AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin

Innovation and Employability in Knowledge Management Curriculum Design

During 2007/8, Southampton Solent University worked on a Leadership Foundation project focused on the utility of the multi-functional team approach as a vehicle to deliver innovation in strategic and operational terms in higher education (HE). The Task-Orientated Multi-Functional Team Approach (TOMFTA) project took two significant undertakings for Southampton Solent as key areas for investigation, one academic and one administrative in focus. The academic project was the development of an innovative and novel degree programme in knowledge management (KM). The new KM Honours degree programme is timely both in recognition of the increasing importance to organisations of knowledge as a commodity, and in its adoption of a distinctive structure and pedagogy. The methodology for the KM curriculum design brings together student-centred and market-driven approaches: positioning the programme for the interests of students and requirements of employers, rather than just the capabilities of staff; while looking at ways that courses can be delivered with more flexibility, e.g. accelerated and block-mode; with level-differentiated activities, common cross-year content and material that is multi-purpose for use in short courses. In order to permit context at multiple levels in common, a graduate skills strand is taught separately as part of the University’s business-facing education agenda. The KM portfolio offers a programme of practically-based courses integrating key themes in knowledge management, business, information distribution and development of the media. They develop problem-solving, communications, teamwork and other employability skills as well as the domain skills needed by emerging information management technologies. The new courses are built on activities which focus on different aspects of KM, drawing on existing content as a knowledge base. This paper presents the ongoing development of the KM programme through the key aspects in its conception and design

Overview of technologies for building robots in the classroom

This paper aims to give an overview of technologies that can be used to implement robotics within an educational context. We discuss complete robotics systems as well as projects that implement only certain elements of a robotics system, such as electronics, hardware, or software. We believe that Maker Movement and DIY trends offers many new opportunities for teaching and feel that they will become much more prominent in the future. Products and projects discussed in this paper are: Mindstorms, Vex, Arduino, Dwengo, Raspberry Pi, MakeBlock, OpenBeam, BitBeam, Scratch, Blockly and ArduBlock

Nineteen Ways of Looking at Statistical Software

We identify principles and practices for writing and publishing statistical software with maximum benefit to the scholarly community.