Explanation in Information Systems

Developing explanations of observed phenomenon is one of the major functions of research in Information Systems (IS). But what is an explanation? What types of explanation can IS research provide and what do they mean? The objectives of this research are to develop a shared language, to increase understanding of the meaning of research results and to stimulate discussion of explanation in Information Systems research. Four years of articles published in two top-ranked IS journals over a period of ten years were sampled based on four explanation types defined in modern philosophy: covering-law, statistical-relevance, pragmatic and functional. Explanation types, sub classifications ontologies and research methods were classified and the relationships between these characteristics were examined. Results reveal opportunities for studying Information Systems beyond a single explanation, towards the use of a rich set of explanation types to fully describe phenomena

PERENCANAAN DAN PENGEMBANGAN SISTEM INFORMASI SEKTOR PUBLIK BERBASIS KOMPUTER

Accounting information systems provide information for both internal and external users. Public sectors adopted internal control procedures in accounting systems to ensure accurate and reliabilty of financial report. One of internal. control components is information that is tools for decision making. Nowadays, information technology facilitates internal control procedures. Both information and information technology support sector public activities more easily, more reliable and more accurate. This article provides explanation about how to use information system in public sector with information technology support. Furthermore, the explanation will be focused on planning and developing information ' systems in public sector based on computer system

Explanations of Information Systems: Can Philosophy Help?

Accounts of explanation given in the information systems literature, most prevalent in the areas knowledge-based systems and human-computer interaction, generally do not consider the philosophical underpinnings of explanation concepts. The theoretically rich accounts that have emerged from the philosophy of science provide us with a well-developed framework for designing the structure and content of explanations to be provided for information systems in documentation, in help systems, and in embedded explanation facilities when these are provided. The work described in this paper is an attempt to draw some of these philosophical ideas into the realm of information systems by briefly reviewing four of the major models of explanation from the philosophy of science literature: deductive-nomological explanation, functional explanation, rational choice explanation, and pragmatic explanation. Elements are drawn from each of these models and are related to their potential utility in the information systems field. The approach to philosophy of science’s contributions to information systems explanation is from the perspective of information systems research and practice, not philosophy, in an attempt to ‘pull’, not ‘push’, these ideas into the realm of information systems development and use

Communications and tracking expert systems study

The original objectives of the study consisted of five broad areas of investigation: criteria and issues for explanation of communication and tracking system anomaly detection, isolation, and recovery; data storage simplification issues for fault detection expert systems; data selection procedures for decision tree pruning and optimization to enhance the abstraction of pertinent information for clear explanation; criteria for establishing levels of explanation suited to needs; and analysis of expert system interaction and modularization. Progress was made in all areas, but to a lesser extent in the criteria for establishing levels of explanation suited to needs. Among the types of expert systems studied were those related to anomaly or fault detection, isolation, and recovery

Explanation and trust: what to tell the user in security and AI?

There is a common problem in artificial intelligence (AI) and information security. In AI, an expert system needs to be able to justify and explain a decision to the user. In information security, experts need to be able to explain to the public why a system is secure. In both cases, the goal of explanation is to acquire or maintain the users' trust. In this paper, we investigate the relation between explanation and trust in the context of computing science. This analysis draws on literature study and concept analysis, using elements from system theory as well as actor-network theory. We apply the conceptual framework to both AI and information security, and show the benefit of the framework for both fields by means of examples. The main focus is on expert systems (AI) and electronic voting systems (security). Finally, we discuss consequences of our analysis for ethics in terms of (un)informed consent and dissent, and the associated division of responsibilities

Evidence based healthcare planning in developing countries: An Informatics perspective

Most of the national Health Information Systems (HIS) in resource limited developing countries do not serve the purpose of management support and thus the service is adversely affected. While emphasising the importance of timely and accurate health information in decision making in healthcare planning, this paper explains that Health Management Information System Failure is commonly seen in developing countries as well as the developed countries. It is suggested that the possibility of applying principles of Health Informatics and the technology of Decision Support Systems should be seriously considered to improve the situation. A brief scientific explanation of the evolution of these two disciplines is included

Signature of effective mass in crackling noise asymmetry

Crackling noise is a common feature in many dynamic systems [1-9], the most familiar instance of which is the sound made by a sheet of paper when crumpled into a ball. Although seemingly random, this noise contains fundamental information about the properties of the system in which it occurs. One potential source of such information lies in the asymmetric shape of noise pulses emitted by a diverse range of noisy systems [8-12], but the cause of this asymmetry has lacked explanation [1]. Here we show that the leftward asymmetry observed in the Barkhausen effect [2] - the noise generated by the jerky motion of domain walls as they interact with impurities in a soft magnet - is a direct consequence of a magnetic domain wall's negative effective mass. As well as providing a means of determining domain wall effective mass from a magnet's Barkhausen noise our work suggests an inertial explanation for the origin of avalanche asymmetries in crackling noise phenomena more generally.Comment: 13 pages, 4 figures, to appear in Nature Physic

Explanation production by expert planners

Although the explanation capability of expert systems is usually listed as one of the distinguishing characteristics of these systems, the explanation facilities of most existing systems are quite primitive. Computer generated explanations are typically produced from canned text or by direct translation of the knowledge structures. Explanations produced in this manner bear little resemblance to those produced by humans for similar tasks. The focus of our research in explanation is the production of justifications for decisions by expert planning systems. An analysis of justifications written by people for planning tasks has been taken as the starting point. The purpose of this analysis is two-fold. First, analysis of the information content of the justifications will provide a basis for deciding what knowledge must be represented if human-like justifications are to be produced. Second, an analysis of the textual organization of the justifications will be used in the development of a mechanism for selecting and organizing the knowledge to be included in a computer-produced explanation. This paper describes a preliminary analysis done of justifications written by people for a planning task. It is clear that these justifications differ significantly from those that would be produced by an expert system by tracing the firing of production rules. The results from the text analysis have been used to develop an augmented phrase structured grammar (APSG) describing the organization of the justifications. The grammar was designed to provide a computationally feasible method for determining textual organization that will allow the necessary information to be communicated in a cohesive manner