1. Reasoning Methodologies in Information Technology R. Weber College of Information Science & Technology Drexel University

2. Reasoning Methodologies in IT R Weber Outline 1.Intelligent Jurisprudence Research 2.Proactive Dissemination of Lessons-learned

3. Reasoning Methodologies in IT R Weber 1. Intelligent Jurisprudence Research Why is current technology used for jurisprudence research propagating injustice? What technology is available to counteract this trend? What are the barriers?

4. Reasoning Methodologies in IT R Weber Current technology used are text databases Search for relevant precedents Common Law and Roman Law Judicial professionals: attorneys, judges Create a query using Boolean operators Read every document to assess their relevance

5. Reasoning Methodologies in IT R Weber What is wrong with text databases? Writing queries is difficult and imprecise Finding a set of words that are present in relevant documents and NOT present in non- relevant ones There is a limit to the number of documents that are returned to the user Matches are based on surface features: words, trigrams, etc. Natural language is ambiguous and open- textured Users need to go through entire documents to assess their relevance

6. Reasoning Methodologies in IT R Weber Literature on text databases 20% recall for 79% precision (Blair & Maron 1985) –100 rel. doc: 25 retrieved, 20 rel and 5 non-rel. Better recall can be achieved, but then precision falls (20% more in large databases) (id.). “It was found that in high recall searching it was not possible to achieve as high performance in the large databases as in the small one.” (Sormunen 2001) Most problems originating low quality are worse in larger databases.

7. Reasoning Methodologies in IT R Weber What technology is available to counteract this trend? How do humans search? Reasoning methodology that replicates this task How humans search for legal precedents? –Target problem in mind –Search for applicable solutions –How? –By assessing similarity between problems Which reasoning methodology can replicate this task?

8. Reasoning Methodologies in IT R Weber Case-Based Reasoning A reasoning methodology that mimics the similarity heuristic

9. Reasoning Methodologies in IT R Weber How can we use case-based reasoning for searching jurisprudence with high levels of precision and recall?

10. Reasoning Methodologies in IT R Weber Legal Cases tasks relations evidence attenuating circumstances

11. Reasoning Methodologies in IT R Weber Similarity Assessment Matching occurs when both documents have similar meaning and not simply same words or same surface features axe murder axe murder

12. Reasoning Methodologies in IT R Weber Similarity Assessment Matching occurs when both documents have similar meaning and not simply same words or same surface features axe robbery axe murder X

13. Reasoning Methodologies in IT R Weber Intelligent Jurisprudence Research The problem or situation motivating the search is represented in the method’s format (and becomes the target case) after a question-answer session Legal cases are retrieved based on how similar they are to the target case, i.e. legal cases from different areas of law can also be considered similar Users access the case representation of retrieved documents

14. Reasoning Methodologies in IT R Weber Barriers Current text databases ‘seem’ to work They represent an apparent substantial improvement over previous method Smaller databases can produce better results No one is suing anyone………yet Designing intelligent jurisprudence research requires manual engineering whereas (domain independent) text databases don’t

15. Reasoning Methodologies in IT R Weber Work on Technological Barriers Several researchers are seeking ways to reduce manual engineering requirements, maintaining high quality Information Extraction uses NLP (1991) Template Mining (1998) Machine learning (1999) Automated conversion into Graphs (2004)

16. Reasoning Methodologies in IT R Weber Further Applications in Law Common Law: –When legal cases are fully engineered, case-based reasoning systems can build complete argumentation structures (Ashley 1990) Roman Law: –Aiming at reducing the uncertainty between the law and its interpretation, create a judicial system based on a controlled reasoning structure that appends legal decisions to rule-based representations of law (Correa 2003)

17. Reasoning Methodologies in IT R Weber Questions? rosina.weber@drexel.edu

18. Reasoning Methodologies in IT R Weber 2. Proactive Dissemination of Lessons- Learned Knowledge management, experience, and learning curves Why is it so difficult to share knowledge? Why best-practices/lessons-learned repositories don’t work? What can be done?

19. Reasoning Methodologies in IT R Weber Knowledge Management Knowledge management refers to best allocation of intellectual assets Organizations become mature through experience and distribution of their knowledge, e.g. culture, doctrine Learning curves ascend as organization become mature but oscillate depending upon: –Changing technologies –Changing consumers, focus, etc. –When scope is broad, they will be always learning –Large number of members and large scope makes the worst combination for learning

20. Reasoning Methodologies in IT R Weber Why is it so difficult to share knowledge? It is difficult to learn It is difficult to communicate It is difficult to absorb

21. Reasoning Methodologies in IT R Weber It is difficult to learn Learning involves taking risks If one repeats exactly the same routine everyday one will hardly learn anything Learning originates from positive and negative experiences New experiences

22. Reasoning Methodologies in IT R Weber It is difficult to communicate Culture in some organizations motivate sharing, e.g. XEROX Communicate may imply admitting attempting something new, equals risk Communicating may decrease one’s competitive advantage Others may not be interested

23. Reasoning Methodologies in IT R Weber It is difficult to absorb Anthropologists explain that people only pay attention when knowledge is presented when and where it is needed Knowledge will be absorbed when it is needed, when it is applicable Knowledge will be absorbed where it is needed, in the context where it is needed, e.g. in within organizational context

24. Reasoning Methodologies in IT R Weber Why best-practices/lessons-learned repositories are not used? They are standalone –They are outside the context of where they are needed –They are not tied to the contexts when they are needed Because they require users to take the initiative to search them Because they may not believe they are useful They need to learn how to operate them They may be poorly composed, produce poor results

25. Reasoning Methodologies in IT R Weber What can be done? Integrate knowledge repositories to applicable tasks Definition of lesson-learned requires it positively impacts a process it targets Integration requires that tasks are identified in the body of lessons-learned Use an applicability oriented method to retrieve lessons-learned Guarantee that lessons-learned are ONLY retrieved when and where they are needed

26. Reasoning Methodologies in IT R Weber Requirements Users (or a knowledge worker) have to deliver their tasks using computerized environment where applicable tasks are identified Lessons-learned have to be collected in a manner that their requirements are all met, e.g. must produce positive impact

27. Reasoning Methodologies in IT R Weber Examples References Questions