Jess Whittlestone
Rune Nyrup
Anna Alexandrova
Stephen Cave

The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions

INTRODUCTION AI systems promise widespread benefits to society, while also posing substantial risks across almost all sectors. In the last few years, a number of different groups and initiatives have attempted to articulate and agree principles to guide the application of AI in society. In this paper we discuss the role and limitations of these principles, and argue that an important next step for AI ethics is to focus more on the tensions that arise as we try to implement them in practice. By 'AI ethics' we specifically mean the emerging field of practical AI ethics, which focuses on developing frameworks and guidelines to ensure the ethical use of AI in society (analogous to the field of biomedical ethics, which provides practical frameworks for ethical practice in medicine.) AI ethics therefore covers several different sectors and types of institutions, including the following: • Technology companies aiming to develop their own ethical guidelines (e.g. Google's 'AI Ethics principles'); • Professional bodies whose codes of ethics are aimed at guiding practitioners; • Standards-setting bodies that aim to set general standards for fields of research of industries (e.g. the IEEE or British Standards Institution); • Government bodies and legislators that aim to develop policy and regulation (e.g. the UK's new Centre for Data Ethics and Innovation); • Researchers across disciplines whose work aims to inform these ways that AI ethics is put into practice: exploring the technical, philosophical, or legal aspects of using algorithms in ethical ways  [20]  or synthesising and translating such research into practice  [27] . Agreeing on principles is valuable for the aims of all of these groups: for example, principles can provide a useful starting point from which to develop more formal standards and regulation, and can help to identify priority issues on which both research and policy should focus. However, we argue that current lists of principles for AI ethics are too high-level to be immediately useful for these groups' aims. When we look at specific cases, it becomes clear that principles will come into conflict with each other. This means their practical value is limited: without acknowledging these conflicts standards may be set unrealistically high, or regulation intended to protect one value might inadvertently compromise other important goals. In order to be practically useful, we suggest that the field of AI ethics should focus more on identifying and attempting to resolve the tensions that arise when we apply them to specific cases. We begin by reviewing how principles have evolved in AI ethics over the last two years. Drawing on comparisons with bioethics -a field with a robust and well-developed tradition in using principles to govern medical practice -we discuss some of the limitations of principles. We make the case that all areas of AI ethics would benefit from a more rigorous exploration of the tensions that arise when we try to apply principles to concrete cases. We outline some key tensions that already arise from the use of AI in society, and discuss what work might be needed to resolve them. To our knowledge, this is the first paper to explicitly examine the role and limits of principles in AI ethics, and the importance of focusing more on tensions as a next step.
 THE EMERGENCE OF PRINCIPLES IN AI ETHICS Though the field is in its infancy, there is widespread agreement on some of the core issues (such as bias) and values (such as fairness) that AI ethics should focus on. Over the last two years, these have begun to be codified in sets of 'principles' or 'tenets'. The Asilomar AI principles, developed in 2017 in conjunction with the Asilomar conference for Beneficial AI, outline guidelines on how research should be conducted, ethics and values that use of AI must respect, and important considerations for thinking about long-term issues  [12] . The principles were signed by several thousand AI researchers and others, including many academic ethicists and social scientists. Around the same time, the US Association for Computing Machinery (ACM) issued a statement and set of seven principles for Algorithmic Transparency and Accountability, addressing a narrower but closely related set of issues  [1] . Over the course of 2017, several other initiatives and organisations published additional sets of principles: including the Japanese Society for Artificial Intelligence's Ethical Guidelines in February 2017  [16] ; a set of draft principles from the Montreal Declaration on Responsible AI in November  [25] ; and the IEEE's General Principles of Ethical Autonomous and Intelligent Systems in December  [14] . This proliferation of principles has continued into 2018: with the Partnership on AI publishing a set of 'tenets' which its members agree to uphold  [18] ; the UK House of Lords suggesting five principles for a cross-sector AI code which could be adopted internationally  [23] , and Google publishing their 'AI ethics principles' in June  [19] . These different sets of principles have considerable overlap. There is widespread agreement that AI-based technologies should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely-held values such as fairness, privacy, and autonomy.  [9]  suggest that many of the different existing sets can be synthesised into five key principles: the four that are already used in bioethics -autonomy, beneficence, non-maleficence, and justice  [4]  -plus the additional principle of explicability, which captures the challenges of intelligibility and accountability unique to AI systems. While this convergence is encouraging, it is unclear at this point whether this reflects a deep consensus about what is important, arrived at independently by numerous different actors, or merely a shallow consensus due to the fact that different groups have read similar papers and built on the work of one another. Principles can be a valuable part of applied ethics; agreeing on high-level principles is therefore an important step for ensuring that AI is developed and used for the benefit of society. Principles help condense complex ethical issues into a few central elements which can be clearly understood and agreed upon by people from diverse fields and sectors. They encourage widespread commitment to a shared set of values, and can give them a more prominent role in institutional decision-making processes. Principles can form a basis for more formal commitments in professional ethics, internationally agreed standards, and regulation. They can also help address public concerns, by clarifying the ethical commitments of researchers and industry. However, while principles are important, they are not in themselves enough to ensure society can reap the benefits and mitigate the risks of new technologies. In order to be useful in practice, principles need to be able to guide action -to help people navigate the competing demands and considerations of concrete situations. As we articulate in the next section, there are several obstacles to this.
 THE LIMITS OF PRINCIPLES The four principles of beneficence, non-maleficence, autonomy, and justice have played a prominent role in bioethics  [4] , a field with decades of experience in managing the challenges posed by new technologies. These principles aim to articulate general values on which everyone can agree, and to function as practical guidelines. But they have spurred substantial debate: some argue that we should put no weight on principles and focus entirely on the elements of specific cases  [10] , while others have advocated a more moderate view, whereby principles should be considered in close conjunction with analysis of 'paradigm' cases  [17] . However, even the strongest advocates of principlism in bioethics acknowledge that principles alone are not enough.  [4]  suggest that principles should be taken as guidelines, which need to be made specific for use in policy and clinical decision-making. They elaborate that in order to be action-guiding, principles need to be accompanied by an account of how they apply in specific situations, and how to balance them when they conflict. In this section, we review some of the main limitations of principles that have been highlighted in the bioethics literature and illustrate why these also apply to the principles proposed for AI ethics.
 Different Groups May Interpret Principles Differently The central terms used in principles are often ambiguous, masking conceptual complexity and differences in interpretation across populations. As  [8]  point out, the principle of 'justice' in bioethics does not say anything about what is just or unjust, leaving this to the agent to decide for themselves. Clouser and Gert argue that principles often mask important moral disagreements rather than presenting a well-developed unified theory as they propose to, and that it would be better if these disagreements were articulated and understood more explicitly. In particular, lists of broadly agreed-upon principles cannot recognise that important and legitimate differences in values exist across people and populations. While everyone might agree in principle that 'fairness' is important, there exist deep political disagreements about what exactly constitutes fairness  [5] . Groups may also vary in how much weight they put on one value relative to others in situations of conflict: more individualist cultures may put more weight on personal privacy than more collectivist cultures, for example. An important step in making principles more practical is to formalize them into standards and regulation  [27] . But this process is not a straightforward unpacking of the relevant principles, as different principles will come into conflict when applied to concrete cases. In the next section, we make the case that in order for principles to inform more practical aspects of AI ethics, including professional ethics, standards, and regulation, we need to begin by exploring in detail the different kinds of tensions that arise when principles are applied.
 Principles Are Highly General Relatedly, principles are by their nature highly general: their value is that they indicate important moral themes that apply across a wide range of scenarios. This means that they can be useful as a kind of checklist: as a set of important considerations that need to be taken into account in specific scenarios. However, the generality of most principles also limits their ability to guide practical action  [3] ). Many of the principles proposed in AI ethics are too broad to be action-guiding. For example, ensuring that AI is used for 'social good' or 'the benefit of humanity' is a common thread among all sets of principles. These are phrases on which a great majority can agree exactly because they carry with them few if any real commitments. A very wide range of differing ideological, political and philosophical standpoints could claim to be for the good, or for the benefit of humanity. Only principles that are narrower and more specific are likely to be useful in practice. Recent industry commitments to not develop technology for autonomous weapons are an example of a principle that is specific, action-guiding and can be used to hold people to account. But at the same time, exactly that specificity means its relevance is limited to one sector, and it has many dissenters.
 Principles Come into Conflict in Practice The gap between principles and practical judgement grows larger still when we consider that principles will inevitably conflict with each other. For example, the UK House of Lords AI Committee report states that, "it is not acceptable to deploy any artificial intelligence system which could have a substantial impact on an individual's life, unless it can generate a full and satisfactory explanation for the decisions it will take. " The intentions behind this principle are important, but it masks a crucial tension between using algorithms for social benefit ('beneficence') and ensuring those algorithms are fully intelligible to humans ('explicability'). For example, algorithms exist today that can diagnose medical conditions more accurately than doctors, potentially saving lives  [24] , but for which a full and satisfactory explanation cannot necessarily be provided (depending on how this is defined). In some situations, the benefit of using an algorithm may be high enough, and its accuracy reliable enough, that all users agree it is worth using even if a fully comprehensive explanation of its decisions cannot be given. There are complex and important trade-offs involved here  [15, 22] , and a principle that simply states that it is not acceptable to deploy AI systems without full explainability fails to recognise this. In conclusion, there is a risk that high-level principles give the impression of being the outcome of meaningful debate about how AI should be developed, but in reality they are simply postponing it.
 WHY THE FIELD SHOULD FOCUS ON TENSIONS We use the term 'tension' to refer to any conflict, whether apparent, contingent or fundamental, between important values or goals, where it appears necessary to give up one in order to realise the other. For example, the use of socially beneficial data-driven technologies might make it impossible for us to fully guarantee otherwise desirable levels of data privacy. If the potential gains of these technologies are significant enough -new and highly effective cancer treatments, say -we might decide that a higher risk of privacy breaches is a price worth paying. In some cases, a tension may reflect a strict moral tradeoff: a situation where two values or goals conflict and it is not possible to get more of one without sacrificing another. However, many tensions in AI are more contingent, and arise as a result of current technological or societal constraints. Using machine learning for social benefit may not be fundamentally in tension with privacy, transparency, or fairness, but many current methods employed for the former goal do conflict with these ideals. We do not yet know how far new technological or governance solutions could go to dissolve these tensions. Others have acknowledged the importance of recognising conflicts between values in AI ethics, but to our knowledge none have explored in detail why this would be beneficial or what it would look like in practice. For example,  [9]  say that, "Ensuring socially preferable outcomes of AI relies on resolving the tension between incorporating the benefits and mitigating the potential harms of AI, in short, simultaneously avoiding the misuse and underuse of these technologies", but do not discuss specific tensions in detail or how to resolve them. In this section, we discuss some of the benefits of focusing on tensions. We outline four reasons this is an important next step for AI ethics: (1) bridging the gap between principles and practice, (2) acknowledging differences in values, (3) highlighting areas where new solutions are needed and (4) identifying ambiguities and knowledge gaps.
 Bridging the Gap between Principles and Practice In general, we see focussing on tensions as an important way of bridging the gap between abstract ethical principles and specific cases, and therefore an important first step towards an ethics of AI that is practical and action-guiding. To identify tensions, we need to consider how different values and goals might come into practice in concrete cases. For example, when Google DeepMind collaborated with the Royal Free Hospital, they encountered a conflict between protecting the privacy of patient data, and their goal of using AI to improve early diagnosis of acute kidney injury  [20] . Since similar tensions will likely arise across a range of different cases, focusing on tensions means we are neither driven entirely by the specifics of an individual case, nor are we relying on abstract high-level values. If we can articulate important tensions by looking at a range of cases, and find ways to resolve them in specific scenarios, what we learn from this can then be used to develop standards and regulation that are more sensitive to how principles apply differently across scenarios.
 Acknowledging Differences in Values Focusing on tensions forces us to consider how different values might be interpreted and endorsed differently across groups. While some important tensions are due to conflicts between principles in practice, others arise because there are conflicting meanings or values within a single principle: broad terms like 'fairness' or 'justice' for example are subject to substantial moral and political disagreement  [5, 8] . It may never be possible to totally resolve all of these disagreements. But clearly articulating them is a crucial starting point for ensuring that all aspects of AI ethics are as inclusive as possible: for example, to ensure that international standards take full account of and accommodate cultural differences, and that agreement on such standards is meaningful.
 Highlighting Areas Where New Solutions Are Needed Noting a tension between two values does not necessarily mean we are forced to choose between them: often, we may be able to find some way to get more of both things we value. Recognising these tensions can therefore highlight high priority areas for both researchers and policymakers. For example, acknowledging that there is currently a tradeoff between performance and interpretability in state-of-the-art machine learning systems has motivated technical research that attempts to reduce or eliminate this trade-off  [2] . Acknowledging tensions will also help direct the development of AI in beneficial directions more generally. It is currently far from clear whether advances in AI will augment or degrade human capabilities and agency, but making this tension explicit focuses attention on the important question of which trajectories of development are most likely to lead to the former.
 Identifying Ambiguities and Knowledge Gaps Finally, a tension-focused approach helps to clearly highlight ambiguities and gaps in our understanding of how uses of AI are impacting society. Again, this can help identify new and important research directions. To think clearly about all tensions, we need to recognise and clarify ambiguities in terms: what do we really mean by things like 'fairness', 'justice', and 'autonomy', and how might these be interpreted differently across groups and contexts? To understand the nature of many tensions we need to understand what is currently technically possible: what are the best current methods for ensuring data privacy in machine learning, for example, and what are the costs of these methods? To understand how tensions arise in practice, we need better evidence on how AI is actually being applied in society today: what effect is automation already having on individual lives across different sectors? And to articulate and resolve conflicts between the interests of different groups, we need to really understand the needs and values of affected communities: how do the trade-offs people are willing to make differ based on demographic factors, for example? Focussing on tensions should help to drive this important work.
 WHICH TENSIONS? There are several different ways that applications of AI can introduce tensions between important goals and values. Some tensions arise due to the very nature of AI and machine learning: these techniques allow us to use and draw inferences from very large amounts of (often personal) data, and so challenge important notions of privacy. The most useful models also often quickly become very complex, introducing new issues around human interpretability  [11, 26] . This means we face tensions between using these technologies for socially beneficial goals: improving healthcare, justice, or security, for example, and other goals such as respecting privacy and maintaining trust and understanding in automated systems. Another possibility is that AI systems exacerbate already existing ethical or societal tensions: between different conflicting notions of fairness, for example. Often this is due to the fact that machine learning models are trained on historical data, and so inherit the biases or mistakes they contain  [13] . Here, applications of AI in society do not necessarily introduce new tensions, but increase the importance of already-existing ones such as how to make decision-making more accurate and efficient without inadvertently discriminating against minority groups. Other tensions arise because the harms and benefits of AI systems are unequally distributed in various ways. For example, the impacts of automation may be unequally distributed across populations and cultures: enhancing the agency of some groups by automating mundane tasks while wiping out the livelihood of others, thus threatening their basic needs. The risks and benefits of AI systems could also be unequally distributed over time, and uses of AI that present opportunities in the near term may compromise important long-term values. Increasing personalisation of messages and services may make our lives more convenient and enjoyable in the short run, but begin to undermine important aspects of autonomy, equality and solidarity over time  [21] . Finally, AI may have the potential to both enhance and threaten a given value. For example, depending on the precise direction in which technology develops, it could be used to either greatly enhance human capabilities -if we can develop sophisticated methods of intelligence augmentation  [7]  -or to degrade them: if our own capabilities atrophy as we outsource more and more tasks  [6] . As mentioned above, automation might enhance the agency of some groups while threatening the autonomy of others: whether we see AI as enhancing or degrading human agency could depend on how narrow or global a view we take of its impacts.
 FOUR KEY TENSIONS Given the wide range of tensions that may arise from applications of AI, now or in the future, there is unlikely to be an exhaustive list of all possible tensions. However, we believe that the following four tensions will be particularly central to thinking about the ethical issues arising from the applications of AI systems in society today. These capture a range of issues which are already salient or likely to grow in importance moving forward. Tension 1: Using data to improve the quality and efficiency of services vs. respecting privacy and autonomy of individuals. Machine learning and big data are already being used to improve various public services (including healthcare, education, and social care). These improvements could be hugely beneficial to citizens, but require large amounts of personal data, raising concerns about how to best protect privacy and ensure meaningful consent. Tension 2: Using algorithms to make decisions and predictions more accurate vs ensuring fair and equal treatment. This tension arises when public or private bodies base decisions on predictions about future behaviour of individuals (e.g. when probation officers estimate risk of reoffending) and when they employ machine learning algorithms to improve their predictions. These algorithms may improve accuracy overall, but discriminate against specific subgroups for whom representative data is not available. Tension 3: Reaping the benefits of increased personalisation in the digital sphere vs enhancing solidarity and citizenship. Companies and governments can use personal data to tailor the messages, offers, and services people see. This personalisation can make it easier for people to find the right products and services for them, but differentiating between people in such fine-grained ways may threaten societal ideals of citizenship and solidarity. Tension 4: Using automation to make people's lives more convenient and empowered vs promoting selfactualisation and dignity. Automated solutions may genuinely improve people's lives by saving them time on mundane tasks that could be better spent on more rewarding activities. But they also risk disrupting some of the practices that are an important part of what makes us human. With automation we may see the gifts of arts, languages and science become more accessible to those who were excluded in the pastbut we may also see widespread deskilling, atrophy, ossification of practices, homogenisation and cultural diversity.
 IDENTIFYING FURTHER TENSIONS The above tensions are important and represent areas where exploring tensions is likely to be fruitful for AI ethics. Going forward, further such areas can and should be identified. In order to do so, it is helpful to ask a range of questions, including: • Where AI is being used to serve a particular goal or value, or for 'social benefit' in general, what risks to other values are introduced? • Where might uses of AI that benefit one group, or the population as a whole, have negative consequences for a specific subgroup? How do we balance the interests of different groups? • Where might applications of AI that are beneficial in the near-term introduce risks in the long-term? How do we trade-off short and long-term impacts of society? • Where might future developments in AI either enhance or threaten important values, depending on the direction they take?
 RESOLVING TENSIONS The best approach to resolving a tension will depend on the nature of the tension in question. Where a strict trade-off between two values exists, a choice must be made to prioritise one set of values over another. For example, this may mean judging what risks to privacy it is acceptable to incur for the sake of better public health, or where to reject innovative automation technologies because the threats they pose to human skills and autonomy are too great. Making these trade-off judgements will be a complex political process. Weighing the costs and benefits of different solutions can be an important part of the process but alone is not enough, since it fails to recognise that values are vague and unquantifiable, and that numbers often hide complex value judgements. In addition, resolving trade-offs will require extensive public engagement, to give voice to a wide range of stakeholders and articulate their interests with rigour and respect. On the other hand, where tensions are more practical in nature, strict trade-offs may not be inevitable. It may be that we simply lack the knowledge or tools to advance conflicting values, and investing in further research could identify solutions that better serve all relevant values or goals. For example, it might be possible to use automation to improve people's lives without sacrificing self-actualisation and devaluing human skills, if a clear line can be drawn between the contexts where we do and do not want to pursue automation. In these situations we face a choice. Even if a tension between two goals is not a fundamentally irresolvable one, if we want to apply current technology in society, we will still need to make the kinds of trade-offs described above. On the other hand, if we can hold-off from implementing technologies that introduce tensionscertain kinds of automation, say -then we could instead invest in more research on how technological or governance solutions might reduce the need to navigate potentially difficult value trade-offs. Of course, this is not a binary choice: we can choose to strike a balance by making the trade-offs necessary to implement technology where doing so is relatively unproblematic, while still investing in research to explore how these tensions might be resolvable in future. This choice can be thought of as involving its own tension, between short-and long-term interests: to what extent should we postpone the benefits of new technologies in order to invest the time and resources necessary to resolve the tensions they introduce?
 CONCLUSION Over the last two years, many different sets of principles for the ethical use of AI have been developed. We argue that these highlevel principles, rather than representing the outcome of meaningful debate on how AI should be developed, risk simply postponing it. In order to make AI ethics practical, the field needs to now focus more on the tensions that arise when principles are applied to concrete cases. Though most of these tensions cannot be resolved straightforwardly, we believe articulating them more clearly and explicitly has several benefits. To be useful in practice, principles need to be formalized in standards, codes and ultimately regulation. To be effective, these in turn must acknowledge that there are tensions between the different high-level goals of AI ethics, and provide some guidance on how they should be resolved in different scenarios. They also need to acknowledge and accommodate different perspectives and values as far as possible, if they are to reflect genuine agreement. A focus on tensions can also help to direct research priorities in AI ethics. Articulating tensions can help to highlight important ambiguities and gaps in our understanding of how AI is currently being applied in society which need further research. More generally, much current research in AI ethics appears to be driven by questions of how we ensure that uses of AI respect important values, such as privacy, transparency, or fairness. Reframing research questions to be more focused on understanding and resolving tensions is an important step towards solving practical problems arising from the use of AI in society, since it directs attention to where new technological or governance solutions might help push the development of AI in robustly beneficial directions. 			 This work is licensed under a Creative Commons Attribution International 4.0 License.

Abstract: The last few years have seen a proliferation of principles for AI ethics. There is substantial overlap between different sets of principles, with widespread agreement that AI should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely held values such as fairness, privacy, and autonomy. While articulating and agreeing on principles is important, it is only a starting point. Drawing on comparisons with the field of bioethics, we highlight some of the limitations of principles: in particular, they are often too broad and high-level to guide ethics in practice. We suggest that an important next step for the field of AI ethics is to focus on exploring the tensions that inevitably arise as we try to implement principles in practice. By explicitly recognising these tensions we can begin to make decisions about how they should be resolved in specific cases, and develop frameworks and guidelines for AI ethics that are rigorous and practically relevant. We discuss some different specific ways that tensions arise in AI ethics, and what processes might be needed to resolve them.


Reconciliation Between Factions Focused on Near-Term and Long-Term Artificial Intelligence

Introduction Artificial intelligence (AI) experts are-to generalize-of two minds about long-term AI, especially regarding the potential for radically transformative types of future AI such as artificial superintelligence (ASI) and brain emulation. One faction views long-term AI as a profound issue, warranting extensive attention and immediate action. The other faction views long-term AI as an unhelpful distraction from the more pressing near-term AI issues. The purpose of this paper is to explore the potential for reconciliation between these two seemingly divergent factions. A reconciliation would enable them to expend less energy debating each other and more energy on activities that both agree are positive. In particular, if there are certain activities that simultaneously address issues associated with both near-term and long-term AI, then there is no need to engage in a near-term vs. long-term AI debate, because either position yields the same prescriptions. I will refer to the two factions as the futurists and the presentists. Each can be said to have a core claim: The futurist AI claim: Attention should go to the potential for radically transformative longterm AI. The presentist AI claim: Attention should go to existing and near-term AI. This division is a generalization in that some AI experts support both claims; there may even be some who support neither claim, though the rise of near-term AI applications makes it increasingly difficult for even non-experts to argue that AI is unworthy of attention. The futurist faction can be traced to the early days of AI. Early AI researchers such as I.J. Good and Marvin Minsky predicted that AI would soon be capable of human-level thinking. Foreshadowing contemporary futurist concerns,  Good (1965)  warned of self-improving ultraintelligent machines that would undergo "intelligence explosion" in which "the intelligence of man would be left far behind", forming "the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control" (p.33). When the early predictions failed to pan out, the field entered into a more muted phase known as AI winter. Here began the presentist faction. Most AI researchers shifted from trying to build human-level general AI to building narrow, domain-specific AI. Thus "mainstream AI" came to be associated with developing narrow, near-term AI and repudiating AI futurism. While a minority of researchers continued work on human-level general AI (e.g.,  Goertzel and Pennachin 2007 ) and a few high-profile futurists brought long-term AI some public attention (e.g.,  Kurzweil 2006) , most AI researchers worked on narrow, near-term AI (see e.g.  Bostrom 2014, p.18; Goertzel 2014, p.1) . Within the last few years, two parallel developments have raised the prominence of the futurist-presentist AI divide. One is a breakthrough in the performance of certain AI systems, in particular those involving "deep learning". Major applications of AI are rapidly appearing, from medical diagnosis to self-driving cars. This breakthrough has generated considerable interest in near-term AI while making dramatic long-term AI projections appear more plausible. The other development is a spike in attention going to long-term AI, sparked mainly by the publication of the book Superintelligence  (Bostrom 2014) , public comments by several major celebrities (e.g.,  Hackett 2016; Hern 2016) , and accompanying efforts by futurist researchers and activists (e.g., Future of Life Institute no date). The sober, philosophical tone of Superintelligence and the perceived credibility of some new outspoken futurists has brought the futurist perspective a burst of serious consideration. This has in turn sparked backlash from the presentist faction. The following, from an op-ed in The New York Times by Microsoft principal researcher Kate Crawford, is representative: According to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -when machines become smarter than humans -have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machinelearning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to  (Crawford 2016) . Crawford emphasizes inequality/discrimination as the near-term AI issue to focus on. Other presentists have emphasized such issues as military applications (e.g.,  Arkin 2009) , safety issues with medical applications or self-driving cars, disruptions to labor markets ("technological unemployment"), or simply the technical capabilities of the AI itself. The unifying theme for this group is its insistence on a focus on current and near-term AI. With growing interest in both near-term and long-term AI, the futurist-presentist divide has intensified. In addition to  Crawford (2016) , the divide can also be found in: a series of events on AI in 2016 sponsored by the White House, in which "many of the speakers… emphasized the need to focus on short-term concerns over long-term concerns of artificial general intelligence"  (Conn 2016a) ; remarks by AI researcher Andrew Ng, arguing that worrying about future AI is as unimportant as worrying about "overpopulation on Mars"  (Garling 2015) ; and an article by AI researcher Oren Etzioni, suggesting that AI that would not be built within the next 25 years is not worth paying attention to  (Etzioni 2016) , which prompted a reply by AI researcher Stuart Russell and political scientist Allan Dafoe arguing AI built more than 25 years from now can still be worth attention  (Dafoe and Russell 2016) ; see also  Bostrom (2014, p.18) . This sort of futurist-presentist divide is not unique to AI. For example, Selin (2007) documents a similar divide within the field of nanotechnology, in which a "mainstream" majority of researchers advocate for attention to (and funding for) existing and near-term nanotechnology at the expense of potential long-term transformative nanotechnology. Similarly, one explanation for the low priority commonly given to global warming is the fact that the harmful impacts of climate change will accrue mainly in the future  (Weber 2006) . In recognition of the challenge of motivating action on long-term issues,  Baum (2015)  proposes to address a wide range of longterm catastrophic risks-including long-term AI, nanotechnology, and climate change-by leveraging interest in synergistic near-term co-benefits. The present paper follows in a similar vein as  Baum (2015) , though it is not intended as a critique or an advocacy of either the futurist or presentist AI factions. Instead, it seeks to pragmatically pursue reconciliation between the factions so that they may be able to pursue mutually agreeable activities. Three such activities are described in Section 4. Leading up to that, Section 2 explores the ideas at the root of the futurist and presentist perspectives, while Section 3 proposes a reconciliation based on a general concern for the societal impacts of AI.
 Root Perspectives This section attempts to articulate the perspectives at the root of the futurist and presentist factions. The descriptions derive from readings from, observations of, and interactions with the two factions. Root perspectives can help explain why the two factions disagree with each other and where reconciliation may be possible.
 AI Futurists Two distinct root perspectives can be found among AI futurists. One is centered on the intellectual goals of AI research, the other on the implications of a certain ethical perspective. The first perspective is rooted in an intellectual aspiration of building advanced AI that have general intelligence at a human or superhuman level. "General" in this context means that they can think across a wide range of domains, or "have a broad capability to self-adapt to changes in their goals or circumstances"  (Goertzel 2014, p.1) . Building such advanced AI has long been "the grand dream of artificial intelligence"  (Legg 2008, p.125 ), but it is only pursued by a small portion of AI researchers. These are the researchers who are willing to accept that their work may be of little immediate practicality in order to work towards a more grand future achievement. They seek "to promote the idea that intelligent machines, even super intelligent machines, is a topic that is both important and one that can be scientifically studied, even if just theoretically for now"  (Legg 2008, p.126) .  Goertzel (2014, p.3)  presents what it calls the core hypothesis of artificial general intelligence: The creation and study of synthetic intelligences with sufficiently broad (e.g. humanlevel) scope and strong generalization capability, is at bottom qualitatively different from the creation and study of synthetic intelligences with significantly narrower scope and weaker generalization capability. Thus, advanced future AI requires fundamentally different research than is needed for the narrower contemporary AI.  Goertzel (2014)  maintains that this hypothesis is widely held among artificial general intelligence researchers. Thus, there is a community of researchers dedicated specifically to developing advanced future AI.  (Goertzel 2014 also surveys this research.)  The second AI futurist perspective starts with an ethical concern for long-term effects and pairs this with a belief that long-term AI can have major long-term effects. The ethical concern for long-term effects has rigorous philosophical justification, though as with any ethical position, it is not universally held. It follows directly from a principle of equality: if all human lives (or whatever else we might care about) should be valued equally, then this (arguably) includes future lives. Furthermore, there could be many more future lives than present lives. Thus, philosophers and economists have long recognized the importance of actions that can have ongoing benefits to future generations, such as economic saving  (Ramsey 1928 ) and survival of the population  (Koopmans 1974) . This same ethical perspective has been articulated more recently by some in the AI futurist faction (e.g.,  Bostrom 2003) . The perspective prompts people to seek out issues and actions that could have significant long-term impact; the "merely near-term" is seen as trivial in comparison. This big-picture, long-term perspective explains why the futurist AI faction disproportionately attracts philosophers (e.g., Bostrom 2014; Price 2013) and cosmologists (e.g.,  Hawking et al. 2014) , and why they give their organizations names like Future of Humanity Institute and Future of Life Institute. The long-term ethical perspective is not fundamentally about AI. Instead, it is a guide for all human action; any role for AI is circumstantial. It relates to AI via the belief that certain AI could have major long-term effects, sending human civilization on fundamentally different trajectories. Only AI that could be so transformative is worth paying attention to; the rest is trivial detail. Thus, the prospect of future transformative AI is seen as important, and indeed is important even if there is only a small probability of it being built and even if it would be built decades or centuries from now. As long as there is some nonzero probability that future AI could, say, take over the world and kill everyone  (Bostrom 2014)  or dominate the global economy while remaking the future of civilization  (Hanson 2016) , then it is worth paying attention to. Indeed, such transformative long-term AI competes for attention not with near-term AI but with other potentially transformative issues such as global warming, nuclear war, and long-term nanotechnology. The long-term ethical perspective can seem harsh in its relentless prioritization of long-term issues. For example,  Crawford (2016)  laments racial bias in AI evaluation of criminals in the United States, resulting in harsher prison sentences for African-Americans than for Americans of European descent. 1 Racial bias in law enforcement would seem to be an important issue, and indeed, at the time of this writing, it is at the very top of the public agenda in the United States. However, the attention is on racial bias as it affects people today, not on how it affects long-term outcomes for humanity. Indeed, it is possible that present cases of racial bias will have limited long-term effect, especially relative to a possible future transformative AI. The same holds for most, if not all, other near-term AI issues. Ironically, it is the same ethical principle of equality that underlies opposition to racial bias that also motivates the ignoring of racial bias in favor of long-term issues.
 AI Presentists In contrast, AI presentists are motivated by a desire to work on contemporary and near-term activities and issues. This holds for presentist AI researchers as well as those focused on societal issues raised by AI. Most AI research is presentist. The focus on contemporary and near-term AI systems is rooted in a practical, down-to-Earth perspective that seeks to work on actual systems and avoid speculation about future possibilities, especially when those possibilities seem strange and fantastic. Thus, for example, AI researcher Andrew Ng dismisses concerns about advanced future AI because "the future is so uncertain" that it cannot be meaningfully considered, and instead attention should be on problems that are more immediate and less ambiguous  (Garling 2015) . Similarly, Nilsson describes how AI researchers shy away from work on advanced future AI because they worry it would "risk losing our respectability"  (Nilsson 2010, p.399) . This near-term focus of AI research is not unusual. Looking across all fields of science and engineering, one finds few people motivated by long-term effects for humanity or other considerations deriving from theoretical philosophy. Indeed, they often believe ethical considerations to be fundamentally outside their professional domain, so much that they "accept radical restrictions on having consequential opinions about what ought to be done"  (Shapin 2010, p.388; emphasis original) . This can be seen, for example, in the United States National Science Foundation's two criteria for evaluating funding proposals: intellectual merit and broader impacts. Intellectual merit is what scientists and engineers consider intrinsically important; it is what wins grants and publications in top journals. Broader impacts is the societal significance of the research, which is often considered less important (e.g.,  Schienke et al. 2009) . AI researchers generally focus on near-term AI because that is what they consider to have more intellectual merit. Long-term AI is speculative and untestable, which places it outside the bounds of normal science and engineering. The potential for long-term AI to have larger societal impacts is not a factor. From this perspective, even talking about the long-term impacts can be counterproductive, leading to concerns about respectability, as noted above, and to boom-bust cycles of hype and disillusionment, as has been seen over the years of AI "summer" and "winter". Hence presentists can resent the mere mention of long-term AI. The norm of intellectual merit further explains why some AI researchers prefer to focus on the capabilities of the AI itself and not on societal issues. 2 Coming from computer science or related backgrounds, they view their role as one of advancing the science and technology of AI; attending to any related societal issues is, if anything someone else's job. However, many AI researchers are attentive to societal issues associated with AI.  Crawford (2016)  is not a rare exception.  3  When presentists do turn to societal issues, they turn to those that relate to existing and nearterm AI. This focus is reinforced by the fact that the general public and political leadership also tend to emphasize near-term issues. Hence even seemingly major long-term issues like global warming commonly get drowned out by near-term issues like economic growth  (Scruggs and Benegal 2012) . And so, when presentist AI researchers seek to discuss societal issues related to their AI, they find a welcome audience in social policy circles. This is seen, for example, in the heavy focus on near-term issues in the recent White House Symposia on AI Research  (Conn 2016a) .
 Reconciliation Despite the divergent perspectives between the presentists and futures, as well as the sometimes bitter debate between them, the two factions share a common interest in AI and belief of its importance. This commonality may seem banal, but it nonetheless distinguishes them from the large population that gives AI little attention or none at all. Thus, for starters, the two factions can at least unite in promoting AI as a locus of attention. But there can be much more than that. There are significant parts of both factions that are concerned about the societal impacts of AI. Their concerns may come from different places, the futurists starting with concern about a certain type of societal impact and then applying this to AI, and the presentists starting with an interest in a certain type of AI and then applying this to societal issues. The fact that they work in opposite directions explains their divergent viewpoints and AI emphases. But they can at least agree that the societal impacts of AI are important, worth paying attention to, and worth trying to improve, even while they disagree on which impacts to focus on. Meanwhile, there are those in both factions who are not so concerned about societal impacts, who instead prefer to focus on the AI itself. Thus, there is potential for a realignment of AI factions from futurist vs. presentist to those who are concerned about societal impacts vs. those who are not. This would seem to be the crucial distinction: between those who wish to pursue AI for its own sake or for narrow conceptions of intellectual merit, on the one hand, and on the other hand those who wish to pursue AI for the benefit of society. I will call these realigned factions the intellectualist faction and the societalist faction, rooted in the following core claims: The intellectualist AI claim: AI should be developed for its own sake, i.e. for its intellectual merit. The societalist AI claim: AI should be developed for its impacts for society. It should be clarified that societalists may often emphasize intellectual aspects of AI en route to pursuing societal benefits. This is to say that belonging to the societalist faction does not preclude one from pursuing intellectually advanced AI. There can be many opportunities to be a "good" AI researcher according to traditional science and engineering notions of intellectual merit while still working towards societal benefits. Another important clarification is that, among societalists, there will always be disagreements about which societal impacts to focus on; this is normal ethical and political debate. And just as conventional politics-the art of the possible-seeks areas of aligned interests, quid pro quo, and compromise, so too can those who disagree about the societal impacts of AI. This realignment has a big advantage in that it enables focus on promoting attention to societal impacts in general. It is straightforward to argue that presentists and futurists alike should be concerned about societal impacts. AI researchers, like other scientists and engineers or any other people, are not above the law or above morality. It is everyone's responsibility to help society.  4  The realignment has further advantages in the synergistic opportunities to be found between presentist and futurist societalists.
 Opportunities Within a realigned societalist faction, the best opportunities will generally be those in which the interests of presentists and futurists are aligned. Such opportunities require no compromise or other sacrifice. Several such opportunities are apparent.
 Social Norms A first opportunity is to encourage more AI researchers to care about the societal impacts of their work. This assumes-quite reasonably, one would think-that the societal impacts of AI tend to be better when more AI researchers aim to achieve better societal impacts. In other words, the pro-societal efforts of AI researchers tend to be productive, not counterproductive. While it is possible to persuade AI researchers one at a time to care about societal impacts, it will often be more effective to persuade many of them all together. This can be achieved by establishing societalist social norms among AI communities, i.e. by making it normal for AI researchers to seek to benefit society through their work. Societalist social norms are seen, for example, in AI researcher Stuart Russell's call for the AI field to switch from a norm of "building pure intelligence for its own sake, regardless of the associated objectives and their consequences" to a norm of not just caring about societal impacts, but also making that be "how practitioners define what they do"  (Bohannon 2015:252) . The advantage of advancing societalist social norms is that it creates more AI researchers available to work on pro-societal designs, policies, etc., and likewise fewer people pushing back against such activities. An expanded societalist faction should benefit both near-term and longterm societalist agendas: a rising tide lifts all boats. Given that AI research communities tend to focus on near-term AI, it may be advantageous to emphasize norms associated with the societal impacts of near-term AI. In practice, this means engaging AI researchers on such issues as inequality/discrimination (as in Crawford 2016), military applications (as in  Arkin 2009) , the safety of medical AI or self-driving cars, etc. It is relatively easy to sell the importance of these issues because either they already are important issues or they clearly will be in the near future, and because they do not depend on the arrival of any speculative future forms of AI but instead are rooted in existing and near-term AI. Social norms surrounding the societal impacts of near-term AI can be of indirect benefit to the societal impacts of long-term AI. One mechanism for this is the durability of norms. What is now long-term AI may one day become near-term AI. If societalist norms remain intact, then the near-term societalists would switch over to what is now long-term AI. There is reason to believe that societalist norms may be so durable. For comparison, norms against slavery or racism or sexism have proved durable; 5 within academia, the norm in favor of interdisciplinary research has gradually advanced for many years. If AI societalist norms could be similarly durable, they would help with what is now near-term and long-term AI. A second mechanism is for societalist norms to prompt AI researchers to become more interested in long-term impacts. This could happen via attention to the ethics underlying which societal impacts one might care about. Ethical reflection is a natural fit for societalist AI, i.e. societalists are likely to engage in ethical reflection. That reflection could include the ethics of future generations, which is precisely how many existing futurists came to be futurists. Therefore, a case can be made for promoting near-term societalist norms among AI communities. Because the issues are near-term, they are more likely to attract interest from AI communities. This increases the size of the near-term societalist faction. Then, some portion of these societalists may come to appreciate the ethical argument for caring about long-term impacts. This increases the size of the long-term societalist faction. Furthermore, the expanded societalist faction may remain in place when the long-term gets nearer. So, this is a win-win for both near-term and long-term societalists.
 Technical Research Societalist social norms expand the population of AI experts who can pursue the lines of technical AI research that benefits society. Some technical research may pertain only to specific AI applications and is thus only of interest to narrow presentist or futurist concerns. However, there is also some technical research that is of wide relevance to both near-term and long-term AI. Progress on this research is another win-win for near-term and long-term societalists. An example of this sort of "timeless" technical research can be found in  Amodei et al. (2016) . This paper describes a range of technical problems oriented towards avoiding unintended harms from AI systems. The paper is expressed in terms of unintended harms from near-term AI. However, in an interview, Amodei describes how he sees the technical problems as being relevant for near-term and long-term AI alike, such that the near-term/long-term distinction is unnecessary  (Conn 2016b) . Thus, all societalists could support this technical research agenda, regardless of whether they are ultimately concerned about near-term or long-term impacts. Indeed, in the same interview, Amodei further notes that his paper has received broad praise, including from people who fit into both the presentist and futurist factions  (Conn 2016b) .
 Policy Given the general orientation of policy communities towards near-term issues, it will typically be easier for policy to address issues associated with near-term AI. However, there are at least two ways that AI policy can concurrently support both near-term and long-term AI, making for a third win-win for near-term and long-term societalists. First, the process of developing near-term AI policy can create processes and competencies of relevance for long-term AI. Already, near-term AI policy issues have prompted legal scholars to familiarize themselves with some technical details of  AI (e.g., Calo 2011; Funkhouser 2013; Hammond 2015) . Such scholarship, and accompanying policy discussions, prepares policy communities for addressing long-term AI.  6  Near-term AI policy will also create demand for people with AI backgrounds working in policy positions. Such in-house AI expertise can help ensure that AI policy matches actual issues associated with AI technology and does not inadvertently restrict beneficial AI or enable harmful AI. Good models can be found, for example, in the Science & Technology Policy Fellowships offered by the American Association for the Advancement of Science and the IEEE-USA Government Fellowships offered by the Institute of Electrical and Electronics Engineers. These are both temporary fellowships; there should also be AI experts in permanent government positions. As long as the private sector offers abundant and lucrative employment for AI experts, it may be difficult to attract them to government positions. This challenge could be overcome via sufficiently strong societalist norms, which could motivate some societalists to forgo salary and pursue government positions simply because it is the right thing to do for society. Second, some specific policy measures may improve outcomes for both near-term and longterm AI. This may seem counterintuitive, given the rapidly changing nature of AI. Indeed, a common concern about policy for emerging technologies like AI is that policy will fail to keep up with changes in the technology. In response to this concern,  Moses (2007)  proposes to phrase policy in more general terms, such as "choose the AI design that is more beneficial to society"; such policy could remain relevant even if AI technology undergoes major changes. Other policies of enduring relevance can be policies that establish institutions for AI governance, including policies to establish support for pro-societal AI efforts.
 Conclusion The current division between AI futurists and presentists-i.e. those concerned mainly about long-term AI vs. those concerned mainly about near-term AI-is not the best focus of attention. Instead of bickering over which AI impacts are most important, both groups would be wise to focus on addressing the AI impacts in general. There are ample opportunities to concurrently address both near-term and long-term AI impacts, including by establishing societalist social norms within AI communities, by advancing technical research that improves societal outcomes for all types of AI, and by advancing public policy that improves the governance of all types of AI. Making progress on these three fronts will take effort, which is precisely why those who worry about AI impacts should not drain their energy on internal disputes. The Introduction to this paper quoted a passage from  Crawford (2016)  that argues for attention to near-term inequality/discrimination issues instead of to long-term threats. The passage is a representative example of the presentist vs. futurist dispute. However, in light of this paper's proposed reconciliation, the passage could be rewritten to instead take on the societalist vs. intellectualist dispute: According to some prominent voices in the tech world, artificial intelligence presents a major intellectual achievement, a triumph of science and technology. They say this achievement is to be celebrated, full stop. Indeed, research into the science of artificial intelligence has attracted millions of dollars and spawned a multitude of conferences. But this intellectual triumphalism is a distraction from the problems that artificial intelligence creates for society. It may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. It is also creating safety challenges for medical products and self-driving cars, as well as profound ethical dilemmas in its military applications. Looking further into the future, there may be some chance that artificial intelligence presents a looming existential threat to humanity. These are serious issues that demand serious attention from society as a whole and especially from the technologists whose work is at the heart of these issues. Expositions along these lines promise to reconcile the differences between presentists and futurists and instead shift attention to where it is desperately needed: on the societal issues posed by AI and on the positive steps can be taken to address these issues. 			 The racial bias Crawford describes comes from the investigative journalism of Angwin et al. (2016) .2  One can argue that the AI itself is situated within society, and thus that AI researchers inevitably work on societal issues, even when they believe that they are working only on the AI itself. However, for present purposes, what matters is that the AI researchers believe that they are focusing on the AI itself and not on societal issues, even if it can be argued that they are inadvertently working on societal issues.
			 As just one of many other examples, see Arkin (2009)  on societal issues associated with military robotics.
			 Again, as with any ethical position, the claim that people should help society is not universally held. A broader defense of this claim is beyond the scope of this paper.
			 The world still has slavery and racism and sexism, but not as much as it once did. For example, while the United States continues to grapple with a variety of racial biases, it has become unthinkable to support the "separate but equal" racial segregation of the former "Jim Crow" laws.6  There has also been some legal scholarship focused on long-termAI (e.g., McGinnis 2010; Wilson 2013) , but this is a relatively small minority of AI legal scholarship.

Abstract: Artificial intelligence (AI) experts are currently divided into "presentist" and "futurist" factions that call for attention to near-term and long-term AI, respectively. This paper argues that the presentist-futurist dispute is not the best focus of attention. Instead, the paper proposes a reconciliation between the two factions based on a mutual interest in AI. The paper further proposes a realignment to two new factions: an "intellectualist" faction that seeks to develop AI for intellectual reasons (as found in the traditional norms of computer science) and a "societalist faction" that seeks to develop AI for the benefit of society. The paper argues in favor of societalism and offers three means of concurrently addressing societal impacts from near-term and long-term AI: (1) advancing societalist social norms, thereby increasing the portion of AI researchers who seek to benefit society; (2) technical research on how to make any AI more beneficial to society; and (3) policy to improve the societal benefits of all AI. In practice, it will often be advantageous to emphasize near-term AI due to the greater interest in near-term AI among AI and policy communities alike. However, presentist and futurist societalists alike can benefit from each others' advocacy for attention to the societal impacts of AI. A reconciliation between the presentist and futurist factions can improve both near-term and long-term societal impacts of AI.