Expert systems and ICAI in tax law: Killing two birds with one AI stone

Abstract

The author describes five separate projects he has undertaken in the intersection of computer science and Canadian income tax law. They are: 1. A computer-assisted instruction (CAI) course for teaching income tax, programmed using conventional CAI techniques; 2. A "document modeling" computer program for generating the documentation for a tax-based transaction and advising the lawyer-user as to what decisions should be made and what the tax effects will be, programmed in a conventional language ; 3. A prototype expert system for determining the income tax effects of transactions and tax-defined relationships, based on a PROLOG representation of the rules of the Income Tax Act; 4. An intelligent CAI (ICAI) system for generating infinite numbers of randomized quiz questions for students, computing the answers, and matching wrong answers to particular student errors, based on a PROLOG representation of the rules of the Income Tax Act; and 5. A HyperCard stack for providing information about income tax, enabling both education and practical research to follow the user's needs path. The author shows that non-AI approaches are a way to produce packages quickly and efficiently. Their primary disadvantage is the massive rewriting required when the tax law changes. AI approaches based on PROLOG, on the other hand, are harder to develop to a practical level but will be easier to audit and maintain. The relationship between expert systems and CAI is discussed.