AI & Society最新文献

This paper considers what liberal philosopher Michael Sandel coins the ‘moral limits of markets’ in relation to the idea of paying people for data about their biometrics and emotions. With Sandel arguing that certain aspects of human life (such as our bodies and body parts) should be beyond monetisation and exchange, others argue that emerging technologies such as Personal Information Management Systems can enable a fairer, paid, data exchange between the individual and the organisation, even regarding highly personal data about our bodies and emotions. With the field of data ethics rarely addressing questions of payment, this paper explores normative questions about data dividends. It does so by conducting a UK-wide, demographically representative online survey to quantitatively assess adults’ views on being paid for personal data about their biometrics and emotions via a Personal Information Management System, producing a data dividend, a premise which sees personal data through the prism of markets and property. The paper finds diverse attitudes based on socio-demographic characteristics, the type of personal data sold, and the type of organisation sold to. It argues that (a) Sandel’s argument regarding the moral limits of markets has value in protecting fundamental freedoms of those in society who are arguably least able to (such as the poor); but (b) that contexts of use, in particular, blur moral limits regarding fundamental freedoms and markets.

Today, the US pursues the global capture of data (understood as a significant engine of growth) by way of bi- and plurilateral trade agreements. However, the project of securing the global free flow of data has been pursued ever since the dawn of digital telecommunication in the 1960s and the US has made significant legal efforts to institutionalise it. These efforts have two phases: In the first 1970s and 80s “freedom of information” phase, the legal justification (and contestation) of the global free flow of data hinged on imagining data as information, and its exchange as a practice of liberty. The second phase began in the late 1990s and continues today. In this phase, the free flow of data is aligned with a free-trade agenda in the context of first e-commerce and, starting in the 2000s, through attempts at creating a global public domain of personal data for the platform economy. The global free flow of data is an intrinsic aspect of informational capitalism. Assuming a constitutive, but not commanding role for law in informational capitalism, we conclude that the US attempt at ensuring free flow for its informational corporations is neither an entirely contingent nor a necessary outcome. It is a product of legal imagination.

Machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the national AI market, and technologies like face recognition, object tracking and automated driving play a central role in surveillance systems and social governance projects relying on the large-scale collection and processing of sensor data. Like other novel articulations of technology and society, machine vision is defined, developed and explained by different actors through the work of imagination. In this article, we draw on the concept of sociotechnical imaginaries to understand how Chinese companies represent machine vision. Through a qualitative multimodal analysis of the corporate websites of leading industry players, we identify a cohesive sociotechnical imaginary of machine vision, and explain how four distinct visual registers contribute to its articulation. These four registers, which we call computational abstraction, human–machine coordination, smooth everyday, and dashboard realism, allow Chinese tech companies to articulate their global ambitions and competitiveness through narrow and opaque representations of machine vision technologies.

The use of technologies in personnel selection has come under increased scrutiny in recent years, revealing their potential to amplify existing inequalities in recruitment processes. To date, however, there has been a lack of comprehensive assessments of respective discriminatory potentials and no legal or practical standards have been explicitly established for fairness auditing. The current proposal of the Artificial Intelligence Act classifies numerous applications in personnel selection and recruitment as high-risk technologies, and while it requires quality standards to protect the fundamental rights of those involved, particularly during development, it does not provide concrete guidance on how to ensure this, especially once the technologies are commercially available. We argue that comprehensive and reliable auditing of personnel selection technologies must be contextual, that is, embedded in existing processes and based on real data, as well as participative, involving various stakeholders beyond technology vendors and customers, such as advocacy organizations and researchers. We propose an architectural draft that employs a data trustee to provide independent, fiduciary management of personal and corporate data to audit the fairness of technologies used in personnel selection. Drawing on a case study conducted with two state-owned companies in Berlin, Germany, we discuss challenges and approaches related to suitable fairness metrics, operationalization of vague concepts such as migration* and applicable legal foundations that can be utilized to overcome the fairness-privacy-dilemma arising from uncertainties associated with current laws. We highlight issues that require further interdisciplinary research to enable a prototypical implementation of the auditing concept in the mid-term.

Dialogue systems, from Virtual Personal Assistants such as Siri, Cortana, and Alexa to state-of-the-art systems such as BlenderBot3 and ChatGPT, are already widely available, used in a variety of applications, and are increasingly part of many people’s lives. However, the task of enabling them to use empathetic language more convincingly is still an emerging research topic. Such systems generally make use of complex neural networks to learn the patterns of typical human language use, and the interactions in which the systems participate are usually mediated either via interactive text-based or speech-based interfaces. In human–human interaction, empathy has been shown to promote prosocial behaviour and improve interaction. In the context of dialogue systems, to advance the understanding of how perceptions of empathy affect interactions, it is necessary to bring greater clarity to how empathy is measured and assessed. Assessing the way dialogue systems create perceptions of empathy brings together a range of technological, psychological, and ethical considerations that merit greater scrutiny than they have received so far. However, there is currently no widely accepted evaluation method for determining the degree of empathy that any given system possesses (or, at least, appears to possess). Currently, different research teams use a variety of automated metrics, alongside different forms of subjective human assessment such as questionnaires, self-assessment measures and narrative engagement scales. This diversity of evaluation practice means that, given two DSs, it is usually impossible to determine which of them conveys the greater degree of empathy in its dialogic exchanges with human users. Acknowledging this problem, the present article provides an overview of how empathy is measured in human–human interactions and considers some of the ways it is currently measured in human–DS interactions. Finally, it introduces a novel third-person analytical framework, called the Empathy Scale for Human–Computer Communication (ESHCC), to support greater uniformity in how perceived empathy is measured during interactions with state-of-the-art DSs.

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did not come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an example.