Explanations of news personalisation across countries and media types

News outlets worldwide increasingly adopt user- and system-driven personalisation to individualise their news delivery. Yet, the technical implementation of news personalisation systems, in particular the one relying on algorithmic news recommenders (ANRs) and tailoring individual news suggestions with the help of user data, often remains opaque. In our article, we examine how news personalisation is used by quality and popular media in three countries with different media accountability infrastructures - Brazil, the Netherlands, and Russia - and investigate how information about personalisation usage is communicated to the news readers via privacy policies. Our findings point out that news personalisation systems are predominantly treated as black boxes that indicate a significant gap between practice and theory of algorithmic transparency, in particular in the non-EU context

Recommenders you can rely on: A legal and empirical perspective on the transparency and control individuals require to trust news personalisation

This article explores the role law can play to support trust in the context of news personalisation. The need to ensure trust in the face of technological changes in information dissemination is an important aspect of both recent horizontal legislation such as the Digital Services Act, as well as context-specific specific efforts surrounding for example disinformation. In these legal discussions, however, what trust is, why law should promote it, and what concrete measures are suitable to do so often remain ambiguous. This raises suspicions over whether trust is simply a selling point of traditional legal measures, and if not, what concrete role law can and should play to promote trust. This article focuses on the role control and transparency measures can play to safeguard trust in organisations that use news personalisation. It first analyses how trust should be understood in the context of news personalisation, how media regulation has traditionally supported trust, and how it should continue to do so in the context of news personalisation. It then draws on a conceptual framework of transparency measures in the context of news personalisation to survey how important different transparency and control measures are to the individuals who place trust in organisations that use personalisation. Law’s current focus on informing individuals about and empowering them to stop personalisation does not account for the importance of enabling individuals to control how news is personalised

Explanations of news personalisation across countries and media types

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Towards a normative perspective on journalistic AI: Embracing the messy reality of normative ideals

Few would disagree that AI systems and applications need to be “responsible,” but what is “responsible” and how to answer that question? Answering that question requires a normative perspective on the role of journalistic AI and the values it shall serve. Such a perspective needs to be grounded in a broader normative framework and a thorough understanding of the dynamics and complexities of journalistic AI at the level of people, newsrooms and media markets. This special issue aims to develop such a normative perspective on the use of AI-driven tools in journalism and the role of digital journalism studies in advancing that perspective. The contributions in this special issue combine conceptual, organisational and empirical angles to study the challenges involved in actively using AI to promote editorial values, the powers at play, the role of economic and regulatory conditions, and ways of bridging academic ideals and the messy reality of the real world. This editorial brings the different contributions into conversation, situates them in the broader digital journalism studies scholarship and identifies seven key-take aways

Towards a normative perspective on journalistic AI: Embracing the messy reality of normative ideals

Few would disagree that AI systems and applications need to be “responsible,” but what is “responsible” and how to answer that question? Answering that question requires a normative perspective on the role of journalistic AI and the values it shall serve. Such a perspective needs to be grounded in a broader normative framework and a thorough understanding of the dynamics and complexities of journalistic AI at the level of people, newsrooms and media markets. This special issue aims to develop such a normative perspective on the use of AI-driven tools in journalism and the role of digital journalism studies in advancing that perspective. The contributions in this special issue combine conceptual, organisational and empirical angles to study the challenges involved in actively using AI to promote editorial values, the powers at play, the role of economic and regulatory conditions, and ways of bridging academic ideals and the messy reality of the real world. This editorial brings the different contributions into conversation, situates them in the broader digital journalism studies scholarship and identifies seven key-take aways

Designing for Joint Human-Automation Cognition Through a Shared Representation of 4D Trajectory Management

The current evolution of the ATM system, led by the SESAR programme in Europe and the NextGen programme in the US, is foreseen to bring a paradigm shift to the work domain of the air traffic controller. A focal point is the introduction of the 4D (space and time) trajectory as a means for strategic management rather than the current –hands on– method of control. In both programmes a central role is foreseen for the human operator, aided by higher levels of automation and advanced decision support tools. However, many other complex socio-technical domains have shown that the transition to higher levels of automation often introduces new problems, problems that are harder to resolve than the ones intended to solve in the first place. This paper presents one approach to the design of a shared representation for 4D trajectory management. The ultimate goal is to design a shared representation which forms the basis for both the design of the humanmachine interfaces and the rationale that guides the automation. It is expected that such a shared representation will greatly benefit the joint cognition of humans and automated agents in ATM and will mitigate breakdowns in coordination by design. A preliminary version of a joint cognitive representation for 4D trajectory management has been developed and is introduced in this paper. Future work will focus on the further development and refinement of shared representations by means of human-in-the-loop experiments