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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12102">arXiv:2502.12102</a> <span> [<a href="https://arxiv.org/pdf/2502.12102">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Relational Norms for Human-AI Cooperation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Earp%2C+B+D">Brian D. Earp</a>, <a href="/search/cs?searchtype=author&query=Mann%2C+S+P">Sebastian Porsdam Mann</a>, <a href="/search/cs?searchtype=author&query=Aboy%2C+M">Mateo Aboy</a>, <a href="/search/cs?searchtype=author&query=Awad%2C+E">Edmond Awad</a>, <a href="/search/cs?searchtype=author&query=Betzler%2C+M">Monika Betzler</a>, <a href="/search/cs?searchtype=author&query=Botes%2C+M">Marietjie Botes</a>, <a href="/search/cs?searchtype=author&query=Calcott%2C+R">Rachel Calcott</a>, <a href="/search/cs?searchtype=author&query=Caraccio%2C+M">Mina Caraccio</a>, <a href="/search/cs?searchtype=author&query=Chater%2C+N">Nick Chater</a>, <a href="/search/cs?searchtype=author&query=Coeckelbergh%2C+M">Mark Coeckelbergh</a>, <a href="/search/cs?searchtype=author&query=Constantinescu%2C+M">Mihaela Constantinescu</a>, <a href="/search/cs?searchtype=author&query=Dabbagh%2C+H">Hossein Dabbagh</a>, <a href="/search/cs?searchtype=author&query=Devlin%2C+K">Kate Devlin</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+X">Xiaojun Ding</a>, <a href="/search/cs?searchtype=author&query=Dranseika%2C+V">Vilius Dranseika</a>, <a href="/search/cs?searchtype=author&query=Everett%2C+J+A+C">Jim A. C. Everett</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+R">Ruiping Fan</a>, <a href="/search/cs?searchtype=author&query=Feroz%2C+F">Faisal Feroz</a>, <a href="/search/cs?searchtype=author&query=Francis%2C+K+B">Kathryn B. Francis</a>, <a href="/search/cs?searchtype=author&query=Friedman%2C+C">Cindy Friedman</a>, <a href="/search/cs?searchtype=author&query=Friedrich%2C+O">Orsolya Friedrich</a>, <a href="/search/cs?searchtype=author&query=Gabriel%2C+I">Iason Gabriel</a>, <a href="/search/cs?searchtype=author&query=Hannikainen%2C+I">Ivar Hannikainen</a>, <a href="/search/cs?searchtype=author&query=Hellmann%2C+J">Julie Hellmann</a>, <a href="/search/cs?searchtype=author&query=Jahrome%2C+A+K">Arasj Khodadade Jahrome</a> , et al. (37 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12102v1-abstract-short" style="display: inline;"> How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These nor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12102v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12102v1-abstract-full" style="display: none;"> How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12102v1-abstract-full').style.display = 'none'; document.getElementById('2502.12102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">76 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.10390">arXiv:2501.10390</a> <span> [<a href="https://arxiv.org/pdf/2501.10390">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards an Environmental Ethics of Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=van+Uffelen%2C+N">Nynke van Uffelen</a>, <a href="/search/cs?searchtype=author&query=Lauwaert%2C+L">Lode Lauwaert</a>, <a href="/search/cs?searchtype=author&query=Coeckelbergh%2C+M">Mark Coeckelbergh</a>, <a href="/search/cs?searchtype=author&query=Kudina%2C+O">Olya Kudina</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.10390v1-abstract-short" style="display: inline;"> In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiabl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10390v1-abstract-full').style.display = 'inline'; document.getElementById('2501.10390v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.10390v1-abstract-full" style="display: none;"> In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiable to develop an AI system, and how to make design choices, considering its environmental impact? However, so far, the environmental impact of AI has largely escaped ethical scrutiny, as AI ethics tends to focus strongly on themes such as transparency, privacy, safety, responsibility, and bias. Considering the environmental impact of AI from an ethical perspective expands the scope of AI ethics beyond an anthropocentric focus towards including more-than-human actors such as animals and ecosystems. This paper explores the ethical implications of the environmental impact of AI for designing AI systems by drawing on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these tenets of justice, we outline criteria for developing environmentally just AI systems, given their ecological impact. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10390v1-abstract-full').style.display = 'none'; document.getElementById('2501.10390v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10706">arXiv:2405.10706</a> <span> [<a href="https://arxiv.org/pdf/2405.10706">pdf</a>, <a href="https://arxiv.org/format/2405.10706">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Challenging the Human-in-the-loop in Algorithmic Decision-making </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tschiatschek%2C+S">Sebastian Tschiatschek</a>, <a href="/search/cs?searchtype=author&query=Stamboliev%2C+E">Eugenia Stamboliev</a>, <a href="/search/cs?searchtype=author&query=Schmude%2C+T">Timoth茅e Schmude</a>, <a href="/search/cs?searchtype=author&query=Coeckelbergh%2C+M">Mark Coeckelbergh</a>, <a href="/search/cs?searchtype=author&query=Koesten%2C+L">Laura Koesten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10706v2-abstract-short" style="display: inline;"> We discuss the role of humans in algorithmic decision-making (ADM) for socially relevant problems from a technical and philosophical perspective. In particular, we illustrate tensions arising from diverse expectations, values, and constraints by and on the humans involved. To this end, we assume that a strategic decision-maker (SDM) introduces ADM to optimize strategic and societal goals while the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10706v2-abstract-full').style.display = 'inline'; document.getElementById('2405.10706v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10706v2-abstract-full" style="display: none;"> We discuss the role of humans in algorithmic decision-making (ADM) for socially relevant problems from a technical and philosophical perspective. In particular, we illustrate tensions arising from diverse expectations, values, and constraints by and on the humans involved. To this end, we assume that a strategic decision-maker (SDM) introduces ADM to optimize strategic and societal goals while the algorithms' recommended actions are overseen by a practical decision-maker (PDM) - a specific human-in-the-loop - who makes the final decisions. While the PDM is typically assumed to be a corrective, it can counteract the realization of the SDM's desired goals and societal values not least because of a misalignment of these values and unmet information needs of the PDM. This has significant implications for the distribution of power between the stakeholders in ADM, their constraints, and information needs. In particular, we emphasize the overseeing PDM's role as a potential political and ethical decision maker, who acts expected to balance strategic, value-driven objectives and on-the-ground individual decisions and constraints. We demonstrate empirically, on a machine learning benchmark dataset, the significant impact an overseeing PDM's decisions can have even if the PDM is constrained to performing only a limited amount of actions differing from the algorithms' recommendations. To ensure that the SDM's intended values are realized, the PDM needs to be provided with appropriate information conveyed through tailored explanations and its role must be characterized clearly. Our findings emphasize the need for an in-depth discussion of the role and power of the PDM and challenge the often-taken view that just including a human-in-the-loop in ADM ensures the 'correct' and 'ethical' functioning of the system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10706v2-abstract-full').style.display = 'none'; document.getElementById('2405.10706v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.02231">arXiv:2305.02231</a> <span> [<a href="https://arxiv.org/pdf/2305.02231">pdf</a>, <a href="https://arxiv.org/format/2305.02231">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=D%C3%ADaz-Rodr%C3%ADguez%2C+N">Natalia D铆az-Rodr铆guez</a>, <a href="/search/cs?searchtype=author&query=Del+Ser%2C+J">Javier Del Ser</a>, <a href="/search/cs?searchtype=author&query=Coeckelbergh%2C+M">Mark Coeckelbergh</a>, <a href="/search/cs?searchtype=author&query=de+Prado%2C+M+L">Marcos L贸pez de Prado</a>, <a href="/search/cs?searchtype=author&query=Herrera-Viedma%2C+E">Enrique Herrera-Viedma</a>, <a href="/search/cs?searchtype=author&query=Herrera%2C+F">Francisco Herrera</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.02231v2-abstract-short" style="display: inline;"> Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.02231v2-abstract-full').style.display = 'inline'; document.getElementById('2305.02231v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.02231v2-abstract-full" style="display: none;"> Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.02231v2-abstract-full').style.display = 'none'; document.getElementById('2305.02231v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">30 pages, 5 figures, under second review</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T01 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; 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