Towards a Rosetta Stone for translating data between information systems

Information systems are an important organizational asset and offer numerous benefits. However, organizations face continued challenges when upgrading ageing information systems, and the data contained within, to newer platforms. This article explores, through conversations with information systems professionals in four organizations, the potential development of a ‘Rosetta Stone’, which can translate data between systems and be used to help overcome various challenges associated with their modernization. Despitemixed feedback regarding theRosetta Stone concept from interviewees, solutions highlighted in literature combinedwith participant feedback presented theories for its development, primarily as a tool to enable meaningful interpretation of data, rather than direct translation. The conclusion reflects on data collected to recommend a framework for how the tool might be developed and has the potential to be of significant interest to practitioners, open-source communities and organizations

Patterns of Discrimination: On Photographic Portraits as Documents of Truth in Automated Facial Recognition

Denne avhandlingen tar for seg fotografiers rolle i treningen av ansiktsgjenkjenningsalgoritmer, samt i selve den tekniske prosessen hvor ansikter analyseres. Gjennom en lesning av tre ulike kunstprosjekter som på ulike måter anvender eksisterende ansiktsgjenkjenningsteknologi til å problematisere denne praksisen, etablerer jeg hvordan ulike fordommer – særlig hva angår fotografiets status som objektiv representasjon av verden – påvirker systemenes evne til å analysere ansikter. De aktuelle prosjektene er ImageNet Roulette (2019) av Trevor Paglen og AI-forsker Kate Crawford, How do you see me? (2019) av Heather Dewey-Hagborg, og Spirit is a Bone (2013-15) av kunstner-duoen Broomberg & Chanarin. Problemstillingen som oppgaven forsøker å besvare er som følger: hva kan disse kunstprosjektene fortelle publikum om ansiktsgjenkjenningsteknologi som praksis, og hvilken rolle spiller digitalt fotografi som slike systemers bindeledd til den analoge verden «utenfor» dem selv? Som svar på dette tar avhandlingen for seg selve den tekniske arkitekturen og hvordan den legger føringer for ansiktsgjenkjenningssystemers operasjoner alt i designprosessen. I tillegg diskuteres ansiktsgjenkjenning fra et historisk perspektiv, hvor forsøk på å knytte juridisk identitet til kroppen gjennom fotografi spores helt tilbake til mediets oppfinnelse på 1800-tallet.Kunsthistorie mastergradsoppgaveKUN350MAHF-KU

Case board, traces, & chicanes: Diagrams for an archaeology of algorithmic prediction through critical design practice

This PhD thesis utilises diagrams as a language for research and design practice to critically investigate algorithmic prediction. As a tool for practice-based research, the language of diagrams is presented as a way to read algorithmic prediction as a set of intricate computational geometries, and to write it through critical practice immersed in the very materials in question: data and code. From a position rooted in graphic and interaction design, the research uses diagrams to gain purchase on algorithmic prediction, making it available for examination, experimentation, and critique. The project is framed by media archaeology, used here as a methodology through which both the technical and historical "depths" of algorithmic systems are excavated. My main research question asks: How can diagrams be used as a language to critically investigate algorithmic prediction through design practice? This thesis presents two secondary questions for critical examination, asking: Through which mechanisms does thinking/writing/designing in diagrammatic terms inform research and practice focused on algorithmic prediction? As algorithmic systems claim to produce objective knowledge, how can diagrams be used as instruments for speculative and/or conjectural knowledge production? I contextualise my research by establishing three registers of relations between diagrams and algorithmic prediction. These are identified as: Data Diagrams to describe the algorithmic forms and processes through which data are turned into predictions; Control Diagrams to afford critical perspectives on algorithmic prediction, framing the latter as an apparatus of prescription and control; and Speculative Diagrams to open up opportunities for reclaiming the generative potential of computation. These categories form the scaffolding for the three practice-oriented chapters where I evidence a range of meaningful ways to investigate algorithmic prediction through diagrams. This includes, the 'case board' where I unpack some of the historical genealogies of algorithmic prediction. A purpose-built graph application materialises broader reflections about how such genealogies might be conceptualised, and facilitates a visual and subjective mode of knowledge production. I then move to producing 'traces', namely probing the output of an algorithmic prediction system|in this case YouTube recommendations. Traces, and the purpose-built instruments used to visualise them, interrogate both the mechanisms of algorithmic capture and claims to make these mechanisms transparent through data visualisations. Finally, I produce algorithmic predictions and examine the diagrammatic "tricks," or 'chicanes', that this involves. I revisit a historical prototype for algorithmic prediction, the almanac publication, and use it to question the boundaries between data-science and divination. This is materialised through a new version of the almanac - an automated publication where algorithmic processes are used to produce divinatory predictions. My original contribution to knowledge is an approach to practice-based research which draws from media archaeology and focuses on diagrams to investigate algorithmic prediction through design practice. I demonstrate to researchers and practitioners with interests in algorithmic systems, prediction, and/or speculation, that diagrams can be used as a language to engage critically with these themes

Flesh Without Blood: (Re)locating Embodiment in Technology

The social/technology divide has eclipsed our understanding of the many ways in which the two are interconnected. In this thesis I examine the interplay of the social and technological through the lens of embodiment. In particular, I focus on the ways in which bodies become located, relocated and even dislocated, in interaction with technologies. My approach is an analytical synthesis informed by three examinations: The art of Mariko Mori; the ‘robot’ social media influencer @lilmiquela; and applications of artificial intelligence on the human body. These examinations can be thought of as thought experiments, case studies or musings to help explore the possibilities for bodies rendered through technologies. Through the complex interaction with technologies, embodiment is affected and the question of where bodies begin and end becomes a productive way to think about sociological processes of identity and power

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Big Data Redux: New Issues and Challenges Moving Forward

As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics

Introduction: Ways of Machine Seeing

How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. This 'editorial' is for a special issue of AI & Society, which includes contributions from: María Jesús Schultz Abarca, Peter Bell, Tobias Blanke, Benjamin Bratton, Claudio Celis Bueno, Kate Crawford, Iain Emsley, Abelardo Gil-Fournier, Daniel Chávez Heras, Vladan Joler, Nicolas Malevé, Lev Manovich, Nicholas Mirzoeff, Perle Møhl, Bruno Moreschi, Fabian Offert, Trevor Paglan, Jussi Parikka, Luciana Parisi, Matteo Pasquinelli, Gabriel Pereira, Carloalberto Treccani, Rebecca Uliasz, and Manuel van der Veen

Towards a Criminology of the Domestic

Criminology has paid insufficient attention to the ‘domestic’ arena, as a locale that is being reconfigured through technological and social developments in ways that require us to reconsider offending and victimisation. This article addresses this lacuna. We take up Campbell's (2016) challenge that criminology needs to develop more sophisticated models of place and space, particularly in relation to changing patterns of consumption and leisure activity and the opportunities to offend in relation to these from within the domestic arena