Journal of responsible technology最新文献

This paper reports on a qualitative research study that explored the practical and emotional experiences of young people aged 13–17 using algorithmically-mediated online platforms. It demonstrates an RI-based methodology for responsible two-way dialogue with the public, through listening to young people's needs and responding to their concerns. Participants discussed in detail how online algorithms work, enabling the young people to reflect, question, and develop their own critiques on issues related to the use of internet technologies. The paper closes with action areas from the young people for a fairer, usefully transparent and more responsible online environment. These actions include a desire to be informed about what data (both personal and situational) is collected and how, and who uses it and why, and policy recommendations for meaningful algorithmic transparency and accountability. Finally, participants claimed that whilst transparency is an important first principle, they also need more control over how platforms use the information they collect from users, including more regulation to ensure transparency is both meaningful and sustained.

The Responsible Innovation (RI) approach aims to transform research and development (R&D) into being more anticipatory, inclusive, reflective, and responsive. This study highlights the challenges of embedding RI in R&D practices. We fostered collective learning on RI in a socially assistive robot development project through applying participatory action research (PAR). In the PAR, we employed a mixed-methods approach, combining interviews, workshops, and online questionnaires, to collectively explore opportunities for RI, and elicit team member perceptions, opinions, and beliefs about RI. Our PAR led to some modest yet purposeful, deliberate efforts to address particular concerns regarding, for instance, privacy, control, and energy consumption. However, we also found that the embedding of RI in R&D practices can be hampered by four partly interrelated barriers: lack of an action perspective, the noncommittal nature of RI, the misconception that co-design equals RI, and limited integration between different R&D task groups. In this paper, we discuss the implications of these barriers for R&D teams and funding bodies, and we recommend PAR as a solution to address these barriers.

Automated Facial Analysis technologies, predominantly used for facial detection and recognition, have garnered significant attention in recent years. Although these technologies have seen advancements and widespread adoption, biases embedded within systems have raised ethical concerns. This research aims to delve into the disparities of Automatic Gender Recognition systems (AGRs), particularly their oversimplification of gender identities through a binary lens. Such a reductionist perspective is known to marginalize and misgender individuals. This study set out to investigate the alignment of an individual's gender identity and its expression through the face with societal norms, and the perceived difference between misgendering experiences from machines versus humans. Insights were gathered through an online survey, utilizing an AGR system to simulate misgendering experiences. The overarching goal is to shed light on gender identity nuances and guide the creation of more ethically responsible and inclusive facial recognition software.

In recent years, we have seen many examples of software products unintentionally causing demonstrable harm. Many guidelines for ethical and responsible computing have been developed in response. Dominant approaches typically attribute liability and blame to individual companies or actors, rather than understanding how the working practices, norms, and cultural understandings in the software industry contribute to such outcomes. In this paper, we propose an understanding of responsibility that is infrastructural, relational, and cultural; thus, providing a foundation to better enable responsible software engineering into the future. Our approach draws on Young's (2006) social connection model of responsibility and Star and Ruhleder's (1994) concept of infrastructure. By bringing these theories together we introduce a concept called infrastructural injustice, which offers a new way for software engineers to consider their opportunities for responsible action with respect to society and the planet. We illustrate the utility of this approach by applying it to an Open-Source software communities’ development of Deepfake technology, to find key leverage points of responsibility that are relevant to both Deepfake technology and software engineering more broadly.

In this editorial, we engage with the European Commission's 2023 recommendation calling for risk assessment with Member States on four critical technology areas, including quantum technology. A particular emphasis is put on the risks associated with technology security and technology leakage. Such risks may lead to protectionist measures. Mobilising European normative anchor points that inform the “right impacts” of research and innovation, we argue that a protectionist approach on the part of the European Union can lead to moral isolationism. This, in turn, can limit Europe's contribution to global development with respect to technological advances, sustainable development and quality of life. We contend that decisions on protectionism around quantum technology should not be made with a protectionist mindset about European values.

Organizations dealing with mission-critical AI based autonomous systems may need to provide continuous risk management controls and establish means for their governance. To achieve this, organizations are required to embed trustworthiness and transparency in these systems, with human overseeing and accountability. Autonomous systems gain trustworthiness, transparency, quality, and maintainability through the assurance of outcomes, explanations of behavior, and interpretations of intent. However, technical, commercial, and market challenges during the software development lifecycle (SDLC) of autonomous systems can lead to compromises in their quality, maintainability, interpretability and explainability. This paper conceptually models transformation of SDLC to enable affordances for assurance, explanations, interpretations, and overall governance in autonomous systems. We argue that opportunities for transformation of SDLC are available through concerted interventions such as technical debt management, shift-left approach and non-ephemeral artifacts. This paper contributes to the theory and practice of governance of autonomous systems, and in building trustworthiness incrementally and hierarchically.