This is Part 2 of a two-part article and discusses issues of tort liability and the use of computers in the courtroom. Part 1 of this article, which appeared in the Spring 1988 issue of AI Magazine, discussed steps that developers of AI systems can take to protect their efforts, and the attendant legal ambiguities that must eventually be addressed in order to clarify the scope of such protection. Part 2 explores the prospect of AI systems as subjects of litigation. Once inside the courtroom, what role can the computer assume in its own defense or in the service of some other litigant? The law of evidence, developed to govern the testimony of human witnesses, must continually evolve to accommodate new, nonhuman sources of information.

This is Part 1 of a two-part article. Part 2 covers tort liability and computers as expert witnesses. It will appear in the Summer 1988 issue of AI Magazine. Technological developments that remove ever-increasing numbers of cognitive tasks from human control will alter the assumptions on which current legal rules are based. These rules will have a growing impact on AI researchers and entrepreneurs as their work reaches a growing audience of beneficiaries. In order to accommodate the needs of practitioners and their recipients, courts and lawmakers will be forced to reevaluate principles whose foundations were developed well before the implications of advanced technology could have been predicted. This article attempts to identify areas of law in which the need for accommodation will be greatest and provide some insight into the process and the direction of change. All software programs, regardless of purpose or complexity, exhibit a common vulnerability: They are expensive to create ...

One of the major problems faced by businesses in the 1990s is how to produce environmentally friendly products and stay profitable. A pioneering consortium at Carnegie Mellon University (CMU) is using AI, combined with operations research, environmental science, public policy, and other disciplines, to build tools for green engineering. Green engineering is an approach to product development that balances environmental compatibility against economic profitability. It looks at the entire life cycle of the product, from design to disposal, and seeks to extend this life cycle through remanufacturing, reusing, and recycling products and components. Today, industrial solutions to environmental problems focus largely on recycling, figuring out how to dispose of products at the end of their useful lives.

A key data source available to FINCEN is reports of large cash transactions made to the Treasury according to terms of the Bank Secrecy Act. FAIS's unique analytic power arises primarily The most common motivation for criminal behavior is profit. The larger the criminal organization is, the greater the profit. By disrupting the ability to profit, law enforcement can focus on a vulnerable aspect of large criminal organizations. Money laundering is a complex process of placing the profit, usually cash, from illicit activity into the legitimate financial system, with the intent of obscuring the source, ownership, or use of the funds.

The 15th International Conference on AI and Law (ICAIL 2015) was held in San Diego, California, USA, June 8-12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery). The conference has been held every two years since 1987, alternating between North America and (usually) Europe. The program for ICAIL 2015 included three days of plenary sessions and two days of workshops, tutorials, and related events. Attendance reached a total of 179 participants from 23 countries. Of the total, 95 were registered for the full conference and 84 for one or two days. The work reported at the ICAIL conferences has always had two thrusts: using law as a rich domain for AI research, and using AI techniques to develop legal applications. That duality continued this year, with an increased emphasis on the applications side. Workshop topics included (1) discovery of electronically stored information, (2) law and big data, (3) automated semantic analysis of legal texts, and (4) evidence in the law. There were also two sessions for which attorneys could obtain Continuing Legal Education credit, one on AI techniques for intellectual property analytics and the other on trends in legal search and software. The program also contained events intended to reach out to a variety of communities and audiences.

Implementation of the case-based reasoner in rules and objects using a commercial knowledge-based system shell is described. Although some refinements remain, the performance of the case-based reasoner has met its design goals. The chemical industry is heavily regulated. Every hazardous chemical product must have a set of shipping descriptions that conform to strict regulations issued by the Department of Transportation (DOT), the International Maritime Organization (IMO), and the International Air Transport Association (IATA). Shipping descriptions provide a concise characterization of the hazards a chemical can present during transportation (figure 1). Failing to comply with transport regulations can result in penalties ranging from delayed shipments to heavy fines or even incarceration of corporate officials. In addition, each chemical product has a material safety data sheet (MSDS) that conforms to Occupational Safety and Health (OSHA) and American National Standards Institute (ANSI) standards. Unlike shipping descriptions, MSDSs are lengthy documents of 8 to 10 pages that provide a detailed description of the health hazards a product can pose in the workplace (figure 2). They also contain information on procedures for storing, handling, and disposing of a chemical. Inadequately prepared MSDSs can lead to substantial product-liability lawsuits against the company if the product is involved in an industrial accident. The ultimate goal of these regulations is to ensure proper communication of health and safety information for the protection of the public. Air Products is committed to the initiative of the Chemical Manufacturers Association (CMA) known as Responsible Care. This initiative focuses on the safe manufacturing, distribution, use, recycling, and disposal of chemicals. Proper communication through accurate shipping descriptions and full disclosure of hazard information in the MSDS plays a key role in fulfilling obligations under Responsible Care. Maintaining shipping descriptions and MSDSs requires a major effort. Most corporate systems are intensely manual.

"I have but one lamp by which my feet are guided, and that is the lamp of experience. I know no way of judging of the future but by the past." AI researchers seek to understand the nature of intelligence and human thought. They examine a range of human cognitive behavior, including memory, learning, planning, and problem solving and look for principles that play general descriptive and explanatory roles. The second agenda for AI research is technological.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition As an alternative analysis: this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend COMPUTERS INCREASINGLY RESEMBLE Computers have proven capable of far more physical and mental "human" functions than most people believed was possible. The increasing similarity between humans and machines might eventually require legal recognition of computers as "persons." In the United States, there are two triers t'o such Views expressed here are those of the author @ Llarshal S. Willick 1982 41 rights reserved Editor's Note: This article is written by an attorney using a common reference style for legal citations The system of citation is more complex than systems ordinarily used in scientific publications since it must provide numerous variations for different sources of evidence and jurisdictions We have decided not to change t.his article's format for citations. The first tier determines which ascertainable individuals are considered persons (e g., blacks, yes; fetuses, no.) The second tier determines which rights and obligations are vested in the recognized persons, based on their observed or presumed capacities (e.g., the insane are restricted; eighteen-year-olds can vote.)

Research in the area of expert systems has developed from our experience in building consultation programs in a number of application domains (Weiss, Kulikowski, and Safir, 1978; Lindberg et al., 1980; Kulikowski, Weiss, and Galen, 1981; Kulikowski, 1980). The EXPERT system (Weiss and Kulikowski, 1979) is a generalized scheme for building expert reasoning models, exercising them with individual problems, testing and analyzing their performance on large numbers of problem-types, and improving them by knowledge base refinement techniques. The system has been operational on DEC lo/20 computers since 1978; versions also exist on VAX and IBM computers This system has been used by specialists in medicine, biomedical modeling, oil exploration, and chemistry to build models that capture their expertise in problem solving. In 1981 we complet,ed an interesting technology transfer experiment in which a model for the interpretation of serum protein electrophoresis patterns was automatically translated from its EXPERT representation into algorithmic form, and then automatically translated into assembler code for running on a microprocessor (Weiss, KuIikowski, and Galen, 1981). The EXPERT system is unusual among knowledge-based AI systems in that efficiency is a major design goal.

Artificial intelligence (AI) will have many profound societal effects It promises potential benefits (and may also pose risks) in education, defense, business, law, and science In this article we explore how AI is likely to affect employment and the distribution of income. I am grateful for the helpful comments provided by many people Specifically I would like to acknowledge the advice teceived from Sandra Cook and Victor Walling of SRI, Wassily Leontief and Faye Duchin of the New York University Institute for Economic Analysis, Margaret Boden of The University of Sussex, Henry Levin and Charles Holloway of Stanford University, James Albus of the National Bureau of Standards, and Peter Hart of Syntelligence Herbert Simon, of Carnegie-Mellon Univetsity, wrote me extensive criticisms and rebuttals of my arguments Robert Solow of MIT was quite skeptical of my premises, but conceded nevertheless that my conclusions could possibly follow from them if certain other economic conditions were satisfied. There are two opposing views in response to this question Some claim that AI is not really very different from other technologies that have supported automation and increased productivity-technologies such as mechanical engineering, ele&onics, control engineering, and operations rcsearch. Like them, AI may also lead ultimately to an expanding economy with a concomitant expansion of employment opportunities. At worst, according to this view, thcrc will be some, perhaps even substantial shifts in the types of jobs, but certainly no overall reduction in the total number of jobs.