Privacy Labelling and the Story of Princess Privacy and the Seven Helpers

Privacy is currently in 'distress' and in need of 'rescue', much like princesses in the all-familiar fairytales. We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the 'knight in shining armour') can be a solution to the current state of Privacy (the 'princess in distress'). We give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives (the 'seven helpers'): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary. We describe a common vision, proposing several important 'traits of character' of PL as well as identifying 'undeveloped potentialities', i.e., open problems on which the community can focus. More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like in order to address these needs. Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we define as, the ideal PL. In the end we present three approaches to establish and manage PL, through: self-evaluations, certifications, or community endeavors. Based on these, we sketch a roadmap for future developments.Comment: 26 pages, 3 figure

Community Coordinated Artificial Intelligence: Towards a unified framework for the democratisation of AI

Contributing to an emerging AI-paradigm shift, this thesis presents a unified socio-technical framework called Community Coordinated Artificial Intelligence (CoCoAI), which expands the horizons of the AI expertocracy. Currently, AI is used mostly by companies (or governments) to analyse people's behaviour to serve their own commercial interests. I argue instead how people, not companies, could ultimately benefit from the development and use of AI. To achieve this goal, I have established four research objectives. The first research objective is performing a literature review on the democratisation of AI. This serves as the scientific foundation for my thesis. My second objective is to establish a definition that unifies the various understandings of what the topic entails. Further, my third objective is to create an overview of the challenges and solutions to the democratisation of AI presented in the literature. Finally, my fourth research objective is to develop a socio-technical framework for the democratisation of AI, using the definition, challenges and solutions I established in my previous objectives. To form the scientific foundation necessary to accomplish this work, I will perform a structured configurative review of the literature on the topic. By creating a unified definition and an overview of the challenges and solutions, I will establish a foundation for further research on the topic. Moreover, my framework can inform the design of AI platforms and projects, promoting processes that ensure democratic control of the technology. CoCoAI provides benefits on three levels. On a societal level, CoCoAI promotes AI solutions that are beneficial to society as a whole, protecting rights and democratic values, and avoiding solutions that discriminate against social groups, or otherwise treats them unfairly. For organisations, CoCoAI increases the availability of AI resources, technology and expertise. This can enable more organisations to benefit from AI for their use case. Finally, on the individual level, CoCoAI promotes education, knowledge sharing, transparency and beneficial solutions. Increased access to educational resources and knowledge sharing in AI can contribute to a society where more people have a basic understanding of the technology. By also having more transparency surrounding the AI systems in use, users will be able to make more informed decisions in their interactions with such AI services and systems. Finally, CoCoAI promotes access to beneficial AI solutions, both by making corporate AI development processes more democratic, but also by enabling the creation of more grassroots AI projects as a result of better access to AI resources, knowledge and technology

A multidisciplinary definition of privacy labels

Purpose This paper aims to present arguments about how a complex concept of privacy labeling can be a solution to the current state of privacy. Design/methodology/approach The authors give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives: Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological and Multidisciplinary. They describe a common vision, proposing several important “traits of character” of PL as well as identifying “undeveloped potentialities”, i.e. open problems on which the community can focus. Findings This position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like to address these needs. Main aspects considered are the PL’s educational power to change people’s knowledge of privacy, tools useful for constructing PL and the possible visual appearances of PL. They also identify how the present landscape of privacy certifications could be improved by PL. Originality/value The authors adopt a multidisciplinary approach to defining PL as well as give guidelines in the form of goals, characteristics, open problems, starting points and a roadmap for creating the ideal PL. </jats:sec