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This dissertation is about the purpose of artificial intelligence (AI) research. New learning algorithms, scales of computation, and modes of sensory input make it possible to better predict or simulate decision-making than ever before. But this does not tell us whether or how AI systems should be built. In fact there is much anxiety about how to build AI applications in ways that respect or enact the decision criteria of existing human institutions. But instead of how to better predict or protect how we decide things, my research question is: how can AI tools be used to reorganize the choices we make about how we want to live together? Answering this question requires investigating the conditions under which deliberation is possible about the systems being built—their models, their real-world performance, and their effects on human domains. These three modes of deliberation are philosophically outlined in the introduction and named as sociotechnical specification, normative cybernetics, and machine politics.The first chapter pursues sociotechnical specification in the context of routing algorithms for autonomous vehicle (AV) fleets. It asks what it would mean to relate this emerging transportation model to the other legacy systems adjacent to the travel domain. It sketches proxies in terms of “known unknown” features of the driving environment. These would need to be monitored and serve as targets for optimization in order for the performance of the AV fleet to be considered to be robustly good. The second chapter pursues a normative cybernetics of AVs in terms of a sustained internal critique of reinforcement learning (RL). This introduces new policy questions, whose answers would correspond to types of feedback between the behavior of AV firms and civil society or state organizations. The third chapter outlines the elements of machine politics in terms of concepts borrowed from contemporary analytic philosophy. Ruth Chang’s notion of parity is mobilized to demonstrate the possibility of domain deliberation at different stages of AI development. This comprises a critique of existing schools of thought, represented here in terms of epistemicism (the notion that the structure of human activities can be passively learned and observed) and ontic incomparabilism (the notion that human activities cannot be organically modeled or developed by means of AI). The three types of feedback that are produced through active developmental inquiry are presented in terms of featurization, optimization, and integration, all of which comprise the structural choices at stake in machine politics.

With the impending advent of AI technologies that are deeply embedded in daily life -- such as autonomous vehicles, elder care robots, and robot nannies -- comes a natural apprehension over whether they can integrate smoothly with human society. From these concerns arises a question: can we impose norms -- be they ethical, legal, or social -- on these technologies while preserving the effectiveness of their performance? This proves a difficult question to answer in the presence of machine learning technologies, which are notoriously opaque and unpredictable. Reinforcement learning (RL) is a powerful machine learning technique geared toward teaching autonomous agents goal-directed behaviour in stochastic environments through a utility function. RL agents have proven capable of exhibiting complex behaviours on par with or beyond the abilities of expert human agents, and have also been a subject of interest for machine ethicists; it has been conjectured by many that RL might prove capable of delivering a positive answer to the above question. Indeed, there are already many attempts to implement an ``ethical agent'' with RL. However, these attempts largely ignore the complexities and idiosyncrasies of normative reasoning. Normative reasoning is the purview of the diverse field of Deontic Logic -- the logic of obligations and related notions -- which has yet to receive a meaningful place in the literature on ``ethical'' RL agents. In the following work, we will explore how RL can fall short of the goal of producing an ethical (or rather, normatively compliant) agent; this includes even more powerful developments like safe RL under linear temporal logic (LTL) constraints, due to the limits of LTL as a logic for normative reasoning. Even so, we provide a method for synthesizing LTL specifications that reflect the constraints deducible from certain normative systems. We will then present an alternative framework for imposing normative constraints from the perspective of altering the internal processes of an RL agent to ensure behaviour that complies (as much as possible) with a normative system. To actuate this process, we propose a module called the Normative Supervisor, which facilitates the translation of data from the agent and a normative system into a defeasible deontic logic, leveraging a theorem prover to provide recommendations and judgements to the agent. This allows us to present Online Compliance Checking (OCC) and Norm-Guided Reinforcement Learning (NGRL) for eliciting normatively compliant behaviour from an RL agent. OCC involves, in each state, filtering out from the agent's arsenal actions that do not comply with a normative system in that state, preventing the agent from taking actions that violate the normative system. When no compliant actions exist, a ``lesser evil'' solution is presented. In NGRL, the agent is trained with two objectives; its original task and a normative objective borne out in a utility function that punishes the agent when it transgresses the normative system. We show through a thorough series of experiments on RL agents playing simple computer games -- constrained by the wide variety of normative systems that we present-- that these techniques are effective, albeit flawed, and best utilized in tandem.

In this paper, we present an empirical study on the perception of the ethical challenges of artificial intelligence groups in the classification made by the European Union (EU). The study seeks to identify the ethical principles that cause the greatest concern among the population, analyzing these characteristics among different actors. The main study analyses the difference between Information and Communications Technology (ICT) professionals and the rest of the population. Along with this study, we conducted a gender study; in addition, we studied differences between university students, classified as future professionals who can work in Artificial Intelligence, and other university students. We believe that this work is a starting point for an informed debate in the scientific community and industry on the ethical implications of artificial intelligence based on the classification of ethical principles made by the EU, which can be extrapolated to any analysis carried out on the use of data to apply them in algorithms based on Artificial Intelligence.

Artificial Intelligence (AI) has become one of the most important and ubiquitous technologies across the world. On a daily basis, we interact with powerful AI-based technologies through our use of mobile phones, voice assistants, and even our cars. Despite the widespread adoption of AI, questions and concerns exist around the ethical use of these technologies and their potential to reconfigure our personal and working lives. The Science Foundation Ireland ADAPT Research Centre has developed the Citizens’ Think-Ins model of citizen-researcher dialogue. ADAPT’s Think-In series to date has focused specifically on AI and the role it increasingly plays in our lives, and its impact on culture and society. This white paper presents an analysis of the various discussions that took place within the Citizens’ Think-Ins series. The discussions are presented with specific reference to citizens and civic society, academia, industry, and policymakers, and provide concrete recommendations to each stakeholder group, to draw parallels between their requirements, and to encourage the periodic use of Citizens’ Think-Ins as part of a larger deliberative and participatory approach comprising all stakeholders.

In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.

The successes of machine learning (ML) and artificial intelligence (AI) models encourage their widespread deployments in high-stakes domains -- from public transportation to social decision-making such as autonomous driving, criminal justice, and company hiring. Such widespread deployments call for assessing and addressing the ethical concerns of AI systems. The thesis aims to develop practical techniques and theoretical understanding for building ethical AI systems. We divide the thesis into two parts. The first part of the thesis focuses on automatic information extraction using natural language processing (NLP) from policy documents. Policy documents are natural language documents about how different stakeholders (e.g., users and ML services providers) in internet services agree on how the services providers commit to the ethical usage of users' data. Specifically, we develop NLP techniques and benchmarks for privacy policies, a type of policy document describing the practices of using, sharing, and protecting users' data. Such developed NLP techniques could be extended to other natural language law documents describing ethical AI principles and help improve mutual trust among different parties. The second part of the thesis focuses on the theoretical understanding and development of algorithmic interventions for ethical artificial intelligence. In particular, we study the fairness problems for various machine learning tasks, such as classification, regression, and sequential decision-making: (1) we provide bias mitigation techniques for text classification using contrastive representation learning; (2) we provide the theoretical understanding and mitigation techniques for accuracy disparity problem in regression; (3) we propose a fairness notion that requires long-term equality on expected utility for different demographic groups for sequential decision-making and develop methods to achieve the proposed fairness notion. In addition, we also study adversarial representation learning, a technique that has been widely used for algorithmic fairness, and its implications for information obfuscation. We hope the research presented in the thesis will facilitate the practices of building ethical machine learning systems and help increase the understanding and trust among stakeholders towards the machine learning systems.

This policy brief aims at guiding public organisations in the trustworthy adoption of disruptive technologies with the aim to increase transparency towards both its internal and external stakeholders about how DTs and data are used, what are the fundamental decisional processes that are followed, and how the potential risks are identified and addressed.

Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its impacts was a key priority. The 2021 World Climate Summit finished with calls for governments to take tougher measures towards reducing their carbon footprints. However, it is not obvious how governments can make practical implementations to achieve this goal. One challenge towards achieving a reduced carbon footprint is gaining awareness of how energy exhaustive a system or mechanism is. Artificial Intelligence (AI) is increasingly being used to solve global problems, and its use could potentially solve challenges relating to climate change, but the creation of AI systems often requires vast amounts of, up front, computing power, and, thereby, it can be a significant contributor to greenhouse gas emissions. If governments are to take the SDGs and calls to reduce carbon footprints seriously, they need to find a management and governance mechanism to (i) audit how much their AI system ‘costs’ in terms of energy consumption and (ii) incentivise individuals to act based upon the auditing outcomes, in order to avoid or justify politically controversial restrictions that may be seen as bypassing the creativity of developers. The idea is thus to find a practical solution that can be implemented in software design that incentivises and rewards and that respects the autonomy of developers and designers to come up with smart solutions. This paper proposes such a sustainability management mechanism by introducing the notion of ‘Sustainability Budgets’—akin to Privacy Budgets used in Differential Privacy—and by using these to introduce a ‘Game’ where participants are rewarded for designing systems that are ‘energy efficient’. Participants in this game are, among others, the Machine Learning developers themselves, which is a new focus for this problem that this text introduces. The paper later expands this notion to sustainability management in general and outlines how it might fit into a wider governance framework.

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented within narrow and targeted fairness toolkits for algorithm assessments that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around the acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values.

There is an explosion of camera surveillance in our cities today. As a result, the risks of privacy infringement and erosion are growing, as is the need for ethical solutions to minimise the risks. This research aims to frame the challenges and ethics of using data surveillance technologies in a qualitative social context. A use case is presented which examines the ethical data required to automatically enforce bus lanes using camera surveillance and proposes ways of minimising the risks of privacy infringement and erosion in that scenario. What we seek to illustrate is that there is a challenge in using technologies in positive, socially responsible ways. To do that, we have to better understand the use case and not just the present, but also the downstream risks, and the downstream ethical questions. There is a gap in the literature in this aspect as well as a gap in the actual thinking of researchers in terms of understanding and responding to it. A literature review and detailed risk analysis of automated bus lane enforcement is conducted. Based on this, an ethical design framework is proposed and applied to the use case. Several potential solutions are created and described. The final chosen solution may also be broadly applicable to other use cases. We show how it is possible to provide an ethical AI solution for detecting infringements that incorporates privacy-by-design principles, while being fair to potential transgressors. By introducing positive, pragmatic and adaptable methods to support and uphold privacy, we support access to innovation that can help us mitigate current emerging risks.

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods.

Advances in machine learning (ML) technologies have greatly improved Artificial Intelligence (AI) systems. As a result, AI systems have become ubiquitous, with their application prevalent in virtually all sectors. However, AI systems have prompted ethical concerns, especially as their usage crosses boundaries in sensitive areas such as healthcare, transportation, and security. As a result, users are calling for better AI governance practices in ethical AI systems. Therefore, AI development methods are encouraged to foster these practices. This research analyzes the ECCOLA method for developing ethical and trustworthy AI systems to determine if it enables AI governance in development processes through ethical practices. The results demonstrate that while ECCOLA fully facilitates AI governance in corporate governance practices in all its processes, some of its practices do not fully foster data governance and information governance practices. This indicates that the method can be further improved., Comment: 8 pages, 1 figure, 2 tables, IEEE 29th International Requirements Engineering Conference Workshops (REW)

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

these algorithms can be held accountable. Opacity as to how algorithmic decisions came to be seems to be the norm and, while principles to which algorithms should adhere have been formulated, these lack proven methods that translate them into practice. Drawing on theories of design-science this research aims to fill that gap by the design of an artefact in the form of a checklist of machine learning monitoring methods that can be used to incorporate algorithmic accountability goals into decision-support systems. A qualitative research approach was taken where, after identifying algorithmic accountability goals from literature, experts in the field of data science were interviewed as to which machine learning monitoring methods could aid in the realisation of these goals. Findings from this stage were later validated using a focus group. Results indicate that the checklist, if embedded in an organisation in a similar strain as security or architectural principles, can aid professionals in the incorporation of algorithmic accountability goals in their decision-support systems.

Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT’s automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the real-identity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacy-protected scenarios, such as confidential meetings, sensitive spaces, or even public toilets.

The potential for bias embedded in data to lead to the perpetuation of social injustice though Artificial Intelligence (AI) necessitates an urgent reform of data curation practices for AI systems, especially those based on machine learning. Without appropriate ethical and regulatory frameworks there is a risk that decades of advances in human rights and civil liberties may be undermined. This paper proposes an approach to data curation for AI, grounded in feminist epistemology and informed by critical theories of race and feminist principles. The objective of this approach is to support critical evaluation of the social dynamics of power embedded in data for AI systems. We propose a set of fundamental guiding principles for ethical data curation that address the social construction of knowledge, call for inclusion of subjugated and new forms of knowledge, support critical evaluation of theoretical concepts within data and recognise the reflexive nature of knowledge. In developing this ethical framework for data curation, we aim to contribute to a virtue ethics for AI and ensure protection of fundamental and human rights.

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite humaninvolvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its "black box" characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the "wrong" choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the "black box" of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the widespread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning., Comment: 9 pages, 1 table, 1 figure

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI.

Dissertação de mestrado em Direito e Informática, É inegável que a inteligência artificial (IA) gera importantes oportunidades para a sociedade, como o aumento do bem estar dos indivíduos e o avanço da economia. Contudo, se mal utilizada, a IA também pode provocar riscos aos interesses da sociedade e aos direitos fundamentais dos indivíduos. A ausência de uma regulação específica voltada para a IA torna esse cenário ainda mais complexo, uma vez que os danos à privacidade, equidade e segurança podem ser irreparáveis. A partir d esse contexto, princípios éticos e de direitos se apresentam para construir uma IA ética e de confiança. No entanto, ainda pela ausência de uma legislação específica, torna se importante perceber se o Regulamento Geral de Proteção de Dados (RGPD) possui os instrumentos adequados para transformar o desígnio europeu de uma IA ética e de confiança em realidade. A fim de verificar essa possibilidade, os dispositivos do RGPD, e especialmente a Avaliação de Impacto na Proteção de Dados (AIPD), serão analisados a luz de uma IA europeia ética e de confiança., It is undeniable that artificial intelligence (AI) generates significant opportunities for society, such as increasing the well being of individuals and advancing the economy. However, if misused, AI can also pose risks to the interests of society and the fundamental rights of individuals. The lack of specific regulation for AI makes this scenario even more complex since the harm to privacy, fairness, and safety can be irreversible. From this context, ethical and rights principles are presented to build a t rustworthy and ethical AI. However, by the absence of specific legislation, it becomes important to understand whether the General Data Protection Regulation (GDPR) is an appropriate instrument to turn the European design of an ethical and trustworthy AI i nto reality. In order to verify this possibility, the provisions of the GDPR, and mainly the Data Protection Impact Assessment (DPIA), will be analyzed in the light of an ethical and trustworthy European AI.

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