196,372 research outputs found

    Artificial Intelligence: Application Today and Implications Tomorrow

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    This paper analyzes the applications of artificial intelligence to the legal industry, specifically in the fields of legal research and contract drafting. First, it will look at the implications of artificial intelligence (A.I.) for the current practice of law. Second, it will delve into the future implications of A.I. on law firms and the possible regulatory challenges that come with A.I. The proliferation of A.I. in the legal sphere will give laymen (clients) access to the information and services traditionally provided exclusively by attorneys. With an increase in access to these services will come a change in the role that lawyers must play. A.I. is a tool that will increase access to cheaper and more efficient services, but non-lawyers lack the training to analyze and understand information it puts out. The role of lawyers will change to fill this role, namely utilizing these tools to create a better work product with greater efficiency for their clients

    Artificial Intelligence: Application Today and Implications Tomorrow

    Get PDF
    This paper analyzes the applications of artificial intelligence to the legal industry, specifically in the fields of legal research and contract drafting. First, it will look at the implications of artificial intelligence (A.I.) for the current practice of law. Second, it will delve into the future implications of A.I. on law firms and the possible regulatory challenges that come with A.I. The proliferation of A.I. in the legal sphere will give laymen (clients) access to the information and services traditionally provided exclusively by attorneys. With an increase in access to these services will come a change in the role that lawyers must play. A.I. is a tool that will increase access to cheaper and more efficient services, but non-lawyers lack the training to analyze and understand information it puts out. The role of lawyers will change to fill this role, namely utilizing these tools to create a better work product with greater efficiency for their clients

    Understanding safety-critical interactions with a home medical device through Distributed Cognition

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    As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients’ interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients’ interaction strategies, and pointed to design opportunities that could improve patients’ experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a DCog approach effectively helps to understand how safety is achieved or compromised in such a system

    The Digitalisation of African Agriculture Report 2018-2019

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    An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

    HILT : High-Level Thesaurus Project. Phase IV and Embedding Project Extension : Final Report

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    Ensuring that Higher Education (HE) and Further Education (FE) users of the JISC IE can find appropriate learning, research and information resources by subject search and browse in an environment where most national and institutional service providers - usually for very good local reasons - use different subject schemes to describe their resources is a major challenge facing the JISC domain (and, indeed, other domains beyond JISC). Encouraging the use of standard terminologies in some services (institutional repositories, for example) is a related challenge. Under the auspices of the HILT project, JISC has been investigating mechanisms to assist the community with this problem through a JISC Shared Infrastructure Service that would help optimise the value obtained from expenditure on content and services by facilitating subject-search-based resource sharing to benefit users in the learning and research communities. The project has been through a number of phases, with work from earlier phases reported, both in published work elsewhere, and in project reports (see the project website: http://hilt.cdlr.strath.ac.uk/). HILT Phase IV had two elements - the core project, whose focus was 'to research, investigate and develop pilot solutions for problems pertaining to cross-searching multi-subject scheme information environments, as well as providing a variety of other terminological searching aids', and a short extension to encompass the pilot embedding of routines to interact with HILT M2M services in the user interfaces of various information services serving the JISC community. Both elements contributed to the developments summarised in this report

    Improving fairness in machine learning systems: What do industry practitioners need?

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    The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019
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