In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

Transparency is often seen as a means to provide accountability and show that something is done with due diligence. This approach to transparency regards it as a remedy to hidden (potentially malevolent) practices. We, therefore, require transparency from manufacturers of products and services. While the outcry for more transparency often occurs in response to a particular scandal (e.g., the various controversies surrounding Facebook in 2018), the European Union's General Data Protection Regulation (GDPR) includes transparency as a proactive requirement for information technologies that process personal data.

Social robots, those that exhibit personality and communicate with us using high-level dialogue and natural cues, will soon be part of our daily lives. In this paper, we gather expert opinions from different international workshops exploring ethical, legal, and social (ELS) concerns associated with social robots. In contrast to literature that looks at specific challenges, often from a certain disciplinary angle, our contribution to the literature provides an overview of the ELS discussions in a holistic fashion, shaped by active deliberation with a multitude of experts across four workshops held between 2015 and 2017 held in major international workshops (ERF, NewFriends, JSAI-isAI). It also explores pathways to address the identified challenges. Our contribution is in line with the latest European robot regulatory initiatives but covers an area of research that the latest AI and robot governance strategies have scarcely covered. Specifically, we highlight challenges to the use of social robots from a user perspective, including issues such as privacy, autonomy, and the dehumanization of interactions; or from a worker perspective, including issues such as the possible replacement of jobs through robots. The paper also compiles the recommendations to these ELS issues the experts deem appropriate to mitigate compounding risks. By then contrasting these challenges and solutions with recent AI and robot regulatory strategies, we hope to inform the policy debate and set the scene for further research.

There is increasing attention given to the concept of trustworthiness for artificial intelligence and robotics. However, trust is highly context-dependent, varies among cultures, and requires reflection on others’ trustworthiness, appraising whether there is enough evidence to conclude that these agents deserve to be trusted. Moreover, little research exists on what happens when too much trust is placed in robots and autonomous systems. Conceptual clarity and a shared framework for approaching overtrust are missing. In this contribution, we offer an overview of pressing topics in the context of overtrust and robots and autonomous systems. Our review mobilizes insights solicited from in-depth conversations from a multidisciplinary workshop on the subject of trust in human–robot interaction (HRI), held at a leading robotics conference in 2020. A broad range of participants brought in their expertise, allowing the formulation of a forward-looking research agenda on overtrust and automation biases in robotics and autonomous systems. Key points include the need for multidisciplinary understandings that are situated in an eco-system perspective, the consideration of adjacent concepts such as deception and anthropomorphization, a connection to ongoing legal discussions through the topic of liability, and a socially embedded understanding of overtrust in education and literacy matters. The article integrates diverse literature and provides a ground for common understanding for overtrust in the context of HRI.

Algorithmic profiling has become increasingly prevalent in many social fields and practices, including finance, marketing, law, cultural consumption and production, and social engagement. Although researchers have begun to investigate algorithmic profiling from various perspectives, socio-technical studies of algorithmic profiling that consider users’ everyday perceptions are still scarce. In this article, we expand upon existing user-centered research and focus on people’s awareness and imaginaries of algorithmic profiling, specifically in the context of social media and targeted advertising. We conducted an online survey geared toward understanding how Facebook users react to and make sense of algorithmic profiling when it is made visible. The methodology relied on qualitative accounts as well as quantitative data from 292 Facebook users in the United States and their reactions to their algorithmically inferred ‘Your Interests’ and ‘Your Categories’ sections on Facebook. The results illustrate a broad set of reactions and rationales to Facebook’s (public-facing) algorithmic profiling, ranging from shock and surprise, to accounts of how superficial – and in some cases, inaccurate – the profiles were. Taken together with the increasing reliance on Facebook as critical social infrastructure, our study highlights a sense of algorithmic disillusionment requiring further research.