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Self Supervised Vision for Climate Downscaling

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arXiv 2024年

作者： Singh, Karandeep Park, Sungwon Jeong, Chaeyoon Nellikkattil, Arjun Cha, Meeyoung Shidqi, Naufal Zeller, Elke Data Science Group IBS Korea Republic of School of Computing KAIST Korea Republic of Department of Climate System PNU Korea Republic of IBS & School of Computing KAIST Korea Republic of

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth’s weather and climate patterns with an increased frequency of unpredictable and extreme weather events. Future projections for climate change research are based on Earth System Models (ESMs), the computer models that simulate the Earth’s climate system. ESMs provide a framework to integrate various physical systems, but their output is bound by the enormous computational resources required for running and archiving higher-resolution simulations. For a given resource budget, the ESMs are generally run on a coarser grid, followed by a computationally lighter "downscaling" process to obtain a finer-resolution output. In this work, we present a deep-learning model for downscaling ESM simulation data that does not require high-resolution ground truth data for model optimization. This is realized by leveraging salient data distribution patterns and the hidden dependencies between weather variables for an individual data point at runtime. Extensive evaluation with 2x, 3x, and 4x scaling factors demonstrates that the proposed model consistently obtains superior performance over that of various baselines. The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Earth system models

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

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

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

The Conflict Between Explainable and Accountable Decision-Making Algorithms 22

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

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

Self-explaining deep models with logic rule reasoning 22

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Self-explaining deep models with logic rule reasoning

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

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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

Collecting the Public Perception of AI and Robot Rights

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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

Descriptive AI ethics: Collecting and understanding the public opinion

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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

The Conflict Between Explainable and Accountable Decision-Making Algorithms

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arXiv 2022年

作者： Lima, Gabriel Grgić-Hlača, Nina Jeong, Jin Keun 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 Kangwon National University Korea Republic of Data Science Group IBS School of Computing KAIST Korea Republic of

关键词： Decision making

Neural Classification of Terrestrial Biomes

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Neural Classification of Terrestrial Biomes

International Conference on Big data and Smart computing (BIGCOMP)

作者： Vyacheslav Shen Dong-Kyum Kim Elke Zeller Meeyoung Cha School of Computing KAIST Daejeon South Korea Data Science Group IBS Daejeon South Korea Center for Climate Physics IBS Busan South Korea Department of Climate System PNU Busan South Korea

Predicting vegetation changes under climate change is crucial because it will alter the distribution of different plants and have repercussions for ecosystems. To detect changes in vegetation, we employ biome classification that assigns vegetation distributions to specific biomes. Conventional methods have used empirical formulas or simple vegetation models. Based on previous research that showed the use of convolutional neural networks (CNN), this work employs multiple deep models to classify biomes with the goal of predicting future changes. Experiments over multiple datasets demonstrate that Transformer models can be a suitable alternative to the CNN model. In addition, we observe that the use of additional climate variables helps improve the prediction accuracy without overfitting the data, which previous studies have not considered. We discuss the future directions of machine learning for biome classification as a complement to traditional biome classification methods.

关键词： Climate change Classification Vegetation mapping Predictive models Convolutional neural networks Meteorology Machine learning Ecosystems Biomes