Journal of responsible technology最新文献

The AI Ethics literature, aimed to responsibly develop AI systems, widely agrees on the fact that society is in dire need for effective accountability mechanisms with regards to AI systems. Particularly, machine learning (ML) systems cause reason for concern due to their opaque and self-learning characteristics. Nevertheless, what such accountability mechanisms should look like remains either largely unspecified (e.g., ‘stakeholder input’) or ineffective (e.g., ‘ethical guidelines’). In this paper, I argue that the difficulty to formulate and develop effective accountability mechanisms lies partly in the predominant focus on Mill's harm's principle, rooted in the conception of freedom as non-interference. A strong focus on harm overcasts other moral wrongs, such as potentially problematic power dynamics between those who shape the system and those affected by it. I propose that the neo-republican conception of freedom as non-domination provides a suitable framework to inform responsible ML development. Domination, understood by neo-republicans, is a moral wrong as it undermines the potential for human flourishing. In order to mitigate domination, neo-republicans plead for accountability mechanisms that minimize arbitrary relations of power. Neo-republicanism should hence inform responsible ML development as it provides substantive and concrete grounds when accountability mechanisms are effective (i.e. when they are non-dominating).

Accountability encompasses multiple aspects such as responsibility, justification, reporting, traceability, audit, and redress so as to satisfy diverse requirements of different stakeholders— consumers, regulators, developers, etc. In order to take into account needs of different stakeholders and thus, to put into practice accountability in Artificial Intelligence, the notion of empathy can be quite effective. Empathy is the ability to be sensitive to the needs of someone based on understanding their affective states and intentions, caring for their feelings, and socialization, which can help in addressing the social-technical challenges associated with accountability. The goal of this paper is twofold. First, we elucidate the connections between empathy and accountability, drawing find- ings from various disciplines like psychology, social science, and organizational science. Second, we suggest potential pathways to incorporate empathy.

Billions of people interact within platform-based ecosystems containing the personal data of their daily lives. Data which have become rigorously creatable, processable, and shareable. Here, platform providers facilitate interactions between three types of relevant actors: users, service providers, and third parties. Research in the information systems field has shown that platform providers influence their platform ecosystems to promote the contributions of service providers and exercise control by utilizing boundary resources. Through a socio-techno-legal analysis of two high-profile cases and their application on the General Data Protection Regulation (GDPR) we show that the boundary resource design, arrangement, and interplay can influence whether and to what extent platform providers are accountable for platform providers unlawful personal data processing in platform ecosystems. The findings can have a huge impact to account actors for personal data misusage in platform ecosystems and, thus, the protection of personal liberty and rights in such socio-technical systems.

Civilian Unmanned Aerial Vehicle (UAV) is an emerging technology in Indian crop insurance applications. The technology is new to an agro-based country like India with diverse socio-cultural norms and values. However, in such a diverse democracy, UAV governance and deployment pose a significant challenge and risk. In other words, charting out a proper framework for a risk-free implementation of this governance has emerged as a leading research topic in the concerned discipline. In innovations literature, Responsible Innovation (RI) takes care of emerging technology governance; thus, RI becomes significant as a theoretical framework. The study is intended to find out how the framework of RI enables responsible governance and also who are the main actors and stakeholders of governance and deployment of civilian UAVs in crop insurance applications in India? An in-depth interview method and snowball sampling technique have been employed to identify interviewees from Delhi, Gujarat, and Rajasthan. Findings suggest that civilian UAV is effective in handling risk, crop damage assessment, and claim settlement. The RI approach, through its dimensions and steps, enables equal participation and deliberation among all the actors and stakeholders of UAV governance that consists of government bodies, research organizations, insurance agencies, local administration, and farmers. Effective regulations, adhering to accountability, and responsibility promote responsible governance.

AI as decision support supposedly helps human agents make ‘better’ decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise with unintended AI influence. I argue that unintended AI influence has important implications for the way we perceive and evaluate human-AI interaction. To make this point approachable from both the theoretical and practical side, and to avoid anthropocentrically-laden ambiguities, I introduce the notion of decision points. Based on this, the main argument of this paper will be presented in two consecutive steps: i) unintended AI influence doesn’t allow for an appropriate determination of decision points - this will be introduced as decision-point-dilemma, and ii) this has important implications for the ascription of responsibility.

Algorithmic decision support systems are widely applied in domains ranging from healthcare to journalism. To ensure that these systems are fair and accountable, it is essential that humans can maintain meaningful agency, understand and oversee algorithmic processes. Explainability is often seen as a promising mechanism for enabling human-in-the-loop, however, current approaches are ineffective and can lead to various biases. We argue that explainability should be tailored to support naturalistic decision-making and sensemaking strategies employed by domain experts and novices. Based on cognitive psychology and human factors literature review we map potential decision-making strategies dependant on expertise, risk and time dynamics and propose the conceptual Expertise, Risk and Time Explainability framework, intended to be used as explainability design guidelines. Finally, we present a worked example in journalism to illustrate the applicability of our framework in practice.

Algorithmic decision support is increasingly used in a whole array of different contexts and structures in various areas of society, influencing many people’s lives. Its use raises questions, among others, about accountability, transparency and responsibility. While there is substantial research on the issue of algorithmic systems and responsibility in general, there is little to no prior research on organisational responsibility and its attribution. Our article aims to fill that gap; we give a brief overview of the central issues connected to ADS, responsibility and decision-making in organisational contexts and identify open questions and research gaps. Furthermore, we describe a set of guidelines and a complementary digital tool to assist practitioners in mapping responsibility when introducing ADS within their organisational context.

The wicked challenge of designing accountability measures aimed at improving algorithmic accountability demands human-centered approaches. Based on one of the most common definitions of accountability as the relationship between an actor and a forum, this article presents an analytic lens in the form of actor and forum agency, through which the accountability process can be analysed. Two case studies - the Austrian Public Employment Service’s AMAS system and the EnerCoach energy accounting system, serve as examples to an analysis of accountability based on the agency of the stakeholders. Developed through the comparison of the two systems, the Algorithmic Accountability Agency Framework (A³ framework) aimed at supporting the analysis and the improvement of agency throughout the four steps of the accountability process is presented and discussed.

With the rapid deployment of socio-technical systems into all aspects of daily life, we need to be prepared for their failures. It is inherently impractical to specify all the lawful interactions of these systems, in turn, the possibility of invalid interactions cannot be excluded at design time. As modern systems might harm people, or compromise assets if they fail, they ought to be accountable. Accountability is an interdisciplinary concept that cannot be easily described as a holistic technical property of a system. Thus, in this paper, we propose a bottom-up approach to enable accountability using goal-specific accountability mechanisms. Each mechanism provides forensic capabilities that help us to identify the root cause for a specific type of events, both to eliminate the underlying (technical) problem and to assign blame. This paper presents the different ingredients that are required to design and build an accountability mechanism and focuses on the technical and practical utilization of causality theories as a cornerstone to achieve our goal. To the best of our knowledge, the literature lacks a systematic methodology to envision, design, and implement abilities that promote accountability in systems. With a case study from the area of microservice-based systems, which we deem representative of modern complex systems, we demonstrate the effectiveness of the approach as a whole. We show that it is generic enough to accommodate different accountability goals and mechanisms.