AI theory and its technology is rarely consulted in attempted resolutions of social problems. Solutions often require that decision-analytic techniques be combined with expert systems. The emerging literature on combined systems is directed at domains where the prediction of human behavior is not required. A foundational shift in AI presuppositions to intelligent agents working in collaboration provides an opportunity to explore efforts to improve the performance of social institutions that depend on accurate prediction of human behavior. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both.

We describe how computational POL modeling integrates diverse artificial intelligence research areas and provides interesting challenges in multiple fields. Simultaneously, these patterns of life impose structure on individual decisions. For example, a pattern of rush hour traffic arises from drivers' decisions to commute at a certain time. Knowledge of rush hour influences individuals' departure times. Modeling POL is not only an academic pursuit.

The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of fraud detection, computer intrusion detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques. There were over 50 attendees, with a balanced mix of university and industry researchers. The organizing committee consisted of Tom Fawcett and Foster Provost of Bell Atlantic Science and Technology, Ira Haimowitz of General Electric Corporate Research and Development, and Salvatore Stolfo of Columbia University. The purpose of the workshop was to gather researchers and practitioners working in the areas of risk management, fraud detection, and computer intrusion detection.

However, most of these researches and systems attain state-of-the-art performance only when trained with large amounts of data. With GDPR and other data regulatory frameworks coming into play, 2018 is expected to witness machine learning systems which can learn efficiently maintaining performance, but in less time and with less data. A data-efficient learning system allows learning in complex domains without requiring large quantities of data. For this, there would be developments in the field of semi-supervised learning techniques, where we can use generative models to better guide the training of discriminative models. More research would happen in the area of transfer learning (reuse generalize knowledge across domains), active learning, one-shot learning, Bayesian optimization as well as other non-parametric methods.

Default and auto-epistemic reasoning were also well represented, with a number of papers discussing aspects, applications, and implementations of default reasoning systerns. Several papers emphasized nonmonotonic facets of computational vision, natural language understanding, and conimo1i-sense reasoning. Thursday evening, a panel discussion was held, with John McCarthy, Dana Scott, and Richmond Thomason as panelists. Compare it with a merely COMMON LISP (Golden Common Lisp@ Version 1.OO): Golden Common Lisp is a registered trademark of Gold Hill Computers. Our low-key, dignified approach to matchingquality candidates with quality companies will offer you the opportunity to examine your alternatives in a confidential, systematic fashion Openingsarenationwide.

This article describes a theory of reasoning about uncertainty, baaed on a representation of states of certainty called endorsements The theory of endorsements is an alternative to numerical methods for reasoning about uncertainty, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda, Hart, and Nilsson, 1976) and the Shafer-Dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning that produces them and thus limit one's reasoning about uncertainty While numbers are easy to propagate over inferences, what the numbers mean is unclear The theory of endorsements provides a richer representation of the factors that affect certainty and supports multiple strategies for dealing with uncertainty. People's certainty of the past is limited by the fidelity of the devices that record it, their knowledge of the present is always incomplete, and their knowledge of the future is but speculation. Even though nothing is certain, people behave as if almost nothing is uncertain. They are adept at discounting uncertainty - making it go away.

A NONPROFIT CORPORATION ARTICLE I. NAME The name of this corporation shall be the American Association for Artificial Intelligence (AAAI). PURPOSE This corporation is a nonprofit public benefit corporation and is not organized for the private gain of any person. It is organized under the California Nonprofit Corporation Law for scientific and educational purposes in the field of artificial intelligence. Notwithstanding any other provision of these articles, the corporation shall not carry on any other activities not permitted to be carried on: (i) by a corporation exempt from Federal Income Tax under Section 501 (c)(3) of the Internal Revenue Code or (ii) by a corporation, contributions to which are deductible under Section 170 (c)(2) of the Internal Revenue Code. DEDICATION AND DISSOLUTION The property of this corporation is irrevocably dedicated to educational and scientific purposes, and no part of the net income or assets of this organization shall ever inure to the benefit of any councilor, officer, or member thereof or to the benefit of any private persons.

The AAAI Executive Council Meeting was held August 12, 1986 in Salon 10, Franklin Plaza Hotel, Philadelphia, Pennsylvania, from 6.30 -10:00 p.m Attendees. Tom Mitchell, Eugune Charniak, Richard Fikes, Ron Brachman, William Woods, Danny Bobrow, Mark Stefik, Ted Shorliffe, Pat Winston [President), Woody Bledsoe (Past-President), Barbara Grosz, Lyn Conway, Rszard Michalski, Doug Lenat, Marty Tenenbaum, Lee Erman, Marvin Minsky, Raj Reddy (President-elect), Peter Patel-Schneider Financial Status of the Association As of the end of July 31st, the AAAI had current assets of over $2SM in the bank Standing Committee Reports Conference Committee (Marty Tenenbaum reporting]: Apparently the technical program split is working and this year's conference seems to be satisfactory to many people. Publication Committee (Lee Erman reporting): If the magazine gets its second class permit, then next year's magazine expenses will be reduced. Committee recommended that we find an associate editor for Bob Engelmore They also recommended that we forge more cooperative relationships with IEEE and the ACM. Because of the tremendous production pressure to create a fifth "conference" issue, it will be eliminated and replaced with an advertising suppliment It was recommended to replace the Book Editor.

Both fields use formal methods, with their strengths and limitations; in AI there are software, logic, and statistics, in law there are statutes, procedures, and institutions. Both fields are creative; in AI, systems are built, experiments designed, and paradigms replaced; in law, regulations are passed by lawmakers, precedents are set, and ideologies balanced. Both fields struggle with the inevitable complexity of modeling human behavior -- in AI with the goal to reconstruct human behavior, in law with the goal to steer human behavior. These and other similarities are driving the active and dedicated community of AI and law. Researchers are taking their inspiration from the law with its insights developed over millennia combining them with AI's half a century of lessons.

Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconscious biases about membership in particular groups can have an effect on their decision-making, even when they have no deliberate intention to discriminate against members of these groups. A growing body of experimental work has pointed to the effect that implicit bias can have in producing adverse outcomes. Here we propose a theoretical model for studying the effects of implicit bias on selection decisions, and a way of analyzing possible procedural remedies for implicit bias within this model. A canonical situation represented by our model is a hiring setting: a recruiting committee is trying to choose a set of finalists to interview among the applicants for a job, evaluating these applicants based on their future potential, but their estimates of potential are skewed by implicit bias against members of one group. In this model, we show that measures such as the Rooney Rule, a requirement that at least one of the finalists be chosen from the affected group, can not only improve the representation of this affected group, but also lead to higher payoffs in absolute terms for the organization performing the recruiting. However, identifying the conditions under which such measures can lead to improved payoffs involves subtle trade-offs between the extent of the bias and the underlying distribution of applicant characteristics, leading to novel theoretical questions about order statistics in the presence of probabilistic side information.