Author name cluster

John Kingston

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AILAW Journal 2017 Journal Article

Using artificial intelligence to support compliance with the general data protection regulation

John Kingston

Abstract The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR—and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances.

Details DOI

AILAW Journal 2004 Journal Article

Towards a Financial Fraud Ontology: A Legal Modelling Approach

John Kingston
Burkhard Schafer
Wim Vandenberghe

Abstract This document discusses the status of research on detection and prevention of financial fraud undertaken as part of the IST European Commission funded FF POIROT (Financial Fraud Prevention Oriented Information Resources Using Ontology Technology) project. A first task has been the specification of the user requirements that define the functionality of the financial fraud ontology to be designed by the FF POIROT partners. It is claimed here that modeling fraudulent activity involves a mixture of law and facts as well as inferences about facts present, facts presumed or facts missing. The purpose of this paper is to explain this abstract model and to specify the set of user requirements.

Details DOI

AAAI Conference 1996 Conference Paper

CommonKADS Models for Knowledge-Based Planning

John Kingston

The CommonKADS methodology is a collection of structured methods for building knowledge-based systems. A key component of CommonKADS is the library of generic inference models which can be applied to tasks of specified types. These generic models can either be used as frameworks for knowledge acquisition, or to verify the completeness of models developed by analysis of the domain. However. the generic models for some task types, such as knowledge-based planning, are not well-developed. Since knowledgebased planning is an important commercial application of Artificial Intelligence, there is a clear need for the development of generic models for planning tasks. Many of the generic models which currently exist have been derived from modelling of existing AI systems. These models have the strength of proven applicability. There are a number of well-known and welltried AI planning systems in existence; one of the best known is the Open Planning Architecture (O-Plan). This paper describes the development of a CommonKADS generic inference model for knowledgebased planning tasks, based on the capabilities of the O-Plan system. The paper also describes the verification of this model in the context of a real-life planning task: the assignment and management of Royal Air Force Search and Rescue operat, ions.

PDF Details

KER Journal 1994 Journal Article

Artificial intelligence in business II: Development, integration and organizational issues

Daniel E. O'Leary
John Kingston

Abstract The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper provided a broad survey of the use of AI in business, summarizing the application of AI in a number of business domains. In addition, it also provided a summary of the use of different forms of knowledge representation in business applications. Part I has a large set of references, including a number of survey papers, focusing on AI in business. Part II of this paper consists of more detailed analysis of particular systems or issues affecting AI in business. It examines technical issues which are central to the construction of business AI systems, and it also examines the commercial contribution made by methods for the development of AI systems. In addition, part II looks at integration between AI and more traditional information systems. AI can be used to add value to many existing information systems, such as database management systems. Particular attention is given to the integration of AI with operations research, which is the one of the primary “competitors” of AI, providing an alternative set of support tools for decision making. Business organizations are not concerned only with technology issues; there is also concern about the impact of AI on organizations. Further, the evaluation of AI often is based on an economic view of the world. Part II therefore investigates the organizational impact of AI, and the economics of AI, including issues such as value creation. The format of Part II is as follows: Section 8 analyses techniques for improving the performance of AI systems, thus maximizing economic return. Section 9 looks at different forms of uncertainty and ambiguity which must be dealt with by AI systems. It examines the contributions of fuzzy logic and numerical measures of certainty to handling these problems. Section 10 examines the usefulness of different approaches to knowledge acquisition in business situations, and investigates the benefits of methodological approaches to AI applications. It also looks at more recent AI programming techniques which eliminate the need for knowledge elicitation from an expert: neural networks, case-based reasoning and genetic algorithms are discussed. Sections 11 and 12 examine issues of integrating AI systems. Generally, the use of AI in business settings must ultimately be integrated with the broader base of corporate information systems. Section 11 looks at integration with information systems in general, and section 12 looks particularly at integration with operations research. Sections 13 and 14 review the organizational and economic impact of AI. Finally, section 15 provides a brief summary of part II.

Details DOI