference on artificial intelligence, vol 2194. CEUR Workshop Proceedings

  39. Schmid U, Kitzelmann E (2011) Inductive rule learning on the knowledge level. Cogn Syst Res 12(3):237–248

    Article  Google Scholar 

  40. Siebers M, Göbel K, Niessen C, Schmid U (2017) Requirements for a companion system to support identifying irrelevancy, pp 1–2. https://doi.org/10.1109/COMPANION.2017.8287076

  41. Soucek R, Moser K (2010) Coping with information overload in email communication: evaluation of a training intervention. Comput Hum Behav 26(6):1458–1466. https://doi.org/10.1016/j.chb.2010.04.024

    Article  Google Scholar 

  42. Srinivasan A (2004) The Aleph manual. http://www.cs.ox.ac.uk/activities/machinelearning/Aleph/

  43. Suthers DD (1993) An analysis of explanation and its implications for the design of explanation planners. Ph.D. Thesis, University of Massachusetts

  44. Sweeney L (2001) Information explosion. In: Zayatz L, Doyle P, Theeuwes J, Lane J (eds) Confidentiality, disclosure, and data access: theory and practical applications for statistical agencies. Urban Institute, Washington, pp 43–74

    Google Scholar 

  45. Tintarev N, Masthoff J (2012) Evaluating the effectiveness of explanations for recommender systems. User Model User Adapt Interact 22(4):399–439

    Article  Google Scholar 

  46. Tintarev N, Masthoff J (2015) Explaining recommendations: design and evaluation. In: Recommender systems handbook. Springer, Berlin, pp 353–382

    Chapter  Google Scholar 

  47. Wang W, Benbasat I (2007) Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J Manag Inf Syst 23(4):217–246. https://doi.org/10.2753/MIS0742-1222230410

    Article  Google Scholar 

  48. Winston PH (1975) Learning structural descriptions from examples. In: Winston PH (ed) The psychology of computer vision. McGraw-Hill, New York, pp 157–210

    Google Scholar 

  49. Zeller C, Schmid U (2016) Automatic generation of analogous problems to help resolving misconceptions in an intelligent tutor system for written subtraction. In: Coman A, Kapetanakis S (eds) Workshops proceedings for the 24th international conference on case-based reasoning, CEUR workshop proceedings, vol 1815, pp 108–117. http://ceur-ws.org/Vol-1815/paper11.pdf

  50. Zeller C, Schmid U (2017) A human like incremental decision tree algorithm: combining rule learning, pattern induction, and storing examples. In: Leyer M (ed) LWDA conference proceedings, vol 1917, pp 64–73. CEUR workshop proceedings. http://ceur-ws.org/Vol-1917/paper12.pdf

Download references

Author information

Authors and Affiliations

  1. Cognitive Systems, University of Bamberg, Bamberg, Germany

    Michael Siebers & Ute Schmid

Authors
  1. Michael Siebers
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ute Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Siebers.

Additional information

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—SCHM 1239/10-1 within the priority program SPP 1921 Intentional Forgetting.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Siebers, M., Schmid, U. Please delete that! Why should I?. Künstl Intell 33, 35–44 (2019). https://doi.org/10.1007/s13218-018-0565-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-018-0565-5

Keywords

Search

Navigation