A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry

Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place

Supporting Annotators with Affordances for Efficiently Labeling Conversational Data

Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time consuming and expensive. To address the concerns of effort and tedium, we designed CAL, a novel interface to aid in data labeling. We made several key design decisions for CAL, which include preventing inapt labels from being selected, guiding users in selecting an appropriate label when they need assistance, incorporating labeling documentation into the interface, and providing an efficient means to view previous labels. We implemented a production-quality implementation of CAL and report a user-study evaluation that compares CAL to a standard spreadsheet. Key findings of our study include users using CAL reported lower cognitive load, did not increase task time, users rated CAL to be easier to use, and users preferred CAL over the spreadsheet

Evaluating a Methodology for Increasing AI Transparency: A Case Study

In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a useful starting point, but no single template can cover the needs of diverse documentation consumers. It is possible in principle, however, to create a repeatable methodology to generate truly useful documentation. Richards et al. [25] proposed such a methodology for identifying specific documentation needs and creating templates to address those needs. Although this is a promising proposal, it has not been evaluated. This paper presents the first evaluation of this user-centered methodology in practice, reporting on the experiences of a team in the domain of AI for healthcare that adopted it to increase transparency for several AI models. The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases. Analysis of the benefits and costs of this methodology are reviewed and suggestions for further improvement in both the methodology and supporting tools are summarized

Information Foraging Theory as a Unifying Foundation for Software Engineering Research : Connecting the Dots

Empirical studies have shown that programmers spend up to one-third of their time navigating through code during debugging. Although researchers have conducted empirical studies to understand programmers’ navigation difficulties and developed tools to address those difficulties, the resulting findings tend to be loosely connected to each other. To address this gap, we propose using theory to “connect the dots” between software engineering (SE) research findings. Our theory of choice is Information Foraging Theory (IFT) which explains and predicts how people seek information in an environment. Thus, it is well-suited as a unifying foundation because navigating code is a fundamental aspect of software engineering. In this dissertation, we investigated IFT’s suitability as a unifying foundation for SE through a combination of tool building and empirical user studies of programmers debugging. Our contributions show how IFT can help to unify SE research via cross-cutting insights spanning multiple software engineering subdisciplines

Ethik des Selbst vs. Ethik am Anderen

Die Arbeit untersucht die kritischen Projekte Friedrich Nietzsches und Emmanuel Lévinas', die auf je eigene Weise gegen den aus ihrer Sicht totalitär-indifferenten Charakter der epistemologischen und moralischen Diskurse des Abendlandes andenken. Nietzsche optiert gegen den Primat des Allgemeinen für eine leiblich fundierte Ethik der Selbstbestimmung, Lévinas hingegen für eine Ethik der lebendigen Verantwortung im Angesicht des anderen Menschen. Verhandelt werden hier die Schnittmengen und Trennlinien zweier Denker, die in ihren asymmetrischen Ethik-Konzepten mehr gemeinsam haben, als es zunächst scheint. Der Autor möchte mit den vermeintlichen Antipoden Nietzsche und Lévinas daran erinnern, dass die Verantwortung des Einzelnen über jede Kodifizierung erhaben bleibt