Mark Ryan

In animal farming, there is the hope that artificial intelligence (AI) will improve efficiency and increase profits while providing solutions to reduce pollution and pesticide use and improve environmental sustainability, animal health and welfare. However, many are also concerned about AI’s ethical, legal, social, and economic impacts. These include the instrumentalisation of animals, bias caused by AI in how animals are portrayed, allowing the continuation of a harmful farming industry, and concerns around power asymmetries, data ownership, and copyright infringements. Therefore, there is a tension between the potential benefits and drawbacks of AI use in animal farming. This paper takes a forward-looking view of the benefits and challenges that AI may create in animal farming by the year 2032. Through several iterative rounds with stakeholders, this paper maps out a future scenario of AI in animal farming, identifying technological developments alongside potential drivers, barriers, and impacts. The scenario concludes with five recommendations for policymakers: 1. Initiate education programmes on AI in the sector; 2. Create ethical guidelines for AI in animal farming; 3. Science policy should be realistic and not only rely on technical solutions like AI; 4. Ensure public safety from harm caused by AI; 5. Implement better guidance on data-sharing in the sector.

Self-driving vehicles (SDVs) offer great potential to improve efficiency on roads, reduce traffic accidents, increase productivity, and minimise our environmental impact in the process. However, they have also seen resistance from different groups claiming that they are unsafe, pose a risk of being hacked, will threaten jobs, and increase environmental pollution from increased driving as a result of their convenience. In order to reap the benefits of SDVs, while avoiding some of the many pitfalls, it is important to effectively determine what challenges we will face in the future and what steps need to be taken now to avoid them. The approach taken in this paper is the construction of a likely future (the year 2025), through the process of a policy scenario methodology, if we continue certain trajectories over the coming years. The purpose of this is to articulate issues we currently face and the construction of a foresight analysis of how these may develop in the next 6 years. It will highlight many of the key facilitators and inhibitors behind this change and the societal impacts caused as a result. This paper will synthesise the wide range of ethical, legal, social and economic impacts that may result from SDV use and implementation by 2025, such as issues of autonomy, privacy, liability, security, data protection, and safety. It will conclude with providing steps that we need to take to avoid these pitfalls, while ensuring we reap the benefits that SDVs bring.

One of the main difficulties in assessing artificial intelligence (AI) is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI (HLEG AI Ethics guidelines for trustworthy AI, 2019, p. 35). Trust is one of the most important and defining activities in human relationships, so proposing that AI should be trusted, is a very serious claim. This paper will show that AI cannot be something that has the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or can be held responsible for their actions—requirements of the affective and normative accounts of trust. While AI meets all of the requirements of the rational account of trust, it will be shown that this is not actually a type of trust at all, but is instead, a form of reliance. Ultimately, even complex machines such as AI should not be viewed as trustworthy as this undermines the value of interpersonal trust, anthropomorphises AI, and diverts responsibility from those developing and using them.

While research in the ethics of artificial intelligence (AI) has grown recently, the relationship between AI’s ethical and economic dimensions is under-researched. This is surprising, given the considerable investments in AI by Big Tech companies (e.g., Microsoft, META and IBM) and their ambiguous role in today’s public debate on AI. After the second Trump election, this ambiguity has resulted in industry opposition to rules and regulations (e.g., disinvestments in moderation facilities at social media platforms and calls for deregulation). AI ethics must respond to the economic underpinnings of this situation. While economics in AI ethics has also been seen in recent funding schemes (e.g., investment in 30 ethical, legal, and social (ELSA) labs), there is a ambiguity in how these AI ELSA labs should respond to economic aspects. This paper examines the role of economics in responsible AI research, using the case of the ELSA lab approach. The four features of ELSA (proximity, anticipation, interdisciplinarity, and interactivity) serve as a point of departure to demonstrate how economics can be integrated within the ELSA framework of AI. This paper proposes that economics should be integrated within these four ELSA features to implement responsible AI successfully.

This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

The use of a ‘human-centred’ artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development and use. However, this paper will use insights from the works of Michel Foucault (mostly The Order of Things) to argue that the HCAI approach is deeply problematic in its assumptions. In particular, this paper will criticise four main assumptions commonly found within HCAI: human–AI hybridisation is desirable and unproblematic; humans are not currently at the centre of the AI universe; we should use humans as a way to guide AI development; AI is the next step in a continuous path of human progress; and increasing human control over AI will reduce harmful bias. This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI [it provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI)], it will contribute to AI ethics debates by offering a critique of human-centredness in AI (by choosing Foucault, it provides a bridge between older ideas with contemporary issues), and it will also contribute to Foucault studies (by using his work to engage in contemporary debates, such as AI).

AI can fundamentally shape our society, raising significant ethical, legal, and social aspects that need careful consideration. Therefore, it is important to educate and train engineering students developing and using AI about ethical, legal, and social aspects. However, teaching this to engineering students with no ethics background poses unique challenges, as engineering is more focused on the development of particular technologies. In addition, engineering education often focuses on artefact-level ethical, legal, and social aspects of AI, such as data privacy and transparency, while recent research shows that structural impacts (e.g., surveillance, instrumentalization, and deskilling) should also be included in responsible AI research. So, how can we effectively educate engineers about ethical, legal, and social aspects, particularly when it comes to structural impacts of AI? This article develops an Educational Intervention to help young engineers identify both artefact-level and broader structural ethical, legal, and social aspects of AI. This Educational Intervention includes a series of lectures that provide a theoretical analysis of ethical, legal, and social aspects in AI, and an in-class Product Impact Tool to help students systematically diagnose ethical, legal, and social aspects in an AI case. We suggest that the combination of active/passive learning is important to this Intervention, but what sets it apart is its use of the adjusted Product Impact Tool. This teaching tool integrates a multi-level approach to ethical, legal, and social aspects, allowing engineering students to move beyond artifact-level concerns like transparency and privacy to critically engage with AI’s institutional, sociopolitical, and ontological dimensions.

This paper examines how professionals (N = 32) working on artificial intelligence (AI) view structural AI ethics challenges like injustices and inequalities beyond individual agents' direct intention and control. This paper answers the research question: What are professionals’ perceptions of the structural challenges of AI (in the agri-food sector)? This empirical paper shows that it is essential to broaden the scope of ethics of AI beyond micro- and meso-levels. While ethics guidelines and AI ethics often focus on the responsibility of designers and the competencies and skills of designers to take this responsibility, our results show that many structural challenges are beyond their reach. This result means that while ethics guidelines and AI ethics frameworks are helpful, there is a risk that they overlook more complicated, nuanced, and intersected structural challenges. In addition, it highlights the need to include diverse stakeholders, such as quadruple helix (QH) participants, in discussions around AI ethics rather than solely focusing on the obligations of AI developers and companies. Overall, this paper demonstrates that addressing structural challenges in AI is challenging and requires an approach that considers four requirements: (1) multi-level, (2) multi-faceted, (3) interdisciplinary, and (4) polycentric governance.

The use of a ‘human-centred’ artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development and use. However, this paper will use insights from the works of Michel Foucault (mostly The Order of Things) to argue that the HCAI approach is deeply problematic in its assumptions. In particular, this paper will criticise four main assumptions commonly found within HCAI: human–AI hybridisation is desirable and unproblematic; humans are not currently at the centre of the AI universe; we should use humans as a way to guide AI development; AI is the next step in a continuous path of human progress; and increasing human control over AI will reduce harmful bias. This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI [it provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI)], it will contribute to AI ethics debates by offering a critique of human-centredness in AI (by choosing Foucault, it provides a bridge between older ideas with contemporary issues), and it will also contribute to Foucault studies (by using his work to engage in contemporary debates, such as AI).

This paper explores the gap in the literature on social responsibility guidance for start-ups and start-up investors. It begins by evaluating research conducted in two different fields (namely, socially responsible investment (SRI) and responsible research and innovation (RRI)) and how they can guide social responsibility in STEM (Science, Technology, Engineering, Mathematics) start-ups. To do this, we evaluate an industry-standard SRI catalogue of metrics - the Global Impact Investing Network's (GIIN) Impact Reporting and Investment Standards (IRIS+) - and indicators from 12 EC-funded RRI projects. Based on this analysis, we propose a framework of 24 indicators to assess the social responsibility of start-ups and investors. The purpose of our framework is twofold: firstly, to provide clear guidance for start-ups aiming to implement socially responsible behaviours, and secondly, to provide start-up investors with criteria to identify if start-ups are socially responsible. While the indicators are phrased in a prescriptive way for start-ups, they can also be used by investors to identify if start-ups are implementing the indicators in practice.

Because of the importance of data-sharing for the economy, improved products and services, and to benefit society, the European Union has proposed developing a Common European Data Space (CEDS). The goal is to create a single European data market through 14 domain-specific data spaces (e.g., agriculture, or the Common European Agricultural Data Space (CEADS)). One of the central tenets of the CEDS is to ensure that those who share data can maintain control over who has access to, use of, and ability to share it (or ‘data sovereignty’). Data sovereignty is an umbrella concept with many different values comprising its implementation. Therefore, to successfully implement data sovereignty in the CEADS (and all CEDS for that matter), it is important to identify what values are important for stakeholders.This paper examines the CEADS to identify the most critical values for potential stakeholders of this data space. We implement a six-phase value-sensitive design methodology called ‘value mapping’ by interviewing stakeholders from an incipient Spanish data-sharing initiative (potentially representing over 1 million farmers) and conducting a workshop with 42 international stakeholders at an agri-tech summit. Our findings demonstrate the different values that are important for stakeholders of an agricultural data space: farmers (privacy, control, and trust); farm advisors (human welfare and autonomy); farmer associations (trust and human welfare); technology providers and intermediaries (autonomy and human welfare); public and regulatory bodies (autonomy); and society (justice). Furthermore, we describe different interdisciplinary steps to ensure and protect these values to ensure sovereignty-by-design in the CEADS.

The use of a ‘human-centred’ artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development and use. However, this paper will use insights from the works of Michel Foucault (mostly _The Order of Things_) to argue that the HCAI approach is deeply problematic in its assumptions. In particular, this paper will criticise four main assumptions commonly found within HCAI: human–AI hybridisation is desirable and unproblematic; humans are not currently at the centre of the AI universe; we should use humans as a way to guide AI development; AI is the next step in a continuous path of human progress; and increasing human control over AI will reduce harmful bias. This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI [it provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI)], it will contribute to AI ethics debates by offering a critique of human-centredness in AI (by choosing Foucault, it provides a bridge between older ideas with contemporary issues), and it will also contribute to Foucault studies (by using his work to engage in contemporary debates, such as AI).

In recent years, there has been tension between farmers and the Dutch government regarding sustainability policy (in the efforts to reduce the harm caused by manure surplus) and how implementing this policy affects farmers (in the form of justice concerns). We interviewed Dutch farmers to uncover how they view manure policy. We identified four types of injustices: procedural, contributive, distributive, and intergenerational. We propose that a multi-tiered approach is required to overcome these kinds of ‘wicked problems’, avoid paralysis from lack of action, and overcome the flaws by overestimating the effectiveness of top-down policy.

A subdiscipline has emerged around AI ethics, which is comprised of a wide array of individuals: computer scientists, ethicists, cognitive scientists, roboticists, legal professionals, economists, sociologists, gender, and race theorists. This has led to a very interesting branch of research, addressing issues surrounding the development and use of AI. This chapter will give a very brief snapshot of some of the most pertinent ethical concerns. Many of the issues in the Big Data Ethics chapter in this collection are often applicable to AI ethics, because of the data that these technologies retrieve, store, and use, so will not be duplicated here.1 While data-related issues are not new or unique to AI, AI does hold the potential to dramatically retrieve and analyze data that would not be possible otherwise. Data is being used in unique and transformative ways, such as the use of facial recognition to identify individuals from photos or CCTV; AI robots to retrieve live video feed about the patient(s) that it is monitoring; or in new ways, such as self-driving cars collecting an abundance of data about our surroundings, how we drive, and passengers in the car. There is the potential to infringe on individuals’ privacy, restriction of resources, or at worst, the creation of a surveillance society.

Big Data ethics3 informs about how data can be used ethically and gives principles and values to decide how to use it. The values and norms proposed within this area of research aim to influence laws and guide the use of data. The field of Big Data ethics is expanding with new digital technologies and applications, such as the creation of smart cities, robots, or biometric technology, where data fuels their development and innovation. Thus, this field relates to other ethics’ fields – bioethics, ethics of AI, business ethics, Internet ethics, or the ethics of robots. This book chapter has been evaluated through an extensive literature in Big Data ethics (Richards and King 2014) (Boyd and Crawford 2012) (Diaz and Gürses 2012) (Chen and Quan-Haase 2020) (SHERPA 2019) to present a list of the most significant issues in this field.

This paper addresses the instructional challenges of teaching business ethics in a way shaped by Catholic Social Teaching. Focusing on the concept of the Common Good in CST, I describe my use of a case narrative in classroom instruction to help students understand the concept of the Common Good and to perceive the variety of ways businesses can serve or undermine the Common Good in a small city. Through these pedagogical explorations, I illustrate the distinctive vision of business ethics that flows from CST.

While the theory of belief change has attracted a lot of interest from researchers, work on implementing belief change and actually putting it to use in real-world problems is still scarce. In this paper, we present an implementation of propositional belief change using Binary Decision Diagrams. Upper complexity bounds for the algorithm are presented and discussed. The approach is presented both in the general case, as well as on specific belief change operators from the literature. In an effort to gain a better understanding of the empirical efficiency of the algorithms involved, a fault diagnosis problem on combinational circuits is presented, implemented and evaluated.