Law,liability and expert systems

This paper examines some of the possible legal implications of the production, marketing and use of expert systems. The relevance of a legally useful definition of expert systems, comprising systems designed for use both by laymen and professionals, is related to the distinctions inherent in the legal doctrine underlying provision of goods and provision of services. The liability of the sellers and users of, and contributors to, expert systems are examined in terms of professional malpractice as well as product liability. A recurring theme indicates that legislators may be inclined to restrict possibilities of liability suits in order to avoid disincentives to the creation of expert systems.     

Institutionalizing expert systems: Guidelines and legal concerns

Often, knowledge engineers become so involved in the development process of the expert system that they fail to look further down the road toward the expert system's institutionalization within the organization. Institutionalization is an important component of the expert system planning process. More specifically, the legal issues associated with expert systems development and deployment are critical institutionalization factors. This paper looks at some expert system institutionalization guidelines, and then focuses on legal considerations. Adapted from Zeide, J. S. and J. Liebowitz, “Institutionalizing Expert Systems: Guidelines and Legal Concerns,” Proceedings of FLAIRS-92 Conference, Ft. Lauderdale, FL.     

Expert system problem selection: A domain characteristics approach

The first generation of commercial expert systems based on AI technology are now available in the market place. But in the available literature, one can find hardly any material on expert system problem selection. In this paper a number of popular and successful expert systems are analyzed. Domain-dependent and domain-independent problem characteristics have been identified, based on the analysis. To test our contention that these characteristics significantly contribute to the success of expert systems, a questionnaire survey involving a number of expert system developers was conducted. Based on this, a domain characteristic approach for expert system problem selection is presented.     

Professional negligence and financial‐legal expert systems: Architectures to enable the reasonableness defence

The legal implications of professionals relying on expert systems have been extensively studied by analysts who have considered the ‘macro’ issues such as whether products liability can be invited for loss resulting from errors in expert systems, and apportionment of liability. These macro‐analyses do not distinguish programming errors from what we call ‘errors of reasoning’. The latter errors may be considered to be advertent on the part of developers and/or user‐professionals; may potentially be directly compared with the reasoning ascribable to a reasonably competent professional; and cannot be removed merely by better quality assurance in the sense prevalent in software engineering. In this paper we consider the mechanisms by which a court may examine errors of reasoning in expert systems used to assist professionals working in certain areas of financial services ('the financial‐legal domain'). Our analysis of the ‘micro’ issues of liability shows that the advertent nature of errors of reasoning makes it difficult to argue that using the best possible quality assurance methods during development is sufficient to make the reliance on an expert system reasonable. However, certain valuable defences that are available to professionals acting unaided can be made out even when expert systems are employed as part of the reasoning process, though these defences will be disabled if certain conceptual structures of the knowledge of professionals are not distinctively maintained in the knowledge base. As such, opacity of a knowledge base assists, rather than hinders, a plaintiff in establishing professional negligence. The results of an analysis of the micro‐issues of liability have been applied by us in the design of a system for financial/tax planning. A case‐based reasoning approach is employed, deductive rules being employed strictly in a subsidiary role. Consequently, the primary justification for a decision is a ‘case’ ‐ an instance of problem‐solving by a professional ‐ that is likely to be accepted by the court as being representative of the actions of reasonably competent professionals.     

Brussels revisited: The European conflicts guide

In this paper, we examine the soundness of Capper & Susskind's recommended legal expert system development methodology in the areas of knowledge acquisition and knowledge representation. Legal expert systems have not yet had the impact in the United Kingdom that might be expected. We argue that this is partly the result of developers paying insufficient attention to the ‘third estate’: user interfaces. We make suggestions about both the look and feel of legal expert systems, and the facilities that such systems should offer. Lastly, we claim that we have developed an exploratory expert system encapsulating the Brussels Convention 1968 which can contribute to the development of a useful computer‐based guide to an important legal domain.     

Some legal aspects of expert systems

As expert systems become more popular, the possibility of lawsuits arising from their use becomes increasingly evident. The expert system developer needs to be aware of important legal concepts associated with an expert system's development and deployment. This paper will primarily explore the rights to intellectual property associated with an expert system and liability for malfunction of an expert system.     

Knowledge acquisition for expert systems in accounting and financial problem domains

Since the mid-1980s, expert systems have been developed for a variety of problems in accounting and finance. The most commonly cited problems in developing these systems are the unavailability of the experts and knowledge engineers and difficulties with the rule extraction process. Within the field of artificial intelligence, this has been called the ‘knowledge acquisition’ (KA) problem and has been identified as a major bottleneck in the expert system development process. Recent empirical research reveals that certain KA techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. This paper presents a mapping between these empirical studies and a generic taxonomy of expert system problem domains. To accomplish this, we first examine the range of problem domains and suggest a mapping of accounting and finance tasks to a generic problem domain taxonomy. We then identify and describe the most prominent KA techniques employed in developing expert systems in accounting and finance. After examining and summarizing the existing empirical KA work, we conclude by showing how the empirical KA research in the various problem domains can be used to provide guidance to developers of expert systems in the fields of accounting and finance.     

Expert systems for legal decision making

Abstract: AI technology is being used to develop expert systems that solve complex problems in the legal area. Most of these systems employ rules to describe the strategies and procedures used by litigators to analyze legal issues. The tasks performed by these systems include interpreting the law, anticipating the legal consequences of proposed actions, predicting the effects of changes in legislation, as well as analyzing and managing cases. The special characteristics of the legal domain cause certain problems for expert system builders. We discuss some of these problems and describe LDS and SAL, two expert systems we have developed for case evaluation and settlement in the product liability area. SAL (System for Asbestos Litigation) evolved from the earlier and more general expert system, LDS (Legal Decisionmaking System). We also describe XPL, an explanation facility we developed for use by SAL and other expert system applications.     

Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development

Principled development techniques could greatly enhance the understandability of expert systems for both users and system developers. Current systems have limited explanatory capabilities and present maintenance problems because of a failure to explicitly represent the knowledge and reasoning that went into their design. This paper describes a paradigm for constructing expert systems which attempts to identify that tacit knowledge, provide means for capturing it in the knowledge bases of expert systems, and, apply it towards more perspicuous machine-generated explanations and more consistent and maintainable system organization.     

Pragmatism and purism in artificial intelligence and legal reasoning

The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. By reference to The Latent Damage System, an operational system, the paper articulates and responds to conceivable purist (jurisprudential and AI) objections to such a program. The methods of the pragmatist are also called into question and refined. The author concludes that pragmatism within a purist framework is the only sound approach to developing reliable AI systems in law.     

Expert systems and economic policy analysis: An application to OCS auction leasing

In this paper we argue that expert systems can be powerful tools for modelling microeconomic systems, including both individual decision making and the coordination of individual agents in a resource allocation mechanism. Using the fact that expert systems are essentially computerized versions of decision processes, we illustrate how they can be viewed as generalized process models of decision-making. We argue that the expert system approach is beneficial because it allows a policy analyst to explore the implication of policy alternatives without having to incur the generally prohibitive cost of field implementation studies. Further, enables the incorporation and updating of decision strategies and qualitative information, which human experts typically use but which is not amenable to pure mathematical modelling.One particular microeconomic system we suggest could be modelled as an expert system is the OCS offshore oil lease auction process. Moreover, we argue that constructing such an expert system model would require the development of two integrated expert systems: one for the auction process and subsequent resource allocation and the other to model the individual bidding behavior of the auction participants. We set out the structure of the auction expert system in some detail and discuss rules of thumb used by bidders inferred from our empirical research on past OCS auctions.Such an expert system of an auction leasing process could provide benefits to both bidders (e.g., oil companies) and the auctioneer (e.g., the Department of the Interior) as well. Bidders, by trying different strategies against different hypothesized strategies by their opponents could use such an integrated expert system to improve their bidding performances. The auctioneer, on the other hand, could test the efficiency of various proposed auction institutions under different assumptions about bidding behavior. In some circumstances, it might be desirable to even automate the auction process with a network coordinating the expert systems used by the individual firms and a computerized auctioneer.     

专家系统与Web技术集成的研究

专家系统和Web技术是两项重要技术。将专家系统与Web技术集成,开发基于Internet的专家系统,可以克服传统专家系统的一些缺陷。文章简要介绍了传统专家系统所存在的问题以及Web技术对专家系统提供的支持,在此基础上提出了三种集成Web技术的专家系统结构模型,并介绍了每种结构所采用的关键技术。     

Expert system technology transfer strategies: Selected cases from the United States and Mexico

Expert systems are being developed in a multitude of domains worldwide. The usage of expert systems within organizations is growing; however, many expert systems projects still fail due to poor ‘institutionalization’ practices. This paper addresses various strategies for providing the transfer of expert systems technology within organizations. Specifically, this paper will address expert system technology transfer strategies using examples from United States and Mexican organizations.     

Decision making,computer attitudes and expert systems: What is our direction?

Expert systems have been concerned with applications dealing with medical diagnosis, mineral exploration, and computer configuration, with some efforts relatively successful in achieving results at least as good as human experts. Today, much is being written about these systems and managerial decision-making activities in organizations and the positive impact that they can have in these situations. However, it appears that expert systems could become somewhat of a panacea for some organizational ailments as research, development, and marketing of them continues at a fast pace. What may be forgotten in this technological thrust is the individual decision maker and his/her unique style of decision making which could affect acceptance and use of these systems. Another important consideration is the attitude people have toward computers and computer systems, which along with decision-making styles could certainly affect expert system effectiveness and ultimate success in organizations. This paper provides a discussion of potential problems that could occur regarding individual decision making and attitudes and their relationships to these systems.     

EXPERT SYSTEMS FOR BUSINESS APPLICATIONS

Abstract

Expert systems are becoming more prevalent in financial and business applications. With venture capital pouring into the expert systems area, there is greater interest in expert systems by commercial firms. Companies are either building in-house expert systems capabilities or contracting out to expert system developers to construct expert systems for business applications. This paper presents much of the work being done in developing expert systems in business and discusses fertile areas for constructing more business expert systems.     

State-of-the-practice in knowledge-based system verification and validation

The majority of the work in knowledge-based system verification and validation (KBS V&V) has focused on developing techniques and concepts for performing V&V on expert systems. Little information is available on what V&V practices are currently in use by expert system developers. This article describes the results of a survey whose purpose was to begin documenting some of the experiences and problems KBS developers have encountered. The results of the survey suggest that current practices can be improved and some specific recommendations are included. The recommendations include developing requirements for expert system V&V, addressing common issues, recommending a life cycle for expert systems development, addressing readability and modularity issues, and investigating applicability of analysis tools.     

Building a financial diagnosis system based on fuzzy logic production system

The purpose of this study is to build a financial expert system based on fuzzy theory and Fuzzy LOgic Production System (FLOPS), which is an expert tool for processing the ambiguity. The study consists if four parts. For the first part, the basic features of expert systems are presented. For the second part, fizzy concepts and the evaluation of classical expert systems to fuzzy expert systems will be presented. For the third part, the expert system shell (FLOPS) used in this study will be described. For the last part, it will be presented the financial diagnosis system, developed by using the Wall's seven ratios, traditional seven ratios and also 34 ratios selected by a financial expert. After analyzing and investigating these three kinds of methods, financial diagnosis system will be developed as a fuzzy expen system which used a membership function based on averages and standard deviation. At the last step, the new approach will be tried by increasing the fuzzy sets for five membership functions. Some practical examples will be given. Throughout the paper, the way of building a financial diagnosis system based on fuzzy expert system is stressed.     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.     

Extraction and representation of facts from legal briefs

In knowledge-based consultation systems, the quality of the advice rendered depends on the techniques employed to represent the domain knowledge, the explanation generating capabilities, and the control strategies utilized during the consultative advice stage. The ability to understand the problem is more crucial in providing effective consultation. In this work, the emphasis is on understanding and the consequent formulation of a plausible internal representation of legal briefs. The system developed, SIFTER, reads the given input text from a legal practitioner's point of view and retrieves from it those facts that are relevant to the particular type of case on hand. In other words, it uses the domain specific knowledge to identify the type of case and to yank out the necessary information pertaining to the case. The SIFTER generates a noun-phrase processed form of the input which contains pseudo names for the proper-nouns, dates and time-intervals. The verbs in the processed input are used to check whether the case specific events have occurred or not and then the appropriate fact-containing noun-phrases are used to instantiate the relevant legal variables and, hence, to construct an internal representation of the given problem which can then be readily used by the consultative advice stage of a problem solver or analyzer. The implementation has been done in LISP culling the required domain knowledge from the Industrial Dispute Act of India.     

Involving users in expert system development

Abstract: The ultimate criterion of success for interactive expert systems is that they will be used, and used to effect, by individuals other than the system developers. A key ingredient of success in most systems is involving users in the specification and development of systems as they are being built. However, until recently, system designers have paid little attention to ascertaining user needs and to developing systems with corresponding functionality and appropriate interfaces to match those requirements. Although the situation is beginning to change, many developers do not know how to go about involving users, or else tackle the problem in an inadequate way. This paper discusses the need for user involvement and considers why many developers are still not involving users in an optimal way. It looks at the different ways in which users can be involved in the development process and describes how to select appropriate techniques and methods for studying users. Finally, it discusses some of the problems inherent in involving users in expert system development, and recommends an approach which incorporates both ethnographic analysis and formal user testing.