18 research outputs found
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The potential harms of online targeting
Despite increasing attention being paid to the potential harms of online targeting over the last year, there is still a lack of clarity over what precisely those harms are. To help address this lack of clarity, this submission focuses on question 1: What evidence is there about the harms and benefits of online targeting? This question was discussed at a workshop we held on “The Methodology and Ethics of Targeting” at the Leverhulme Centre for the Future of Intelligence in May 2019 (organized by the authors of this submission, and attended by some members of the CDEI). Our submission summarises some of these discussions and attempts to map out some of the key researchers, groups, and publications we know are working on various harms of targeting. This is not intended to be comprehensive, but we hope will help highlight areas worthy of more attention for the CDEI.Leverhulme Trust, under Grant RC-2015-067
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The Societal Implications of Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) is an avenue of research in Artificial Intelligence (AI) that has received increasing attention within the research community in recent years, and is beginning to show potential for real-world application. DRL is one of the most promising routes towards developing more autonomous AI systems that interact with and take actions in complex real-world environments, and can more flexibly solve a range of problems for which we may not be able to precisely specify a correct ‘answer’. This could have substantial implications for people’s lives: for example by speeding up automation in various sectors, changing the nature and potential harms of online influence, or introducing new safety risks in physical infrastructure. In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL’s societal implications.



This article appears in the special track on AI and Society.


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Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI
We propose a method for identifying early warning signs of transformative progress in artificial intelligence (AI), and discuss how these can support the anticipatory and democratic governance of AI. We call these early
warning signs ‘canaries’, based on the use of canaries to provide early warnings of unsafe air pollution in coal
mines. Our method combines expert elicitation and collaborative causal graphs to identify key milestones
and identify the relationships between them. We present two illustrations of how this method could be
used: to identify early warnings of harmful impacts of language models; and of progress towards high-level
machine intelligence. Identifying early warning signs of transformative applications can support more efficient
monitoring and timely regulation of progress in AI: as AI advances, its impacts on society may be too great to
be governed retrospectively. It is essential that those impacted by AI have a say in how it is governed. Early
warnings can give the public time and focus to influence emerging technologies using democratic, participatory
technology assessments. We discuss the challenges in identifying early warning signals and propose directions
for future work
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Reconfiguring Resilience for Existential Risk: Submission of Evidence to the Cabinet Office on the new UK National Resilience Strategy
This submission provides input on the UK Government's National Resilience Strategy Call for Evidence, which sought “public engagement to inform the development of a new Strategy that will outline an ambitious new vision for UK National Resilience and set objectives for achieving it.” In response, an interdisciplinary team of experts at the Centre for the Study of Existential Risk worked to prepare a concrete response to this call. In this document, we aim to share the contents of our submission for public deliberation.
While we laud the UK government's inititiative to develop a new National Resilience Strategy, we argue that more work can and should be done to categorize and identify catastrophic, and existential risks; we emphasize the importance of taking a long-term perspective on mitigating and responding to the challenges these pose; and we encourage the development of a more comprehensive strategy, as these risks are all intertwined in an interconnected and complex environment.
In our responses, we focus on the six broad thematic areas of the National Resilience Strategy (Risk and Resilience, Responsibilities and Accountability, Partnerships, Community, Investment, and Resilience in an Interconnected World), and provide key recommendations for improving UK national resilience, both from a general perspective on existential and global catastrophic risks, as well as with regards to policies in key risk domains such as in biorisk, climate risk, or emerging technologies within critical national infrastructure & - defence systems.
While we laud the UK government's initial to develop a new National Resilience Strategy, we argue that more work can and should be done to categorize and identify catastrophic, complex, and existential risks; we emphasize a long-term perspective on mitigating and responding to the threats these pose; and we encourage the development of a more comprehensive strategy, as these risks are all intertwined in an interconnected and complex environment.
In our responses, we focus on the six broad thematic areas of the National Resilience Strategy (Risk and Resilience, Responsibilities and Accountability, Partnerships, Community, Investment, and Resilience in an Interconnected World), and provide key recommendations for improving UK national resilience, both from a general perspective on existential and global catastrophic risks, as well as with regards to policies in key risk domains such as in biorisk, climate risk, or emerging technologies within critical national infrastructure & - defence systems
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Overcoming Barriers to Cross-cultural Cooperation in AI Ethics and Governance
Abstract: Achieving the global benefits of artificial intelligence (AI) will require international cooperation on many areas of governance and ethical standards, while allowing for diverse cultural perspectives and priorities. There are many barriers to achieving this at present, including mistrust between cultures, and more practical challenges of coordinating across different locations. This paper focuses particularly on barriers to cooperation between Europe and North America on the one hand and East Asia on the other, as regions which currently have an outsized impact on the development of AI ethics and governance. We suggest that there is reason to be optimistic about achieving greater cross-cultural cooperation on AI ethics and governance. We argue that misunderstandings between cultures and regions play a more important role in undermining cross-cultural trust, relative to fundamental disagreements, than is often supposed. Even where fundamental differences exist, these may not necessarily prevent productive cross-cultural cooperation, for two reasons: (1) cooperation does not require achieving agreement on principles and standards for all areas of AI; and (2) it is sometimes possible to reach agreement on practical issues despite disagreement on more abstract values or principles. We believe that academia has a key role to play in promoting cross-cultural cooperation on AI ethics and governance, by building greater mutual understanding, and clarifying where different forms of agreement will be both necessary and possible. We make a number of recommendations for practical steps and initiatives, including translation and multilingual publication of key documents, researcher exchange programmes, and development of research agendas on cross-cultural topics
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produce
systems with both beneficial and harmful capabilities. Further progress in AI
development could lead to capabilities that pose extreme risks, such as
offensive cyber capabilities or strong manipulation skills. We explain why
model evaluation is critical for addressing extreme risks. Developers must be
able to identify dangerous capabilities (through "dangerous capability
evaluations") and the propensity of models to apply their capabilities for harm
(through "alignment evaluations"). These evaluations will become critical for
keeping policymakers and other stakeholders informed, and for making
responsible decisions about model training, deployment, and security