1. Some Comments on “The Reports of My Death are Greatly Exaggerated – Expert Systems Research in Accounting” Daniel E. O’Leary University of Southern California © 2015

2. Overview My perspective - I am “Pro” AI Sub-Disciplines of AI “AI Renaissance” Good news and bad news of “expert systems” Gray et al. expert systems findings Research methodologies Life Cycle Models Integration of AI into other Technologies Summary

3. My Biased Perspective - I am “Pro” AI I am editor of Intelligent Systems in Accounting, Finance and Management – Preceded by the Expert Systems Review (1987-1990) From 1997 - 2001 I was editor of IEEE Intelligent Systems I am a senior editor or advisory board member on a number of AI- based (“near AI”) editorial boards – IEEE Intelligent Systems Advisory Board – Knowledge and Information Systems (2002 – to-date) Senior Editor. – Decision Support Systems (2014 to date) Editorial Advisory Board I am on the editorial board of a number of AI-based journals – Expert Systems with Applications: An International Journal (1990 to- date); – Expert Systems: The Journal of Knowledge Engineering (2007-to-date); – International Journal of Agent-Oriented Software Engineering (2005- to-date)

4. Intelligent Systems in Accounting, Finance and Management Originally it was the section journal for what is now the “Strategic and Emerging Technologies Section” 23 Years of publishing AI and Accounting and Business Application papers Increasingly we get cool papers from computational economics and finance – Particle Swarm Optimization – Dynamic Fuzzy Approach to Risk – Self Organizing Maps and Risk – Multi-agent simulations Currently a call for papers on “Enterprise Ontologies and Semantic Models” (December 15)

5. Gray et al.’s list of Expert Systems Publications

6. There are many sub-disciplines and views of AI Expert Systems Case-based reasoning Certainty Factors Bayes Nets Genetic Algorithms Neural Networks Multiple Agent Systems Semantic Web Ontologies Natural language processing … AI and Knowledge Management AI and Continuous Monitoring AI and Big Data AI and Twitter Mining AI and Audit Analytics AI and Internet of Things AI and Question asking and answering …

7. January 1997, IEEE Computer

8. AI is anything but dead… But it is arguable that classic accounting and auditing expert systems are dead Even more broadly, AIS researchers do not work on stand alone systems – In fact, there are hardly any accounting information systems researchers building any kind of system or artifact – Increasingly, researchers are turning to archival and behavioral Probably for some good reasons …

9. Good News and Bad News of Building an Expert System Good News – Seems relatively easy to capture rules used in some decision making settings – Seems easy to put likelihood or certainty factor estimates on rules – Much knowledge is unstructured and we would like to structure – Very seductive – to build a system that is an “expert” Bad News – Devil is in details, e.g., “if sales are increasing …” Systematic differences in the words “increasing” – Seems easy enough, yet few have done it well – many fundamental errors made – Rules are structured and very fragile. – Uncertainty is difficult – People are forgetting the past lessons What went wrong back at the ranch?

10. IJAIS 2003

11. Expertise makes a difference when using systems

12. Gray et al. Findings Decreasing number of publications in Expert Systems in accounting, auditing and tax Decreasing number of dissertations in Expert Systems in accounting, auditing and tax Decreasing number of presentations in Expert Systems at AAA meetings Drill down …

13. Expert Systems Publications Mature and Downgraded 1999-on

14. Expert Systems Dissertations Mature and Downgraded 1999-on It looks like it was pretty clear to Ph. D. students – or we got new Ph. D students.

15. Expert System AAA Presentations Mature and Downgraded 1999-on

16. Research Methodologies Vary by Discipline Design science – build and test artifacts – In computer science this is the dominant methodology. – Used to be the key AIS methodology. Behavioral – How does system use impact behavior? – In information systems this is a dominant methodology – Increasingly important in AIS. Archival – e.g., Event studies – In accounting, economics and finance, archival financial is the dominant methodology. – Gaining substantial traction in AIS and some IS. Analytic/Mathematical Models – Useful in computer science and operations research, but seldom in accounting

18. Technologies Go Through a Life Cycle

19. Gartner Hype Cycle (and others) “Imagine” AIS on a hype curve starting in 1970s or so It is arguable that it is a natural progression for AIS researchers to move through the life cycle. Maybe now AIS is on the Plateau of Productivity … at least with many technologies.

20. “Gartner's hype cycle and information system research issues” IJAIS 2009 Retro-Fit “Expert Systems” to Hype Cycle 1988 1995

21. Technologies get embedded in other technologies Rarely see “pure” expert systems any more. Expert systems and AI have been embedded with other technologies. – Decision Support Systems – Conventional Systems – Statistics – Drill down …

22. There has long been an emphasis on integrating AI into DSS and other systems HICSS 1992

23. Intelligent Systems in Accounting, Finance and Management 1992

24. Are you familiar with SAS’s JMP? Neural Nets are part of Statistics

25. Summary – Some Discussion Points AI and expert systems go through life cycles – There are many different AI technologies. – The way that most built Expert Systems had fundamental flaws that are not easily fixed. – We are forgetting those problems and limitations AI is being integrated with many other technologies The type of research that can be done depends on where the technology is in the life cycle – AIS (IS) researchers seem to be opting out of design science research for various reasons. – We can imagine AIS on a hype curve … and AIS has progressed along the curve.

26. Questions?