AI Safety Subproblems for Software Engineering Researchers
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[1] Kevin Jesse,et al. Large Language Models and Simple, Stupid Bugs , 2023, 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR).
[2] Tom B. Brown,et al. The Capacity for Moral Self-Correction in Large Language Models , 2023, ArXiv.
[3] Daniel Buschek,et al. Co-Writing with Opinionated Language Models Affects Users’ Views , 2023, CHI.
[4] M. Babar,et al. A Survey on Data-driven Software Vulnerability Assessment and Prioritization , 2021, ACM Comput. Surv..
[5] Tom B. Brown,et al. Constitutional AI: Harmlessness from AI Feedback , 2022, ArXiv.
[6] T. Zimmermann,et al. “It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation Tools , 2022, ACM Trans. Interact. Intell. Syst..
[7] Rohin Shah,et al. Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals , 2022, ArXiv.
[8] Richard Yuanzhe Pang,et al. What Do NLP Researchers Believe? Results of the NLP Community Metasurvey , 2022, ACL.
[9] Shuvendu K. Lahiri,et al. Interactive Code Generation via Test-Driven User-Intent Formalization , 2022, ArXiv.
[10] Tom B. Brown,et al. Language Models (Mostly) Know What They Know , 2022, ArXiv.
[11] Dan Hendrycks,et al. X-Risk Analysis for AI Research , 2022, ArXiv.
[12] S. Sundar,et al. Designing for Responsible Trust in AI Systems: A Communication Perspective , 2022, FAccT.
[13] Tom B. Brown,et al. Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback , 2022, ArXiv.
[14] Ayça Kolukısa Tarhan,et al. Systematic literature review on software quality for AI-based software , 2022, Empirical Software Engineering.
[15] Ryan J. Lowe,et al. Training language models to follow instructions with human feedback , 2022, NeurIPS.
[16] Junchao Wang,et al. A Survey of Automatic Source Code Summarization , 2022, Symmetry.
[17] T. Besiroglu,et al. Compute Trends Across Three Eras of Machine Learning , 2022, 2022 International Joint Conference on Neural Networks (IJCNN).
[18] Michael Matthews,et al. The Alignment Problem: Machine Learning and Human Values , 2022, Personnel Psychology.
[19] Foutse Khomh,et al. How to certify machine learning based safety-critical systems? A systematic literature review , 2021, Automated Software Engineering.
[20] Nan Duan,et al. Learning to Complete Code with Sketches , 2021, ICLR.
[21] Xavier Franch,et al. Software Engineering for AI-Based Systems: A Survey , 2021, ACM Trans. Softw. Eng. Methodol..
[22] M. Kirikova,et al. Challenges of Low-Code/No-Code Software Development: A Literature Review , 2022, International Workshop on Bibliometric-enhanced Information Retrieval.
[23] Po-Sen Huang,et al. Ethical and social risks of harm from Language Models , 2021, ArXiv.
[24] Florian Saurwein,et al. Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms , 2021, Media and Communication.
[25] Ziwei Liu,et al. Generalized Out-of-Distribution Detection: A Survey , 2021, International Journal of Computer Vision.
[26] Jan Leike,et al. Recursively Summarizing Books with Human Feedback , 2021, ArXiv.
[27] Wojciech Zaremba,et al. Evaluating Large Language Models Trained on Code , 2021, ArXiv.
[28] Allan Dafoe,et al. Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers , 2021, J. Artif. Intell. Res..
[29] Roman V. Yampolskiy,et al. AI Risk Skepticism , 2021, ArXiv.
[30] Zheng Leong Chua,et al. Scalable Quantitative Verification for Deep Neural Networks , 2020, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).
[31] Yogesh Kumar Dwivedi,et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy , 2019, International Journal of Information Management.
[32] Christian Bird,et al. Today Was a Good Day: The Daily Life of Software Developers , 2019, IEEE Transactions on Software Engineering.
[33] Eric D. Ragan,et al. A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems , 2018, ACM Trans. Interact. Intell. Syst..
[34] J. Pfau,et al. Objective Robustness in Deep Reinforcement Learning , 2021, ArXiv.
[35] Seok-Won Lee,et al. Multilayered review of safety approaches for machine learning-based systems in the days of AI , 2021, J. Syst. Softw..
[36] Jonathan Stray,et al. Aligning AI Optimization to Community Well-Being , 2020, International Journal of Community Well-Being.
[37] Gerson Sunyé,et al. Challenges & opportunities in low-code testing , 2020, MoDELS.
[38] Andrew Critch,et al. AI Research Considerations for Human Existential Safety (ARCHES) , 2020, ArXiv.
[39] Mario Brcic,et al. AI safety: state of the field through quantitative lens , 2020, 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO).
[40] Simos Gerasimou,et al. Importance-Driven Deep Learning System Testing , 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).
[41] Daniel S. Weld,et al. S2ORC: The Semantic Scholar Open Research Corpus , 2020, ACL.
[42] Stuart Russell. Human Compatible: Artificial Intelligence and the Problem of Control , 2019 .
[43] Christoph Treude,et al. Automatic Generation of Pull Request Descriptions , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[44] Scott Garrabrant,et al. Risks from Learned Optimization in Advanced Machine Learning Systems , 2019, ArXiv.
[45] Anne Lauscher. Life 3.0: being human in the age of artificial intelligence , 2019, Internet Histories.
[46] Tim Miller,et al. Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..
[47] Onur Ozdemir,et al. Automated Vulnerability Detection in Source Code Using Deep Representation Learning , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).
[48] John Salvatier,et al. When Will AI Exceed Human Performance? Evidence from AI Experts , 2017, ArXiv.
[49] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[50] Shane Legg,et al. Deep Reinforcement Learning from Human Preferences , 2017, NIPS.
[51] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[52] N. Soares,et al. Agent Foundations for Aligning Machine Intelligence with Human Interests: A Technical Research Agenda , 2017 .
[53] Stuart J. Russell,et al. Research Priorities for Robust and Beneficial Artificial Intelligence , 2015, AI Mag..
[54] Manish Mahajan,et al. Proof carrying code , 2015 .
[55] Roman V Yampolskiy,et al. Responses to catastrophic AGI risk: a survey , 2014 .
[56] N. Oreskes,et al. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming , 2010 .
[57] Shane Legg,et al. A Collection of Definitions of Intelligence , 2007, AGI.
[58] N Wiener,et al. Some moral and technical consequences of automation , 1960, Science.