Journal of responsible technology最新文献

National digital identity management systems have gained traction as a critical tool for inclusion of citizens in the increasingly digitised public services. With the help of the World Bank, countries around the world are committing to building and promoting digital identification systems to improve development outcomes as part of the Identity for development initiative (ID4D). One of those countries is Nigeria, which is building a national ID management database for its over 100 million residents. However, there are privacy, security, human rights, ethics and socio-cultural implications associated with the design and scaling of such a system at a national level. Through a mixed method approach, this paper identifies some of these concerns and categorises which ones Nigerians are most worried about. It provides an empirically sound perspective around centralised national electronic identity (eID) management system, public trust and responsible data governance, and offers recommendations on enhancing privacy, security and trustworthiness of the digital infrastructure for identity management in Nigeria.

Autonomous Vehicles (AVs) collect a vast amount of data during their operation (MBs/sec). What data is recorded, who has access to it, and how it is analysed and used can have major technical, ethical, social, and legal implications. By embedding Responsible Innovation (RI) methods within the AV lifecycle, negative consequences resulting from inadequate data logging can be foreseen and prevented. An RI approach demands that questions of societal benefit, anticipatory governance, and stakeholder inclusion, are placed at the forefront of research considerations. Considered as foundational principles, these concepts create a contextual mindset for research that will by definition have an RI underpinning as well as application. Such an RI mindset both inspired and governed the genesis and operation of a research project on autonomous vehicles. The impact this had on research outlines and workplans, and the challenges encountered along the way are detailed, with conclusions and recommendations for RI in practice.

In this article, we address the broad issue of a responsible use of Artificial Intelligence in Human Resources Management through the lens of a fair-by-design approach to algorithm development illustrated by the introduction of a new machine learning-based approach to job matching. The goal of our algorithmic solution is to improve and automate the recruitment of temporary workers to find the best match with existing job offers. We discuss how fairness should be a key focus of human resources management and highlight the main challenges and flaws in the research that arise when developing algorithmic solutions to match candidates with job offers. After an in-depth analysis of the distribution and biases of our proprietary data set, we describe the methodology used to evaluate the effectiveness and fairness of our machine learning model as well as solutions to correct some biases. The model we introduce constitutes the first step in our effort to control for unfairness in the outcomes of machine learning algorithms in job recruitment, and more broadly a responsible use of artificial intelligence in Human Resources Management thanks to “safeguard algorithms” tasked to control for biases and prevent discriminatory outcomes.

Artificial Intelligence (AI) promises huge potential for businesses but due to its black-box character has also substantial drawbacks. This is a particular challenge in regulated use cases, where software needs to be certified or validated before deployment. Traditional software documentation is not sufficient to provide the required evidence to auditors and AI-specific guidelines are not available yet. Thus, AI faces significant adoption barriers in regulated use cases, since accountability of AI cannot be ensured to a sufficient extent. This interview study aims to determine the current state of documenting AI in regulated use cases. We found that the risk level of AI use cases has an impact on the AI adoption and the scope of AI documentation. Further, we discuss how AI is currently documented and which challenges practitioners face when documenting AI.

What does it mean to be responsible and responsive when developing and deploying trusted autonomous systems in Defence? In this short reflective article, we describe a case study of building a trusted autonomous system – Athena AI  – within an industry-led, government-funded project with diverse collaborators and stakeholders. Using this case study, we draw out lessons on the value and impact of embedding responsible research and innovation-aligned, ethics-by-design approaches and principles throughout the development of technology at high translation readiness levels.

Understanding stakeholders’ views on novel autonomous systems in healthcare is essential to ensure these are not abandoned after substantial investment has been made. The ExTRAPPOLATE project applied the principles of Responsible Research and Innovation (RRI) in the development of an automated feedback system for psychological therapists, ‘AutoCICS’. A Patient and Practitioner Reference Group (PPRG) was convened over three online workshops to inform the system's development. Iterative workshops allowed proposed changes to the system (based on stakeholder comments) to be scrutinized. The PPRG reference group provided valuable insights, differentiated by role, including concerns and suggestions related to the applicability and acceptability of the system to different patients, as well as ethical considerations. The RRI approach enabled the anticipation of barriers to use, reflection on stakeholders’ views, effective engagement with stakeholders, and action to revise the design and proposed use of the system prior to testing in future planned feasibility and effectiveness studies. Many best practices and learnings can be taken from the application of RRI in the development of the AutoCICS system.

There have been ongoing discussions in research communities, including the field of trustworthy autonomous systems (TAS), on how researchers may meaningfully engage with responsible research and innovation (RRI). By critically reflecting on the RRI aspects of an ongoing research project focusing on the efficient capture of richer quantitative human response data (e.g., from consumer surveys), this paper offers a case study on how research development can be ethically driven. The role of RRI in the project is unpicked against the broader considerations of its possible interactions with researchers in a typology we developed: as a research safeguard, research subject, and research driver. Going beyond the more common practice of using RRI simply to safeguard how research should be conducted, it is demonstrated that it can also serve as a positive driving force to explore what should be researched. Experiences and challenges are elaborated within the main stages of research development, potentially applicable to a wider range of future projects in the field.

The digitalisation of production driven by new paradigms such as Industry 4.0, factories of the future and smart manufacturing, create new challenges as to how manufacturers and other supply chain actors would discharge their corporate responsibility to respect human rights. These new paradigms enable novel approaches like distributed and collaborative manufacturing. Manufacturers increasingly leverage digital technologies, such as 3D printing, cloud manufacturing and artificial intelligence, to provide customised products. Digital technologies also improve predictive and preventive maintenance on the shop floor and across the supply chain, increasing the overall resilience of manufacturing industries in times of crisis. This article proposes a blueprint of a collaborative, decentralised approach to human rights due diligence in digital supply chains. It argues that the pooling of human rights due diligence efforts in manufacturing industries could have network-wide effects of incentivising value chain actors to also collaborate on providing collective remedy.