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Collecting the Public Perception of AI and Robot Rights

Proceedings of the ACM on Human-Computer Interaction 2020年第CSCW2期4卷 1-24页

作者： Lima, Gabriel Kim, Changyeon Ryu, Seungho Jeon, Chihyung Cha, Meeyoung School of Computing Kaist Data Science Group Ibs Daejeon Korea Republic of School of Computing Kaist Daejeon Korea Republic of Dept. of Computer Science Dept. of Mathematics Hkust Hong Kong SAR Hong Kong Graduate School of Science and Technology Policy Kaist Daejeon Korea Republic of Data Science Group Ibs School of Computing Kaist Daejeon Korea Republic of

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities."Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar. © 2020 ACM.

关键词： Intelligent robots

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Human perceptions on moral responsibility of ai: A case study in ai-assisted bail decision-making 21

Human perceptions on moral responsibility of ai: A case stud...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Lima, Gabriel Grgic-Hlaca, Nina Cha, Meeyoung School of Computing Kaist Data Science Group Ibs Korea Republic of

ISBN: (纸本)9781450380966

How to attribute responsibility for autonomous artifcial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people's perceptions of eight diferent notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful diference in how people perceived these agents' moral responsibility;human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these fndings, such as the need for explainable AI in high-stakes scenarios. © 2021 ACM.

关键词： Decision making

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Detection and Localization of Stationary Waves on Venus Using a Self-Supervised Anomaly Detection Model

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Husnu Baris Baydargil Jose Eduardo Oliveira Silva Yeon Joo Lee Meeyoung Cha Data Science Group Institute for Basic Science (IBS) Daejeon Republic of Korea Planetary Atmospheres Group Institute for Basic Science (IBS) Daejeon Republic of Korea School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Max Planck Institute for Security and Privacy (MPI-SP) Bochum Germany

The atmosphere of Venus features stationary waves of planetary scale, which are believed to be caused by wind flowing over elevated terrains. Detecting these waves can provide valuable insight into atmospheric dynamics. However, manual detection is time-consuming, especially with the increasing volume of new images. To address this, we propose a self-supervised anomaly detection model based on a vector-quantized generative adversarial network to identify and localize stationary atmospheric waves in spacecraft images of Venus. Our model is developed over Akatsuki images of Venus and is trained on smaller cropped images containing features other than stationary waves, allowing it to learn the underlying patterns of normal cloud features. We enhance the model with spatial attention modules, skip connections, and latent space transformation to improve image reconstruction and avoid mixing stationary wave features with artifacts. For inference, we use an overlapping sliding window approach, considering the reconstruction loss of each grid slice to identify anomalies. The model records bounding boxes with high reconstruction loss, localizes stationary waves, and draws the final bounding box. Experiments demonstrate that our model outperforms benchmark models using ultraviolet imaging (UVI) and longwave infrared imaging (LIR) acquired by the Akatsuki Venus orbiter, achieving an area under the curve (AUC) score of 94.78% for UVI and 92.84% for LIR images, and an intersection over union score of 66.53% for UVI and 61.49% for LIR images. The ablation study shows consecutive performance improvements with each added module. Propose a self-supervised model for detecting stationary atmospheric waves on Venus through anomaly detection in Akatsuki images Localize stationary waves by analyzing overlapping image patches, identifying regions with significant original-reconstruction deviations Streamline the identification of stationary waves, eliminating the constraints of date and or

关键词： Venus stationary wave deep learning anomaly detection localization

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Collecting the Public Perception of AI and Robot Rights

Collecting the Public Perception of AI and Robot Rights

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Proceedings of the ACM on Human-Computer Interaction

关键词： Artificial intelligence Autonomous agents Intelligent agents Robots Autonomous electronics Electronic agents European Parliament First impressions General population Online users Public perception Representative sample

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The Conflict Between Explainable and Accountable Decision-Making Algorithms 22

The Conflict Between Explainable and Accountable Decision-Ma...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Lima, Gabriel Grgić-Hlača, Nina Jeong, Jin Keun Cha, Meeyoung School of Computing Kaist Data Science Group Korea Republic of Max Planck Institute for Software Systems & Max Planck Institute for Research on Collective Goods Germany Kangwon National University Korea Republic of Data Science Group Ibs & School of Computing Kaist Korea Republic of

ISBN: (纸本)9781450393522

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility. © 2022 ACM.

关键词： Decision making

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Self-explaining deep models with logic rule reasoning 22

Self-explaining deep models with logic rule reasoning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Seungeon Lee Xiting Wang Sungwon Han Xiaoyuan Yi Xing Xie Meeyoung Cha KAIST School of Computing IBS Data Science Group Social Computing Group Microsoft Research Asia IBS Data Science Group KAIST School of Computing

ISBN: (纸本)9781713871088

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust and allows users to collaborate closely with the model. We demonstrate that logic rule explanations naturally satisfy human precision with the expressive power required for good predictive performance. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of deep learning models.

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Descriptive AI ethics: Collecting and understanding the public opinion

arXiv

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arXiv 2021年

作者： Lima, Gabriel Cha, Meeyoung School of Computing KAIST and Data Science Group IBS Korea Republic of Data Science Group IBS and School of Computing KAIST Korea Republic of

There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems. The current debate on the responsibility gap posed by these systems is one such example. This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other, by aiding scholarly discussion with data gathered from the public. We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems’ deployment. Copyright © 2021, The Authors. All rights reserved.

关键词： Social aspects

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Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity 23

Transformer as a hippocampal memory consolidation model base...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dong-Kyum Kim Jea Kwon Meeyoung Cha C. Justin Lee IBS Data Science Group IBS Center for Cognition and Sociality IBS Data Science Group and KAIST School of Computing

The hippocampus plays a critical role in learning, memory, and spatial representation, processes that depend on the NMDA receptor (NMDAR). Inspired by recent findings that compare deep learning models to the hippocampus, we propose a new nonlinear activation function that mimics NMDAR dynamics. NMDAR-like nonlinearity shifts short-term working memory into long-term reference memory in transformers, thus enhancing a process that is similar to memory consolidation in the mammalian brain. We design a navigation task assessing these two memory functions and show that manipulating the activation function (i.e., mimicking the Mg2+-gating of NMDAR) disrupts long-term memory processes. Our experiments suggest that place cell-like functions and reference memory reside in the feed-forward network layer of transformers and that nonlinearity drives these processes. We discuss the role of NMDAR-like nonlinearity in establishing this striking resemblance between transformer architecture and hippocampal spatial representation.

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Elsa: Energy-based learning for semi-supervised anomaly detection

arXiv

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arXiv 2021年

作者： Han, Sungwon Song, Hyeonho Lee, Seungeon Park, Sungwon Cha, Meeyoung School of Computing KAIST Korea Republic of Data Science Group IBS Korea Republic of

Anomaly detection aims at identifying deviant instances from the normal data distribution. Many advances have been made in the field, including the innovative use of unsupervised contrastive learning. However, existing methods generally assume clean training data and are limited when the data contain unknown anomalies. This paper presents Elsa, a novel semi-supervised anomaly detection approach that unifies the concept of energy-based models with unsupervised contrastive learning. Elsa instills robustness against any data contamination by a carefully designed fine-tuning step based on the new energy function that forces the normal data to be divided into classes of prototypes. Experiments on multiple contamination scenarios show the proposed model achieves SOTA performance. Extensive analyses also verify the contribution of each component in the proposed model. Beyond the experiments, we also offer a theoretical interpretation of why contrastive learning alone cannot detect anomalies under data contamination. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

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Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm

arXiv

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arXiv 2023年

作者： Lima, Gabriel Grgić-Hlača, Nina Cha, Meeyoung School of Computing KAIST & Data Science Group IBS Korea Republic of Max Planck Institute for Software Systems Max Planck Institute for Research on Collective Goods Germany Data Science Group IBS & School of Computing KAIST Korea Republic of

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people’s reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people’s reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople’s reactions to AI-caused harm. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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