This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to (...) the SDGs. Second, as a specific instantiation of the SDGIE framework, the SDG Impact Intensity Model (SDGIIM) is theoretically and operationally established. SDGIIM embeds expert decision-making and SDG keyword banks in textual data processing to determine overall SDG “impact intensity.” Ideally, SDGIIM can be applied to textual data sets from any sector or discipline: academia, business, government, non-profit, civil society, etc. Third, the SDGIIM instantiation is applied to the specific domain of academic journal rating systems as a case study. Traditionally, academic journals have been evaluated on loosely conceived and empirically shaky notions of ‘quality.’ Aligned with the trend of AI4SG and broader calls to action, ‘impact’ is rapidly becoming the primary normative consideration for assessing academic journals. We hypothesize and demonstrate that SDGIIM is capable of producing evaluations aligned with experts’ expectations of SDG impact intensity; the consistent analysis and rating of textual data sets that embody the SDGs with varying degrees of meaning and, ultimately, promote positive impact on the actual material conditions of the world. (shrink)

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial (...) contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed. (shrink)

Automated influence is one of the most pervasive applications of artificial intelligence in our day-to-day lives, yet a thoroughgoing account of its associated individual and societal harms is lacking. By far the most widespread, compelling, and intuitive account of the harms associated with automated influence follows what I call the control argument. This argument suggests that users are persuaded, manipulated, and influenced by automated influence in a way that they have little or no control over. Based on evidence about (...) the effectiveness of targeted advertising as well as empirical results about the nature of attentional control, I provide reasons to reject this argument. In turn, I use C. Thi Nguyen’s theory of value collapse to develop a new account of the harmfulness of automated influence. (shrink)

The EU AI Act is the first step toward a comprehensive legal framework for AI. It introduces provisions for AI systems based on their risk levels in relation to fundamental rights. Providers of AI systems must conduct Conformity Assessments before market placement. Recent amendments added Fundamental Rights Impact Assessments for high-risk AI system users, focusing on compliance with EU and national laws, fundamental rights, and potential impacts on EU values. The paper suggests that automating business process compliance can help (...) standardize these assessments and outlines some methodological guidelines. (shrink)

In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and artificial intelligence research have largely overlooked this aspect. To bridge this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model (LLM) Enhanced PARIS (LE-PARIS). These systems are designed to enhance the efficiency of patent attorneys in handling OA responses through collaboration with AI. The systems’ key features include (...) the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. To validate the effectiveness of the systems, we have employed a multi-paradigm analysis using the USPTO Office Action database and longitudinal data based on attorney interactions with our systems over six years. Through five studies, we have examined the constructiveness of OA topics (studies 1 and 2) using topic modeling and our proposed Delphi process, the efficacy of our proposed hybrid LLM-based recommender system tailored for OA responses (study 3), the quality of generated responses (study 4), and the systems’ practical value in real-world scenarios through user studies (study 5). The results indicate that both PARIS and LE-PARIS significantly achieve key metrics and have a positive impact on attorney performance. (shrink)

Rapid advances in AI-based automation have led to a number of existential and economic concerns. In particular, as automating technologies develop enhanced competency they seem to threaten the values associated with meaningful work. In this article, we focus on one such value: the value of achievement. We argue that achievement is a key part of what makes work meaningful and that advances in AI and automation give rise to a number achievement gaps in the workplace. This could (...) limit people’s ability to participate in meaningful forms of work. Achievement gaps are interesting, in part, because they are the inverse of the (negative) responsibility gaps already widely discussed in the literature on AI ethics. Having described and explained the problem of achievement gaps, the article concludes by identifying four possible policy responses to the problem. (shrink)

Efforts for better services are achieved by small steps such as analyzing data of the customer. What is significant for the customer should as well significant for the banking institution. Transparency and a better understand- ding of the pattern behavior of customers can be used for the good of both partners such as good relationships in the fu- ture eventually be beneficial for the customer as well as a banking institution. The responsibility of both sides is crucial to (...) understand the accountability of customers and banking institutions. The method of identification of user messages of the banking application proposed in the article involves the use of user data for information processing, taking into account the peculiarities of the use of mobile devices and the user's dialogue with bank messages. Also, the proposed method allows you to rank messages to identify the most important messages and get the desired result by providing ef- fective recommendations in favor of each of the participants in customer interaction with the bank. The introduction of modern educational programs "Information Control Systems and Technologies", "Artificial Intelligence Systems" and "Systems Analysis" in the field of information technology, allows users and managers to interact with the bank's custo- mers sufficient information to make informed recommendations for effective management decisions. The article consi- ders the conceptual model of interaction of users and managers on interaction with users of the bank, use of technolo- gies and algorithms of artificial intelligence, machine learning processes to formalize the process of dialogue, systema- tization and ranking of messages and notifications between customers and managers. The Conceptual model of inter- action of the user of banking services with messages is presented. The article also describes the features of the dialogue between the user of banking services and the manager for the implementation of algorithms for interaction with custo- mers. The example of the city block of bank users considers and takes into account the difference in the amount of in- formation received by the bank, which must be sent during different periods of the week and take into account the amount of information to be sent, which will be significantly less and, consequently, the number of necessary services. will also be smaller. In this example, taking into account the amount of information that can be consumed during differ- rent periods of the week, the number of services that can be provided to the user will also be much smaller. The reflec- tion of such interactions in the model is an important aspect, as noted in the article. (shrink)

Healthcare data warehousing test automation is becoming a success through the help of AI-based technology that results in accuracy, efficiency, and data integrity on the automated test. The one central to personalized patient data that forms the core of traditional data warehousing solutions frequently faces problems of complexity and dissatisfaction. Deep learning with test automation solutions makes data ingestion, processing, and testing conversant through machine learning algorithms [1]. These systems encompass separate acts such as testing for data (...) integration, testing the quality of data as well as performance testing removing the factors of time and human error. In healthcare organizations, this approach delivers the message of enhancing quality, compliance and organizational efficiency in value-based care and precision medicine initiatives. As such, artificial intelligence tools can be used to address the essential issues regarding data warehousing, and this can in turn enhance decision-making, and thus, enhance the health of patient and the overall population. (shrink)

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also (...) identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology. (shrink)

The use of predictive machine learning algorithms is increasingly common to guide or even take decisions in both public and private settings. Their use is touted by some as a potentially useful method to avoid discriminatory decisions since they are, allegedly, neutral, objective, and can be evaluated in ways no human decisions can. By (fully or partly) outsourcing a decision process to an algorithm, it should allow human organizations to clearly define the parameters of the decision and to, in principle, (...) remove human biases. Yet, in practice, the use of algorithms can still be the source of wrongful discriminatory decisions based on at least three of their features: the data-mining process and the categorizations they rely on can reconduct human biases, their automaticity and predictive design can lead them to rely on wrongful generalizations, and their opaque nature is at odds with democratic requirements. We highlight that the two latter aspects of algorithms and their significance for discrimination are too often overlooked in contemporary literature. Though these problems are not all insurmountable, we argue that it is necessary to clearly define the conditions under which a machine learning decision tool can be used. We identify and propose three main guidelines to properly constrain the deployment of machine learning algorithms in society: algorithms should be vetted to ensure that they do not unduly affect historically marginalized groups; they should not systematically override or replace human decision-making processes; and the decision reached using an algorithm should always be explainable and justifiable. (shrink)

The debate about the impact and desirability of automizing labor is one that emerges alongside every major technological development. In recent years, attention for the potential ethical and social implications of automizing labor increased once again, in response to the successes and promises of artificial intelligence (AI) and other modern technologies. The aim of this article is, first and foremost, to introduce the reader to recent work on the desirability of automizing labor. The article distinguishes two philosophical debates about replacing (...) human jobs with AI systems or robots. The first debate has to do with questions of distributive justice, in light of the prospect of structural technological unemployment. The second debate has to do with questions about the relation between a job and a meaningful life. In addition to sketching these debates, the article highlights environmental concerns regarding automation as a possible future direction for research on the future of work. (shrink)

This study investigates the impact of bank digitalization on capital reallocation using the adoption of digital lending platforms and a difference-in-differences design. We find that digital transformation in banking facilitates the reallocation of capital from higher-risk to lower-risk sectors. This effect is particularly pronounced in regions with high levels of government influence and in city and rural commercial banks, which are more susceptible to government intervention. Our findings suggest that banks that implement digital platforms are better positioned to (...) resist political pressures, as the data-driven, automated decision-making processes make it difficult to justify risky, politically motivated loans, while increased transparency limits local government influence over credit allocation. The effects of digitalization on capital reallocation are further amplified in banks with opaque information environments, weak internal control, and high credit risks. Additional analyses show that the adoption of digital lending platforms improves the timeliness of loan loss recognition, reduces bank risk, and enhances operating performance. This study contributes to the literature on digitalization by highlighting its role in improving capital allocation in the banking sector and offers insights into how technological advancements can mitigate the risks posed by political intervention and high-risk lending. (shrink)

This chapter examines the role of shopfloor knowledge in the operation of advanced manufacturing systems. Design trends towards full automation are contrasted with those toward hybrid, human-centred systems with particular emphasis on job design and the development and reproduction of knowledge. The chapter concludes with a short discussion of the problems inherent in hybrid design.

A great deal of theorizing has emerged about the economic ramifications of increased automation. However, significantly less attention has been paid to the potential effects of AI-driven occupational replacement on less measurable metrics—in particular, what it feels like to be replaced. In politics, we see examples of nation-states and extremist groups invoking the concept of replacement as a motivator for political action, unrest, and, at times, violence. In the realm of workplace automation, and in particular, in the case (...) of AI-driven workplace automation, the replacement of human labor with artificial labor is an explicit goal. In this paper, we suggest that, given the effects that the experience of a sense of replacement has in political contexts and the potential for that sense of replacement to motivate unrest and violence, we should be concerned about the widely predicted replacement of workers over the coming decades beyond the potential economic challenges which may arise. (shrink)

We claim the climate crisis is the proper baseline for establishing the terms of debate about the viability of a post-work future. In this paper, we aim to assess the viability of a post-work future in which automation replaces a significant portion of human labor. We do this by laying out the possible outcomes of what such a future will look like based on three related axes: technological capacity, politics and social distribution, and alternative conceptions of the good. The (...) purpose in tracking these outcomes is to have a clear picture of those outcomes we should be pursuing in contrast to those we ought to avoid. Put another way, to achieve a post-work future that is egalitarian in character will require ensuring that technological capacity is used to improve the prospects for all humanity. The argument proceeds along the following lines. First, we summarize the scientific consensus about the causes and consequences of climate change, as well as the tools of mitigation and adaptation it recommends. The causes are primarily rooted in the global system of production and consumption that has developed over the last century. We refer to this pattern of development as the “Growth Model” since it primarily relies on energy created from burning fossil fuels and the exponential consumption of resources to promote more growth (Rose 2020). Second, we construct a range of possible outcomes regarding the climate crisis that are based on different trajectories of development. We do so by examining each of two cases located on the poles of three different axes. These axes include technology, political organization and social distribution, and conceptions of the good, yielding eight possible outcomes. The point of considering these cases and how they intersect along these axes is two-fold: to clarify the features that define them and compare their merits and drawbacks, enabling us to formulate the means for avoiding some outcomes and strive for others. Third, we then consider the viability of a post-work future in terms of these outcomes. Some of these outcomes point in a direction where automation replaces labor, and its benefits are shared widely. Others indicate a future in which some groups benefit at the expense of others, while some appear to preclude the emergence of such a future altogether. Finally, we conclude with some observations about the viability of a post-work future given our evaluation of these outcomes. (shrink)

Purpose The aim of this research is to conduct a systematic review of the literature on responsible artificial intelligence (RAI) practices within the domain of AI-based Credit Scoring (AICS) in banking. This review endeavours to map the existing landscape by identifying the work done so far, delineating the key themes and identifying the focal points of research within this field. Design/methodology/approach A database search of Scopus and Web of Science (last 20 years) resulted in 377 articles. This was further (...) filtered for ABDC listing, and augmented with manual search. This resulted in a final list of 53 articles which was investigated further using the TCCM (Theory, Context, Characteristics and Methodology) review protocol. Findings The RAI landscape for credit scoring in the banking industry is multifaceted, encompassing ethical, operational and technological dimensions. The use of artificial intelligence (AI) in banking is widespread, aiming to enhance efficiency and improve customer experience. Based on the findings of the systematic literature review we found that past studies on AICS have revolved around four major themes: (a) Advances in AI technology; (b) Ethical considerations and fairness; (c) Operational challenges and limitations; and (d) Future directions and potential applications. The authors further propose future directions in RAI in credit scoring. Originality/value Earlier studies have focused on AI in banking, credit scoring in isolation. This review attempts to provide deeper insights, facilitating the development of this key field. (shrink)

Responding to long-standing warnings that robots and AI will enslave humans, I argue that the main problem we face is not that automation might turn us into slaves but, rather, that we remain masters. First I construct an argument concerning what I call ‘the tragedy of the master’: using the master–slave dialectic, I argue that automation technologies threaten to make us vulnerable, alienated, and automated masters. I elaborate the implications for power, knowledge, and experience. Then I critically discuss (...) and question this argument but also the very thinking in terms of masters and slaves, which fuels both arguments. I question the discourse about slavery and object to the assumptions made about human–technology relations. However, I also show that the discussion about masters and slaves attends us to issues with human–human relations, in particular to the social consequences of automation such as power issues and the problem of the relation between automation and (un)employment. Finally, I reflect on how we can respond to our predicament, to ‘the tragedy of the master’. (shrink)

The intention of this paper is to point to the dilemma humanity may face in light of AI advancements. The dilemma is whether to create a world with less evil or maintain the human status of moral agents. This dilemma may arise as a consequence of using automated decision-making systems for high-stakes decisions. The use of automated decision-making bears the risk of eliminating human moral agency and autonomy and reducing humans to mere moral patients. On the other hand, it (...) also has the potential to bring tremendous benefits to humanity by decreasing human-induced harm in the world. After presenting how this dilemma may arise, I explore general avenues for addressing it. I will argue that we do not have to solve this dilemma in an all-or-nothing fashion and that a more nuanced approach may be suitable. However, the main point I want to highlight is that we need to have a principled way of addressing this dilemma, which is currently missing. (shrink)

Algorithmic decision making is used in an increasing number of fields. Letting automated processes take decisions raises the question of their accountability. In the field of computational journalism, the algorithmic accountability framework proposed by Diakopoulos formalizes this challenge by considering algorithms as objects of human creation, with the goal of revealing the intent embedded into their implementation. A consequence of this definition is that ensuring accountability essentially boils down to a transparency question: given the appropriate reverse-engineering tools, it should (...) be feasible to extract design criteria and to identify intentional biases. General limitations of this transparency ideal have been discussed by Ananny and Crawford :973–989, 2018). We further focus on its technical limitations. We show that even if reverse-engineering concludes that the criteria embedded into an algorithm correspond to its publicized intent, it may be that adversarial behaviors make the algorithm deviate from its expected operation. We illustrate this issue with an automated news recommendation system, and show how the classification algorithms used in such systems can be fooled with hard-to-notice modifications of the articles to classify. We therefore suggest that robustness against adversarial behaviors should be taken into account in the definition of algorithmic accountability, to better capture the risks inherent to algorithmic decision making. We finally discuss the various challenges that this new technical limitation raises for journalism practice. (shrink)

Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding (...) their niche to replace some job loss in other sectors? In this paper, we compare the problems created by the current mass production economy and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human–machine production at the microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation. (shrink)

Automated Influence is the use of Artificial Intelligence to collect, integrate, and analyse people’s data in order to deliver targeted interventions that shape their behaviour. We consider three central objections against Automated Influence, focusing on privacy, exploitation, and manipulation, showing in each case how a structural version of that objection has more purchase than its interactional counterpart. By rejecting the interactional focus of “AI Ethics” in favour of a more structural, political philosophy of AI, we show that the real problem (...) with Automated Influence is the crisis of legitimacy that it precipitates. (shrink)

The Automated Self explores meta-theoretical issues in the philosophy of artificial intelligence, combining it with themes from philosophy of science and technology, media and communication studies, and ethics. Balsemão-Pires provides an integrated view of contemporary problems of AI including the theoretical premises and discussions on the meaning, functioning and social uses of cognitive machines, the recent ethical and legal challenges on privacy, interpretability, and data ownership, passing through a careful discussion of the media embedment of the new technologies, and (...) the uses of algorithms in the production and consumption of art. (shrink)

Standpoint logic is a recently proposed formalism in the context of knowledge integration, which advocates a multi-perspective approach permitting reasoning with a selection of diverse and possibly conflicting standpoints rather than forcing their unification. In this paper, we introduce nested sequent calculi for propositional standpoint logics---proof systems that manipulate trees whose nodes are multisets of formulae---and show how to automate standpoint reasoning by means of non-deterministic proof-search algorithms. To obtain worst-case complexity-optimal proof-search, we introduce a novel technique in the (...) context of nested sequents, referred to as "coloring," which consists of taking a formula as input, guessing a certain coloring of its subformulae, and then running proof-search in a nested sequent calculus on the colored input. Our technique lets us decide the validity of standpoint formulae in CoNP since proof-search only produces a partial proof relative to each permitted coloring of the input. We show how all partial proofs can be fused together to construct a complete proof when the input is valid, and how certain partial proofs can be transformed into a counter-model when the input is invalid. These "certificates" (i.e. proofs and counter-models) serve as explanations of the (in)validity of the input. (shrink)

With the advent of automated decision-making, governments have increasingly begun to rely on artificially intelligent algorithms to inform policy decisions across a range of domains of government interest and influence. The practice has not gone unnoticed among philosophers, worried about “algocracy”, and its ethical and political impacts. One of the chief issues of ethical and political significance raised by algocratic governance, so the argument goes, is the lack of transparency of algorithms. One of the best-known examples of philosophical analyses (...) of algocracy is John Danaher’s “The threat of algocracy”, arguing that government by algorithm undermines political legitimacy. In this paper, I will treat Danaher’s argument as a springboard for raising additional questions about the connections between algocracy, comprehensibility, and legitimacy, especially in light of empirical results about what we can expect the voters and policymakers to know. The paper has the following structure: in Sect. 2, I introduce the basics of Danaher’s argument regarding algocracy. In Sect. 3 I argue that the algocratic threat to legitimacy has troubling implications for social justice. In Sect. 4, I argue that, nevertheless, there seem to be good reasons for governments to rely on algorithmic decision support systems. Lastly, I try to resolve the apparent tension between the findings of the two preceding Sections. (shrink)

Healthcare is becoming increasingly automated with the development and deployment of care robots. There are many benefits to care robots but they also pose many challenging ethical issues. This paper takes care robots for the elderly as the subject of analysis, building on previous literature in the domain of the ethics and design of care robots. Using the value sensitive design approach to technology design, this paper extends its application to care robots by integrating the values of care, values (...) that are specific to AI, and higher-scale values such as the United Nations Sustainable Development Goals. The ethical issues specific to care robots for the elderly are discussed at length alongside examples of specific design requirements that work to ameliorate these ethical concerns. (shrink)

Purpose Artificial intelligence and automation are currently changing human life with a great implication in the communication field. This research focusses on understanding the current and growing impact of AI and automation in the role of communication professionals to identify what skills and training are needed to face its impacts leading to a recommendation. Design/methodology/approach The research involves methodological triangulation, analysing and comparing data gathered from consulting with experts using the Delphi method, focus group with (...) communication students, and literature review. Findings Findings show that the likely impacts are on the one hand the enhancing of efficiency and productivity, as well as freeing communication professionals to focus on the creative side, strategy and analytical thinking, on the other hand, repetitive and low-level jobs could be lost, being higher position jobs or those involving creativity and decision making harder to automate. Two types of training are needed: to gather experience with the current AI and automated tools, and to focus on developing human qualities that AI cannot replicate. Originality/value The outcomes of this research are valuable to help current and future communication practitioners, as well as organisations, to be one step ahead and survive the age AI and automation, being aware of its current and near-future impacts. The paper offers a list of recommended soft and technical skills, as well as training needed, categorizing them in low, medium and high priority. (shrink)

It is often argued (and feared) that the human capacity to pay attention is being transformed by computational technologies. Are computing machines distraction machines? This article takes this question as its starting point in order to address concerns about attention deficits vis-à-vis questions and issues about the mechanisation of cognitive procedures. I will claim that, when approaching the attention ecology of the twenty-first century, it is necessary to differentiate between augmentation and automation. While augmentation implies the extension of predefined (...) forms or modes of behaviour, contemporary developments in computational automation ask us instead to consider the possibility of moving beyond phenomenological analogies. The article will thus discuss how transformations in the capacity to pay attention in a computational age need to be analysed in relation to the emergence of quasi-autonomous artificial cognitive agents driven by AI technologies, such as those known as machine learning. I will argue that these artificial cognitive agents can no longer be described in terms of technological add-ons to pre-existing human cognitive capacities. Today, we think alongside machines that are, is a sense, already thinking. Similarly, we pay attention alongside machines that are, in a sense, already paying attention. The challenge for philosophy and cultural theory is that of moving beyond ‘projectionist’ conceptions of such technological agency. This challenge, however, also involves overcoming the anthropomorphism that is implicit in expression such as ‘thinking machines’. In a century where robot-to-robot communications have outpaced and outnumbered human-machine interactions, these artificial cognitive agents are not just reframing the human capacity to pay attention: they are also re-structuring the conditions for such capacity. Addressing the conditions for attention beyond augmentation and vis-à-vis computational automation involves considering the role and scope of both human and algorithmic decision-making, and engaging with the ways in which the humanities can intervene upon contemporary complex cognitive scenarios. (shrink)

Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) (...) help organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a ‘soft’ yet ‘formal’ governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame AMDS as parts of larger sociotechnical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should, therefore, be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS. (shrink)

We continuously talk about autonomous technologies. But how can words qualifying technologies be the very same words chosen by Kant to define what is essentially human, i.e. being autonomous? The article focuses on a possible answer by reflecting upon both etymological and philosophical issues, as well as upon the case of autonomous vehicles. Most interestingly, on the one hand, we have the notion of “autonomy”, meaning that there is a “law” that is “self-given”, and, on the other hand, we have (...) the notion of “automation”, meaning that there is something “offhand” that is “self-given”. Yet, we are experiencing a kind of twofold shift: on the one hand, the shift from defining technologies in terms of automation to defining technologies in terms of autonomy and, on the other hand, the shift from defining humans in terms of autonomy to defining humans in terms of automation. From a philosophical perspective, the shift may mean that we are trying to escape precisely from what autonomy founds, i.e. individual responsibility of humans that, in the Western culture, have been defined for millennia as rational and moral decision-makers, even when their decisions have been the toughest. More precisely, the shift may mean that we are using technologies, and in particular emerging algorithmic technologies, as scapegoats that bear responsibility for us by making decisions for us. Moreover, if we consider the kind of emerging algorithmic technologies that increasingly surround us, starting from autonomous vehicles, then we may argue that we also seem to create a kind of technological divine that, by being always with us through its immanent omnipresence, omniscience, omnipotence and inscrutability, can always be our technological scapegoat freeing us from the most unbearable burden of individual responsibility resulting from individual autonomy. (shrink)

In this paper, we explore and describe what is needed to allow connected and automated vehicles to break traffic rules in order to minimise road safety risk and to operate with appropriate transparency. Reviewing current traffic rules with particular reference to two driving situations, we illustrate why current traffic rules are not suitable for CAVs and why making new traffic rules specifically for CAVs would be inappropriate. In defining an alternative approach to achieving safe CAV driving behaviours, we (...) describe the use of ethical goal functions as part of hybrid AI systems, suggesting that functions should be defined by governmental bodies with input from citizens and stakeholders. Ethical goal functions for CAVs would enable developers to optimise driving behaviours for safety under conditions of uncertainty whilst allowing for differentiation of products according to brand values. Such functions can differ between regions according to preferences for safety behaviours within that region and can be updated over time, responding to continual socio-technological feedback loops. We conclude that defining ethical goal functions is an urgent and necessary step from governmental bodies to enable the safe and transparent operation of CAVs and accelerate the reduction in road casualties they promise to achieve. (shrink)

The mental health crisis and loneliness epidemic have sparked a growing interest in leveraging artificial intelligence (AI) and chatbots as a potential solution. This report examines the benefits and risks of incorporating chatbots in mental health treatment. AI is used for mental health diagnosis and treatment decision-making and to train therapists on virtual patients. Chatbots are employed as always-available intermediaries with therapists, flagging symptoms for human intervention. But chatbots are also sold as stand-alone virtual therapists or as friends and (...) lovers. There is evidence that these uses of AI and chatbots can provide better quality service, improve accessibility, and lower costs. The systems can reduce the stigma and shame of sharing their problems and leverage a mass of biometric and behavioral data to supplement self-reports. As the systems' intelligence rapidly improves, they will need to be rigorously tested for the accuracy and precision of their diagnoses and the quality of their interactions with patients. As chatbots become indistinguishable from humans and leverage their superhuman capacity to detect affect and draw on knowledge of a patient’s life, patients will be drawn to attribute personality to and relationship with the chatbot. Consequently, it will be essential to study what the “therapeutic alliance” with an actual human counselor provides and the risks of patients attributing such a relationship to a one-sided or “parasocial” relationship. (shrink)

The paper demonstrates that social alignment is distinct from value alignment as it is currently understood in the AI safety literature, and argues that social alignment is an important research agenda. Work provides an important example for the argument, since work is a cooperative endeavor, and it is part of the larger manifold of social cooperation. These cooperative aspects of work are individually and socially valuable, and so they must be given a central place when evaluating the impact of AI (...) upon work. Workplace technologies are not simply instruments for achieving productive goals, but ways of mediating interpersonal relations. They are aspects of a cooperative interface i.e. the infrastructure by which we engage cooperative behavior with others. The concept of the cooperative interface suggests two conjectures to foreground in the social alignment agenda, motivated by the experience of algorithmic trading and social robotics: that AI impacts cooperation through its effects on social networks, and through its effects on social norms. (shrink)

Large language models and other generative artificial intelligence systems are achieving increasingly impressive results, though the quality of those results still seems dull and uninspired. This paper argues that this poor quality can be linked to the philosophical notion of inauthenticity as presented by Kierkegaard, Nietzsche, and Heidegger, and that this inauthenticity is fundamentally grounded in the design and structure of such systems by virtue of the way they statistically level down the materials on which they are trained. Although it (...) seems possible to create the conditions for authenticity in these systems, the resulting authenticity would be grounded in machine intelligence, not human intelligence. The argument extends the criticisms of artificial intelligence articulated by Hubert Dreyfus, updated to account for recent developments in machine learning and artificial neural networks. While more optimistic concerning the prospects for successfully creating artificial intelligence than Dreyfus had been, this paper argues that the resulting intelligence may not align well with human intelligence and may not be desirable for humans, if it is fully authentic. (shrink)

Science is being transformed by the increasing capabilities of automation technologies and artificial intelligence (AI). Integrating AI and machine learning (ML) into scientific practice requires changing established research methods while maintaining a scientific understanding of research findings. Researchers are at the forefront of this change, but there is currently little understanding of how they are experiencing these upheavals in scientific practice. In this paper, we examine how researchers working in several research fields (automation engineering, computational design, conservation decision-making, (...) materials science, and synthetic biology) perceive AI/ML technologies used in their work, such as laboratory automation, automated design of experiments, computational design, and computer experiments. We find that researchers emphasised the need for AI/ML technologies to have practical benefits (such as efficiency and improved safety) to justify their use. Researchers were also hesitant to automate data analysis, and the importance of explainability differed between researchers working with laboratory automation and those using AI/ML directly in their research. This difference is due to the different role AI/ML plays in different research fields: laboratory automation performs processes already defined by the researcher and the actions are visible or recorded, while in AI/ML applications the decisions that produced the result may be obscure to the researcher. Understanding the role AI/ML plays in scientific practice is important for ensuring that scientific knowledge continues to grow. (shrink)

As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; examines some (...) of the existing automated tools used by major platforms to handle copyright infringement, terrorism and toxic speech; and identifies key political and ethical issues for these systems as the reliance on them grows. Recent events suggest that algorithmic moderation has become necessary to manage growing public expectations for increased platform responsibility, safety and security on the global stage; however, as we demonstrate, these systems remain opaque, unaccountable and poorly understood. Despite the potential promise of algorithms or ‘AI’, we show that even ‘well optimized’ moderation systems could exacerbate, rather than relieve, many existing problems with content policy as enacted by platforms for three main reasons: automated moderation threatens to further increase opacity, making a famously non-transparent set of practices even more difficult to understand or audit, further complicate outstanding issues of fairness and justice in large-scale sociotechnical systems and re-obscure the fundamentally political nature of speech decisions being executed at scale. (shrink)

In this article, I discuss the implications of generative AI in future of creative work in the music industries through an ethnographic study of music technologists in Melbourne, Australia. Drawing on visual ethnography methods, I explore how researchers, developers, as well as professional and aspiring artists, across sectors in Melbourne’s ecosystem, value the role of mistakes, glitches and uncertainty in the creative process with music technology. This stands in opposition to the controlled processes within generative AI models, based on (...) the management of uncertainty as an input value. Thus, I reframe the concept of creative uncertainty which emerges as a shaping force in the creative process, and in opposition to the controlled outputs of generative AI. Furthermore, this article shows the limitations and misalignment of dominant visions about the impact of generative AI, on creative workflows and practices, by highlighting the experiences, motivations and expectations of the research participants in this multi-sited ethnography. (shrink)

When Lisanne Bainbridge wrote about counterintuitive consequences of the increasing human–machine interaction, she concentrated on the resulting issues for system performance, stability, and safety. Now, decades later, however, the automized work environment is substantially more pervasive, sophisticated, and interactive. Current advances in machine learning technologies reshape the value, meaning, and future of the human workforce. While the ‘human factor’ still challenges automation system architects, inconspicuously new ironic settings have evolved that only become distinctly evident from a human-centered perspective. This (...) brief essay discusses the role of the human workforce in human–machine interaction as machine learning continues to improve, and it points to the counterintuitive insight that although the demand for blue-collar workers may decrease, exactly this labor class increasingly enters more privileged working domains and establishes itself henceforth as ‘blue collar with tie.’. (shrink)

We perform a mixed-method frame semantics-based analysis on a dataset of more than 49,000 sentences collected from 5846 news articles that mention AI. The dataset covers the twelve-month period centred around the launch of OpenAI’s chatbot ChatGPT and is collected from the most visited open-access English-language news publishers. Our findings indicate that during the six months succeeding the launch, media attention rose tenfold—from already historically high levels. During this period, discourse has become increasingly centred around experts and political leaders, and (...) AI has become more closely associated with dangers and risks. A deeper review of the data also suggests a qualitative shift in the types of threat AI is thought to represent, as well as the anthropomorphic qualities ascribed to it. (shrink)

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (...) (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that (...) AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

Professor Vuori's paper, first presented at the fourth Medico-legal Conference in Prague in the spring of this year, deals with the problem of the maintenance of confidentiality in computerized health records. Although more and more information is required, the hardware of the computer systems is so sophisticated that it would be very expensive indeed to 'break in' and steal from a modern data bank. Those concerned with programming computers are becoming more aware of their responsibilities concerning confidentiality and (...) privacy, to the extent that a legal code of ethics for programmers is being formulated. They are also aware that the most sensitive of all relationships--the doctor-patient relationship--could be in danger if they failed to maintain high standards of integrity. An area of danger is where administrative boundaries between systems must be crossed--say between those of health and employment. Protection of privacy must be ensured by releasing full information about the type of data being stored, and by maintaining democratic control over the establishment of information systems. (shrink)

The ethics of autonomous cars and automated driving have been a subject of discussion in research for a number of years :28–58, 2016). As levels of automation progress, with partially automated driving already becoming standard in new cars from a number of manufacturers, the question of ethical and legal standards becomes virulent. For exam-ple, while automated and autonomous cars, being equipped with appropriate detection sensors, processors, and intelligent mapping material, have a chance of being much safer than (...) human-driven cars in many regards, situations will arise in which accidents cannot be completely avoided. Such situations will have to be dealt with when programming the software of these vehicles. In several instances, internationally, regulations have been passed, based on legal considerations of road safety, mostly. However, to date, there have been few, if any, cases of a broader ethics code for autonomous or automated driving preceding actual regulation and being based on a broadly composed ethics committee of independent experts. In July 2016, the German Federal Minister of Transport and Digital Infrastructure, Alexander Dobrindt, appointed a national ethics committee for automated and connected driving, which began its work in September 2016. In June 2017, this committee presented a code of ethics which was published in German and in English. It consists of 20 ethical guidelines. Having been a member of this committee, I will present the main ethical topics of these guidelines and the discussions that lay behind them. (shrink)

In the past, the conventional concentration of Internet banking (IB) research has been on technology development, but this is now shifting to user-focused research. It has been suggested that potential users of IB services in Malaysia may not adopt the system even if they are available, due to their perceptions of this application and their level of confidence in using it to solve their banking needs. This study therefore employs the extended technology acceptance model as the theoretical framework (...) for assessing the influences of perceived usefulness, perceived ease of use, perceived reliability, and computer self-efficacy on IB adoption in Malaysia. This study scrutinizes the moderation effect of computer self-efficacy on perceptions–intention relationships. Based on data from a sample of 133 Internet-using bank customers collected through a field survey, it was found that perceived usefulness and perceived ease of use were strong determinants of behavioral intention to adopt IB, while perceived reliability has no direct relationship with intention. Computer self-efficacy significantly moderates the relationship of perceived usefulness and perceived ease of use with intention. (shrink)

Analysing how news media portray A.I. reveals what interpretative frameworks around the technology circulate in public discourses. This allows for critical reflections on the making of meaning in prevalent narratives about A.I. and its impact. While research on the public perception of datafication and automation is growing, only a few studies investigate news framing practices. The present study connects to this nascent research area by charting A.I. news frames in four internationally renowned media outlets: The New York Times, The (...) Guardian, Wired, and Gizmodo. The main goals are to identify dominant emphasis frames in AI news reporting over the past decade, to explore whether certain A.I. frames are associated with specific data risks, and what journalists and experts contribute to the media discourse. An automated content analysis serves for inductive frame detection, identification of risk references, and network analysis of news writers. The results show how A.I.’s ubiquity emerged rapidly in the mid-2010s, and that the news discourse became more critical over time. It is further argued that A.I. news reporting is an important factor in building critical data literacy among lay audiences. (shrink)

This study examines the role of blockchain technology in promoting sustainability in Bangladesh’s banking sector. Using Structural Equation Modeling (SEM), it explores the factors influencing blockchain adoption, including Perceived Ease of Use (PEU), Perceived Usefulness (PU), Perceived Compatibility (PC), Perceived External Variables (PEV), and Intention to Use (IBT). Sustainability (SB) is the ultimate outcome variable to assess the long-term impact of blockchain-driven practices. Data were collected from 380 banking professionals across private and public banks. The results show that (...) PEU, PU, PC, and PEV significantly influence IBT, which strongly impacts SB. PEU and PU emerged as the most important predictors of IBT, emphasizing the role of usability and perceived benefits. Path coefficient analysis confirmed the significance of both direct and indirect effects, with IBT acting as a key mediator. The findings highlight blockchain’s potential to enhance transparency, accountability, and efficiency while supporting sustainable development goals. The study also stresses the importance of aligning blockchain with organizational practices and addressing external factors like infrastructure and social influence. This research provides actionable insights for policymakers and banking institutions, recommending user-friendly blockchain systems, regulatory support, and targeted incentives to foster blockchain adoption for sustainability. (shrink)

The literature on ethics and user attitudes towards AVs discusses user concerns in relation to automation; however, we show that there are additional relevant issues at stake. To assess adolescents’ attitudes regarding the ‘car of the future’ as presented by car manufacturers, we conducted two studies with over 400 participants altogether. We used a mixed methods approach in which we combined qualitative and quantitative methods. In the first study, our respondents appeared to be more concerned about other aspects (...) of AVs than automation. Instead, their most commonly raised concerns were the extensive use of AI, recommender systems, and related issues of autonomy, invasiveness and personal privacy. The second study confirmed that several AV impacts were negatively perceived. The responses were, however, ambivalent. This confirms previous research on AV attitudes. On one hand, the AV features were perceived as useful, while on the other hand, their impacts were negatively assessed. We followed theoretical insights form futures studies and responsible research and innovation, which helped to identify that there are additional user concerns than what has been previously discussed in the literature on public attitudes and ethics of AVs, as well what has been envisioned by car manufactures. (shrink)

Automation is inherently tied to ethical challenges because of its potential involvement in morally loaded decisions. In the present research, participants (n = 34) took part in a moral multi-trial dilemma-based task where the agent (human vs. machine) and the behavior (action vs. inaction) factors were randomized. Self-report measures, in terms of morality, consciousness, responsibility, intentionality, and emotional impact evaluation were gathered, together with electroencephalography (delta, theta, beta, upper and lower alpha, and gamma powers) and peripheral autonomic (electrodermal (...) activity, heart rate variability) data. Data showed that moral schemata vary as a function of the involved decider, and when the agent and behavior factors are crossed. Subjects did not consider machines full moral deciders to the same degree as humans and tend to morally better accept human active behavior and machine inaction. Moreover, the autonomic physiological activity might support the a-posteriori moral evaluation. In the evaluation of the agent’s consciousness, a beta ventrolateral prefrontal synchronization was detected for human action and machine inaction, while a generalized gamma synchronization occurred in artificial agent trials while rating the emotional impact of the decider’s behavior. The detected differences might point to a potential explicit and implicit asymmetry in moral reasoning toward artificial and human agents. (shrink)

The escalating number of pending cases is a growing concern worldwide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. To enhance the effectiveness of automatically extracting these attributes (...) from legal documents within a sequence labeling framework, we propose the utilization of a few-shot learning approach based on Large Language Models (LLMs). Moreover, we demonstrate the efficacy of the extracted attributes in downstream tasks, such aslegal judgment prediction and legal statute prediction. (shrink)