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Search</a> </div> </div> <input type="hidden" name="order" value="-announced_date_first"> <input type="hidden" name="size" value="50"> </form> <div class="level breathe-horizontal"> <div class="level-left"> <form method="GET" action="/search/"> <div style="display: none;"> <select id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option 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class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</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="2409.13168v1-abstract-short" style="display: inline;"> This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distributio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13168v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13168v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13168v1-abstract-full" style="display: none;"> This paper examines the profound challenges that transformative advances in AI towards Artificial General Intelligence (AGI) will pose for economists and economic policymakers. I examine how the Age of AI will revolutionize the basic structure of our economies by diminishing the role of labor, leading to unprecedented productivity gains but raising concerns about job disruption, income distribution, and the value of education and human capital. I explore what roles may remain for labor post-AGI, and which production factors will grow in importance. The paper then identifies eight key challenges for economic policy in the Age of AI: (1) inequality and income distribution, (2) education and skill development, (3) social and political stability, (4) macroeconomic policy, (5) antitrust and market regulation, (6) intellectual property, (7) environmental implications, and (8) global AI governance. It concludes by emphasizing how economists can contribute to a better understanding of these challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13168v1-abstract-full').style.display = 'none'; document.getElementById('2409.13168v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </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">26 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.09932">arXiv:2404.09932</a> <span> [<a href="https://arxiv.org/pdf/2404.09932">pdf</a>, <a href="https://arxiv.org/format/2404.09932">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> <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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Foundational Challenges in Assuring Alignment and Safety of Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Anwar%2C+U">Usman Anwar</a>, <a href="/search/cs?searchtype=author&query=Saparov%2C+A">Abulhair Saparov</a>, <a href="/search/cs?searchtype=author&query=Rando%2C+J">Javier Rando</a>, <a href="/search/cs?searchtype=author&query=Paleka%2C+D">Daniel Paleka</a>, <a href="/search/cs?searchtype=author&query=Turpin%2C+M">Miles Turpin</a>, <a href="/search/cs?searchtype=author&query=Hase%2C+P">Peter Hase</a>, <a href="/search/cs?searchtype=author&query=Lubana%2C+E+S">Ekdeep Singh Lubana</a>, <a href="/search/cs?searchtype=author&query=Jenner%2C+E">Erik Jenner</a>, <a href="/search/cs?searchtype=author&query=Casper%2C+S">Stephen Casper</a>, <a href="/search/cs?searchtype=author&query=Sourbut%2C+O">Oliver Sourbut</a>, <a href="/search/cs?searchtype=author&query=Edelman%2C+B+L">Benjamin L. Edelman</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zhaowei Zhang</a>, <a href="/search/cs?searchtype=author&query=G%C3%BCnther%2C+M">Mario G眉nther</a>, <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</a>, <a href="/search/cs?searchtype=author&query=Hernandez-Orallo%2C+J">Jose Hernandez-Orallo</a>, <a href="/search/cs?searchtype=author&query=Hammond%2C+L">Lewis Hammond</a>, <a href="/search/cs?searchtype=author&query=Bigelow%2C+E">Eric Bigelow</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+A">Alexander Pan</a>, <a href="/search/cs?searchtype=author&query=Langosco%2C+L">Lauro Langosco</a>, <a href="/search/cs?searchtype=author&query=Korbak%2C+T">Tomasz Korbak</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+H">Heidi Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhong%2C+R">Ruiqi Zhong</a>, <a href="/search/cs?searchtype=author&query=h%C3%89igeartaigh%2C+S+%C3%93">Se谩n 脫 h脡igeartaigh</a>, <a href="/search/cs?searchtype=author&query=Recchia%2C+G">Gabriel Recchia</a>, <a href="/search/cs?searchtype=author&query=Corsi%2C+G">Giulio Corsi</a> , et al. (17 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="2404.09932v2-abstract-short" style="display: inline;"> This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.09932v2-abstract-full" style="display: none;"> This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.09932v2-abstract-full').style.display = 'none'; document.getElementById('2404.09932v2-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> 5 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12107">arXiv:2403.12107</a> <span> [<a href="https://arxiv.org/pdf/2403.12107">pdf</a>, <a href="https://arxiv.org/format/2403.12107">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="General Economics">econ.GN</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"> Scenarios for the Transition to AGI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</a>, <a href="/search/cs?searchtype=author&query=Suh%2C+D">Donghyun Suh</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="2403.12107v1-abstract-short" style="display: inline;"> We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to au… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12107v1-abstract-full').style.display = 'inline'; document.getElementById('2403.12107v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12107v1-abstract-full" style="display: none;"> We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If the distribution of task complexity exhibits a sufficiently thick infinite tail, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12107v1-abstract-full').style.display = 'none'; document.getElementById('2403.12107v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09227">arXiv:2311.09227</a> <span> [<a href="https://arxiv.org/pdf/2311.09227">pdf</a>, <a href="https://arxiv.org/format/2311.09227">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="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seger%2C+E">Elizabeth Seger</a>, <a href="/search/cs?searchtype=author&query=Dreksler%2C+N">Noemi Dreksler</a>, <a href="/search/cs?searchtype=author&query=Moulange%2C+R">Richard Moulange</a>, <a href="/search/cs?searchtype=author&query=Dardaman%2C+E">Emily Dardaman</a>, <a href="/search/cs?searchtype=author&query=Schuett%2C+J">Jonas Schuett</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+K">K. Wei</a>, <a href="/search/cs?searchtype=author&query=Winter%2C+C">Christoph Winter</a>, <a href="/search/cs?searchtype=author&query=Arnold%2C+M">Mackenzie Arnold</a>, <a href="/search/cs?searchtype=author&query=h%C3%89igeartaigh%2C+S+%C3%93">Se谩n 脫 h脡igeartaigh</a>, <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</a>, <a href="/search/cs?searchtype=author&query=Anderljung%2C+M">Markus Anderljung</a>, <a href="/search/cs?searchtype=author&query=Bucknall%2C+B">Ben Bucknall</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+A">Alan Chan</a>, <a href="/search/cs?searchtype=author&query=Stafford%2C+E">Eoghan Stafford</a>, <a href="/search/cs?searchtype=author&query=Koessler%2C+L">Leonie Koessler</a>, <a href="/search/cs?searchtype=author&query=Ovadya%2C+A">Aviv Ovadya</a>, <a href="/search/cs?searchtype=author&query=Garfinkel%2C+B">Ben Garfinkel</a>, <a href="/search/cs?searchtype=author&query=Bluemke%2C+E">Emma Bluemke</a>, <a href="/search/cs?searchtype=author&query=Aird%2C+M">Michael Aird</a>, <a href="/search/cs?searchtype=author&query=Levermore%2C+P">Patrick Levermore</a>, <a href="/search/cs?searchtype=author&query=Hazell%2C+J">Julian Hazell</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+A">Abhishek Gupta</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="2311.09227v1-abstract-short" style="display: inline;"> Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09227v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09227v1-abstract-full" style="display: none;"> Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09227v1-abstract-full').style.display = 'none'; document.getElementById('2311.09227v1-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> 29 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Official release at https://www.governance.ai/research-paper/open-sourcing-highly-capable-foundation-models</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.01550">arXiv:2311.01550</a> <span> [<a href="https://arxiv.org/pdf/2311.01550">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="General Economics">econ.GN</span> </div> </div> <p class="title is-5 mathjax"> Market Concentration Implications of Foundation Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vipra%2C+J">Jai Vipra</a>, <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</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="2311.01550v1-abstract-short" style="display: inline;"> We analyze the structure of the market for foundation models, i.e., large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets. This calls for a two-pronged regulatory resp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.01550v1-abstract-full').style.display = 'inline'; document.getElementById('2311.01550v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.01550v1-abstract-full" style="display: none;"> We analyze the structure of the market for foundation models, i.e., large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets. This calls for a two-pronged regulatory response: (i) Antitrust authorities need to ensure the contestability of the market by tackling strategic behavior, in particular by ensuring that monopolies do not propagate vertically to downstream uses, and (ii) given the diminished potential for market discipline, there is a role for regulators to ensure that the most capable models meet sufficient quality standards (including safety, privacy, non-discrimination, reliability and interoperability standards) to maximally contribute to social welfare. Regulators should also ensure a level regulatory playing field between AI and non-AI applications in all sectors of the economy. For models that are behind the frontier, we expect competition to be quite intense, implying a more limited role for competition policy, although a role for regulation remains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.01550v1-abstract-full').style.display = 'none'; document.getElementById('2311.01550v1-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> 2 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Working Paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.03718">arXiv:2307.03718</a> <span> [<a href="https://arxiv.org/pdf/2307.03718">pdf</a>, <a href="https://arxiv.org/format/2307.03718">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> </div> </div> <p class="title is-5 mathjax"> Frontier AI Regulation: Managing Emerging Risks to Public Safety </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Anderljung%2C+M">Markus Anderljung</a>, <a href="/search/cs?searchtype=author&query=Barnhart%2C+J">Joslyn Barnhart</a>, <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</a>, <a href="/search/cs?searchtype=author&query=Leung%2C+J">Jade Leung</a>, <a href="/search/cs?searchtype=author&query=O%27Keefe%2C+C">Cullen O'Keefe</a>, <a href="/search/cs?searchtype=author&query=Whittlestone%2C+J">Jess Whittlestone</a>, <a href="/search/cs?searchtype=author&query=Avin%2C+S">Shahar Avin</a>, <a href="/search/cs?searchtype=author&query=Brundage%2C+M">Miles Brundage</a>, <a href="/search/cs?searchtype=author&query=Bullock%2C+J">Justin Bullock</a>, <a href="/search/cs?searchtype=author&query=Cass-Beggs%2C+D">Duncan Cass-Beggs</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+B">Ben Chang</a>, <a href="/search/cs?searchtype=author&query=Collins%2C+T">Tantum Collins</a>, <a href="/search/cs?searchtype=author&query=Fist%2C+T">Tim Fist</a>, <a href="/search/cs?searchtype=author&query=Hadfield%2C+G">Gillian Hadfield</a>, <a href="/search/cs?searchtype=author&query=Hayes%2C+A">Alan Hayes</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+L">Lewis Ho</a>, <a href="/search/cs?searchtype=author&query=Hooker%2C+S">Sara Hooker</a>, <a href="/search/cs?searchtype=author&query=Horvitz%2C+E">Eric Horvitz</a>, <a href="/search/cs?searchtype=author&query=Kolt%2C+N">Noam Kolt</a>, <a href="/search/cs?searchtype=author&query=Schuett%2C+J">Jonas Schuett</a>, <a href="/search/cs?searchtype=author&query=Shavit%2C+Y">Yonadav Shavit</a>, <a href="/search/cs?searchtype=author&query=Siddarth%2C+D">Divya Siddarth</a>, <a href="/search/cs?searchtype=author&query=Trager%2C+R">Robert Trager</a>, <a href="/search/cs?searchtype=author&query=Wolf%2C+K">Kevin Wolf</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="2307.03718v4-abstract-short" style="display: inline;"> Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "frontier AI" models: highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety. Frontier AI models pose a distinct regulatory challenge: dangerous capabilit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03718v4-abstract-full').style.display = 'inline'; document.getElementById('2307.03718v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.03718v4-abstract-full" style="display: none;"> Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "frontier AI" models: highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety. Frontier AI models pose a distinct regulatory challenge: dangerous capabilities can arise unexpectedly; it is difficult to robustly prevent a deployed model from being misused; and, it is difficult to stop a model's capabilities from proliferating broadly. To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models. Industry self-regulation is an important first step. However, wider societal discussions and government intervention will be needed to create standards and to ensure compliance with them. We consider several options to this end, including granting enforcement powers to supervisory authorities and licensure regimes for frontier AI models. Finally, we propose an initial set of safety standards. These include conducting pre-deployment risk assessments; external scrutiny of model behavior; using risk assessments to inform deployment decisions; and monitoring and responding to new information about model capabilities and uses post-deployment. We hope this discussion contributes to the broader conversation on how to balance public safety risks and innovation benefits from advances at the frontier of AI development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.03718v4-abstract-full').style.display = 'none'; document.getElementById('2307.03718v4-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> 7 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">Update July 11th: - Added missing footnote back in. - Adjusted author order (mistakenly non-alphabetical among the first 6 authors) and adjusted affiliations (Jess Whittlestone's affiliation was mistagged and Gillian Hadfield had SRI added to her affiliations) Updated September 4th: Various typos</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.04279">arXiv:2205.04279</a> <span> [<a href="https://arxiv.org/pdf/2205.04279">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"> Aligned with Whom? Direct and social goals for AI systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</a>, <a href="/search/cs?searchtype=author&query=Balwit%2C+A">Avital Balwit</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="2205.04279v1-abstract-short" style="display: inline;"> As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem - how to ensure that AI systems pursue the goals that we want them to pursue - has garnered growing attention. This article distinguishes two types of alignment problems depending on whose goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04279v1-abstract-full').style.display = 'inline'; document.getElementById('2205.04279v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.04279v1-abstract-full" style="display: none;"> As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem - how to ensure that AI systems pursue the goals that we want them to pursue - has garnered growing attention. This article distinguishes two types of alignment problems depending on whose goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers whether an AI system accomplishes the goals of the entity operating it. In contrast, the social alignment problem considers the effects of an AI system on larger groups or on society more broadly. In particular, it also considers whether the system imposes externalities on others. Whereas solutions to the direct alignment problem center around more robust implementation, social alignment problems typically arise because of conflicts between individual and group-level goals, elevating the importance of AI governance to mediate such conflicts. Addressing the social alignment problem requires both enforcing existing norms on their developers and operators and designing new norms that apply directly to AI systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04279v1-abstract-full').style.display = 'none'; document.getElementById('2205.04279v1-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> 9 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </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">Prepared for the Oxford Handbook of AI Governance (23 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/2105.08475">arXiv:2105.08475</a> <span> [<a href="https://arxiv.org/pdf/2105.08475">pdf</a>, <a href="https://arxiv.org/format/2105.08475">other</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="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Economics">econ.GN</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3461702.3462619">10.1145/3461702.3462619 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AI and Shared Prosperity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Klinova%2C+K">Katya Klinova</a>, <a href="/search/cs?searchtype=author&query=Korinek%2C+A">Anton Korinek</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="2105.08475v1-abstract-short" style="display: inline;"> Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to addition… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.08475v1-abstract-full').style.display = 'inline'; document.getElementById('2105.08475v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.08475v1-abstract-full" style="display: none;"> Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.08475v1-abstract-full').style.display = 'none'; document.getElementById('2105.08475v1-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> 18 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.4; K.4.1 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES '21) </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a> </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary"> <!-- MetaColumn 1 --> <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 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