How to make privacy policies both GDPR-compliant and usable

It is important for organisations to ensure that their privacy policies are General Data Protection Regulation (GDPR) compliant, and this has to be done by the May 2018 deadline. However, it is also important for these policies to be designed with the needs of the human recipient in mind. We carried out an investigation to find out how best to achieve this.We commenced by synthesising the GDPR requirements into a checklist-type format. We then derived a list of usability design guidelines for privacy notifications from the research literature. We augmented the recommendations with other findings reported in the research literature, in order to confirm the guidelines. We conclude by providing a usable and GDPR-compliant privacy policy template for the benefit of policy writers

Ethical Challenges in Data-Driven Dialogue Systems

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence, Ethics, and Societ

Presenting the networked home: a content analysis of promotion material of Ambient Intelligence applications

Ambient Intelligence (AmI) for the home uses information and communication technologies to make users’ everyday life more comfortable. AmI is still in its developmental phase and is headed towards the first stages of diffusion. \ud Characteristics of AmI design can be observed, among others, in the promotion material of initial producers. A literature study revealed that AmI originally envisioned a central role for the user, convenience that AmI offers them and that attention should be paid to critical policy issues such as privacy and a potential loss of freedom. A content analysis of current promotion material of several high-tech companies revealed that these original ideas are not all reflected in the material. Attributes which were used most in the promotion material were ‘connectedness’, ‘control’, ‘easiness’ and ‘personalization’. An analysis of the pictures in the promotion material showed that almost half of the pictures contained no humans but appliances. These results only partly correspond to the original vision on AmI, since the emphasis is now on technology. The results represent a serious problem, since both users, as well as critical policy issues are underexposed in the current promotion material

Responding to the (almost) unknown: Social representations and corporate policies of social media

End-user driven technologies, such as social media, have dramatically changed organizations’ innovation processes. In these new contexts, organizational decision makers have to contend with a de facto adoption of new technologies that they have yet to understand fully. In order to contribute to the understanding of these new contexts and their implications for organizations and their decision makers, this paper examines the following question: How do organizations come to comprehend and react to end-user driven technologies? Conceptually based on social representations theory, this paper underscores how organizational decision-makers develop common sense knowledge of end-user driven technologies and how they consequently endorse responses in the form of dedicated policies that reflect this knowledge. This theoretical framework helps us interpret the empirical analysis of 25 corporate social media policies. The paper shows that, in developing their understanding of social media, so far organizational decision-makers have mostly associated them with what was already familiar to them and have devised policies that have reflected this mostly conservative understanding of the new technologies. Implications of this research include a better understanding of the foundation of the duality between mindful and mindless innovations in the context

Enhancing Privacy through the Visual Design of Privacy Notices: Exploring the Interplay of Curiosity, Control and Affect

Privacy policies are the initial communicators of the services' data handling practices. Yet, their design seldom ensures users' privacy comprehension or provides people with choices around their information management, resulting in negative feelings associated with the sign-up process. In this paper, we investigate how to improve these conditions to enhance privacy comprehension and management, while inducing more positive feelings towards privacy notices. In an online experiment (N=620), we examine factors active during privacy interactions: curiosity, privacy concerns, trust, and time. We study how, together with framing and control incorporated in visual designs of notices, these factors influence privacy comprehension, intention to disclose, and affect (negative-positive valence). Our results show that, depending on an individual's level of curiosity, control can influence privacy comprehension, disclosure, and valence. We demonstrate the moderating ability of valence on privacy concerns, indirectly affecting disclosures. We elaborate on the results, highlighting how privacy notices designed to activate curiosity and provide control, could enhance usability and strengthen privacy-conscious behaviors. We argue that future work should study affect to further the knowledge of its role in cognitive processing resulting from privacy interactions