暂无分享,去创建一个
Emily Denton | Alex Hanna | Andrew Smart | Ben Hutchinson | Parker Barnes | Oddur Kjartansson | Margaret Mitchell | Emily L. Denton | Christina Greer | Margaret Mitchell | Parker Barnes | B. Hutchinson | A. Hanna | A. Smart | Christina Greer | Oddur Kjartansson
[1] G. Ryle. I.—Knowing How and Knowing that: The Presidential Address , 1946 .
[2] H. Rittel,et al. Dilemmas in a general theory of planning , 1973 .
[3] Herbert A. Simon,et al. The Structure of Ill Structured Problems , 1973, Artif. Intell..
[4] B. Latour,et al. Laboratory Life: The Construction of Scientific Facts , 1979 .
[5] Ian Hacking,et al. Representing and Intervening: Introductory Topics in the Philosophy of Natural Science , 1983 .
[6] T. Nagel. The view from nowhere , 1987 .
[7] B. Latour. Science in action : how to follow scientists and engineers through society , 1989 .
[8] D. Haraway. Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective , 1988 .
[9] Heinz Eulau. Crossroads of Social Science: The ICPSR 25th Anniversary Volume , 1989 .
[10] Standard Glossary of Software Engineering Terminology , 1990 .
[11] Richard Buchanan,et al. Wicked Problems in Design Thinking , 1992 .
[12] Douglas Biber,et al. Representativeness in corpus design , 1993 .
[13] Dale Goodhue,et al. Develop Long-Term Competitiveness through IT Assets , 1996 .
[14] H. Nissenbaum. Accountability in a computerized society , 1997 .
[15] Eldon C. Hall. Journey to the Moon: The History of the Apollo Guidance Computer , 1996 .
[16] P. Kidwell. Journey to the moon: the history of the Apollo guidance computer , 1999, IEEE Annals of the History of Computing.
[17] Eric Livingston,et al. Cultures of Proving , 1999 .
[18] Anandhi S. Bharadwaj,et al. A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation , 2000, MIS Q..
[19] Keith H. Bennett,et al. Software maintenance and evolution: a roadmap , 2000, ICSE '00.
[20] Ron Patton,et al. Software Testing , 2000 .
[21] J. Kennedy. The relationship between science and technology , 2001 .
[22] J. Overhage,et al. Sorting Things Out: Classification and Its Consequences , 2001, Annals of Internal Medicine.
[23] B. V. Koen,et al. Discussion of the Method : Conducting the Engineer's Approach to Problem Solving , 2003 .
[24] Adam Kilgarriff,et al. Introduction to the Special Issue on the Web as Corpus , 2003, CL.
[25] Steven L. Goldman,et al. Why we need a philosophy of engineering: a work in progress , 2004 .
[26] I. Stevenson,et al. Data on data , 2004 .
[27] Deborah K. Heikes,et al. The Bias Paradox: Why it's Not Just for Feminists Anymore , 2004, Synthese.
[28] Herb Sutter,et al. C++ Coding Standards: 101 Rules, Guidelines, and Best Practices (C++ in Depth Series) , 2004 .
[29] Torgeir Dingsøyr,et al. Postmortem reviews: purpose and approaches in software engineering , 2005, Inf. Softw. Technol..
[30] N. Mackenzie,et al. Research dilemmas: Paradigms, methods and methodology , 2006 .
[31] Gary King,et al. An Introduction to the Dataverse Network as an Infrastructure for Data Sharing , 2007 .
[32] Peter M. Bednar,et al. Contextual Inquiry and Requirements Shaping , 2007, ISD.
[33] Gary Klein,et al. Performing a Project Premortem , 2008, IEEE Engineering Management Review.
[34] David Loshin. Metadata Management for MDM , 2008 .
[35] David Loshin. Master Data Management , 2008 .
[36] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[37] Peter Norvig,et al. The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.
[38] David Loshin,et al. The Practitioner's Guide to Data Quality Improvement , 2010 .
[39] David Shoemaker,et al. Attributability, Answerability, and Accountability: Toward a Wider Theory of Moral Responsibility* , 2011, Ethics.
[40] Janet Abbate,et al. Recoding Gender: Women's Changing Participation in Computing , 2012 .
[41] I. Arel. Deep Reinforcement Learning as Foundation for Artificial General Intelligence , 2012 .
[42] Brett M. Frischmann. Infrastructure: The Social Value of Shared Resources , 2012 .
[43] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[44] Heather A. Piwowar,et al. Data reuse and the open data citation advantage , 2013, PeerJ.
[45] Michael Weisberg,et al. Biology and Philosophy symposium on Simulation and Similarity: Using Models to Understand the World , 2013 .
[46] Matthew Richardson,et al. MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text , 2013, EMNLP.
[47] B. Larkin. The Politics and Poetics of Infrastructure , 2013 .
[48] Victoria Stodden,et al. Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research , 2014 .
[49] Irfan A. Alvi. Engineers Need to Get Real, But Can't: The Role of Models , 2013 .
[50] M. Six Silberman,et al. Turkopticon: interrupting worker invisibility in amazon mechanical turk , 2013, CHI.
[51] F. Hanusch,et al. Journalism Students’ Professional Views in Eight Countries: The Role of Motivations, Education, and Gender , 2014 .
[52] Ernest Davis. The Limitations of Standardized Science Tests as Benchmarks for Artificial Intelligence Research: Position Paper , 2014, ArXiv.
[53] Elihu Katz. Kanonizing Katz| Commuting and Co-Authoring: How To Be in More Than One Place at the Same Time , 2014 .
[54] Frank A. Pasquale,et al. [89WashLRev0001] The Scored Society: Due Process for Automated Predictions , 2014 .
[55] Kate M. Miltner,et al. Big Data| Critiquing Big Data: Politics, Ethics, Epistemology | Special Section Introduction , 2014 .
[56] Brendan Hall,et al. Distributed System Design Checklist , 2014 .
[57] Lawrence Busch,et al. Big Data, Big Questions| A Dozen Ways to Get Lost in Translation: Inherent Challenges in Large Scale Data Sets , 2014 .
[58] D. Sculley,et al. Hidden Technical Debt in Machine Learning Systems , 2015, NIPS.
[59] Jonathan Krause,et al. Fine-grained recognition without part annotations , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] David W. Shoemaker. Responsibility from the Margins , 2015 .
[61] Luiz Fernando Capretz,et al. Influence of personality types in software tasks choices , 2015, Comput. Hum. Behav..
[62] Vahid Garousi,et al. Cost, benefits and quality of software development documentation: A systematic mapping , 2015, J. Syst. Softw..
[63] Tonatiuh Rodriguez-Nikl,et al. Philosophy of Engineering: What It Is and Why It Matters , 2015 .
[64] Bent Flyvbjerg,et al. The Principle of the Malevolent Hiding Hand; or, the Planning Fallacy Writ Large , 2015, 1509.01526.
[65] Yi Yang,et al. WikiQA: A Challenge Dataset for Open-Domain Question Answering , 2015, EMNLP.
[66] Rick O. Gilmore,et al. Curating identifiable data for sharing: The databrary project , 2016, 2016 New York Scientific Data Summit (NYSDS).
[67] Andrew D. Selbst,et al. Big Data's Disparate Impact , 2016 .
[68] E. Dougherty,et al. Big data need big theory too , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[69] Matthew Kelly,et al. Information Cultures in the Digital Age , 2016 .
[70] Anna Lauren Hoffmann,et al. Digitizing Books, Obscuring Women’s Work: Google Books, Librarians, and Ideologies of Access , 2016 .
[71] Mariarosaria Taddeo,et al. The ethics of algorithms: Mapping the debate , 2016, Big Data Soc..
[72] Michael I. Jordan,et al. CoCoA: A General Framework for Communication-Efficient Distributed Optimization , 2016, J. Mach. Learn. Res..
[73] L. Winner. DO ARTIFACTS HAVE (cid:1) POLITICS? , 2022 .
[74] Solon Barocas,et al. Ten simple rules for responsible big data research , 2017, PLoS Comput. Biol..
[75] Shrikanth S. Narayanan,et al. Designing Contestability: Interaction Design, Machine Learning, and Mental Health , 2017, Conference on Designing Interactive Systems.
[76] Jennifer Wortman Vaughan. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research , 2017, J. Mach. Learn. Res..
[77] Jieyu Zhao,et al. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints , 2017, EMNLP.
[78] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[79] Gina Neff,et al. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science , 2017, Big Data.
[80] D. Sculley,et al. No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World , 2017, 1711.08536.
[81] J. Jordana,et al. Accountability Challenges in the Governance of Infrastructure , 2017 .
[82] Helmut K. Anheier. Infrastructure and the Principle of the Hiding Hand , 2017 .
[83] Alun D. Preece,et al. Interpretability of deep learning models: A survey of results , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[84] D. Sculley,et al. The ML test score: A rubric for ML production readiness and technical debt reduction , 2017, 2017 IEEE International Conference on Big Data (Big Data).
[85] Xin Zhang,et al. TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.
[86] Christine L. Borgman,et al. On the Reuse of Scientific Data , 2017, Data Sci. J..
[87] Timnit Gebru,et al. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification , 2018, FAT.
[88] Neoklis Polyzotis,et al. Data Lifecycle Challenges in Production Machine Learning , 2018, SIGMOD Rec..
[89] Nitin Kohli,et al. Translation Tutorial : A Shared Lexicon for Research and Practice in Human-Centered Software Systems , 2018 .
[90] Matei Zaharia,et al. Provenance Analysis for Missing Answers and Integrity Repairs. , 2018 .
[91] D. Sculley,et al. Winner's Curse? On Pace, Progress, and Empirical Rigor , 2018, ICLR.
[92] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[93] Virginia E. Eubanks. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018 .
[94] Emily M. Bender,et al. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.
[95] Dan Roth,et al. Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences , 2018, NAACL.
[96] Juan Carlos De Martin,et al. Ethical and Socially-Aware Data Labels , 2018, SIMBig.
[97] Yejin Choi,et al. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference , 2018, EMNLP.
[98] Trevor Darrell,et al. Women also Snowboard: Overcoming Bias in Captioning Models , 2018, ECCV.
[99] Ali Ghodsi,et al. Accelerating the Machine Learning Lifecycle with MLflow , 2018, IEEE Data Eng. Bull..
[100] Daniel Jurafsky,et al. Word embeddings quantify 100 years of gender and ethnic stereotypes , 2017, Proceedings of the National Academy of Sciences.
[101] Nazli Ikizler-Cinbis,et al. RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes , 2018, EMNLP.
[102] Ahmed Hosny,et al. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards , 2018, Data Protection and Privacy.
[103] Miroslav Dudík,et al. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? , 2018, CHI.
[104] Nanyun Peng,et al. Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects , 2019, EMNLP/IJCNLP.
[105] Solon Barocas,et al. Problem Formulation and Fairness , 2019, FAT.
[106] E. Topol,et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. , 2019, The Lancet. Digital health.
[107] Ian Taylor,et al. Towards Traceability in Data Ecosystems using a Bill of Materials Model , 2019, ArXiv.
[108] Kush R. Varshney,et al. Increasing Trust in AI Services through Supplier's Declarations of Conformity , 2018, IBM J. Res. Dev..
[109] Andreas Vogelsang,et al. Requirements Engineering for Machine Learning: Perspectives from Data Scientists , 2019, 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW).
[110] Ingmar Weber,et al. Racial Bias in Hate Speech and Abusive Language Detection Datasets , 2019, Proceedings of the Third Workshop on Abusive Language Online.
[111] S. E. Sachs,et al. The algorithm at work? Explanation and repair in the enactment of similarity in art data , 2020, Information, Communication & Society.
[112] Inioluwa Deborah Raji,et al. Model Cards for Model Reporting , 2018, FAT.
[113] Laurens van der Maaten,et al. Does Object Recognition Work for Everyone? , 2019, CVPR Workshops.
[114] Eduard Hovy,et al. Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization , 2019, EMNLP.
[115] Radu Calinescu,et al. Assuring the Machine Learning Lifecycle , 2019, ACM Comput. Surv..
[116] K. Crawford,et al. Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice , 2019 .
[117] Inioluwa Deborah Raji,et al. ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles , 2019, ArXiv.
[118] Danah Boyd,et al. Fairness and Abstraction in Sociotechnical Systems , 2019, FAT.
[119] Deirdre K. Mulligan,et al. Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions , 2019 .
[120] Stuart N. Lane. Editorial 2020 Part II: Data from nowhere? , 2019 .
[121] Lora Aroyo,et al. Metrology for AI: From Benchmarks to Instruments , 2019, ArXiv.
[122] Cultures of Programming , 2019 .
[123] Harald C. Gall,et al. Software Engineering for Machine Learning: A Case Study , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).
[124] Sabina Leonelli,et al. Scientific research and big data , 2020 .
[125] Inioluwa Deborah Raji,et al. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing , 2020, FAT*.
[126] Jeanna Neefe Matthews,et al. Quantifying Gender Bias in Different Corpora , 2020, WWW.
[127] Hirotoshi Yasuoka,et al. Engineering problems in machine learning systems , 2019, Machine Learning.
[128] Stefanie N. Lindstaedt,et al. SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle , 2019, CIDR.
[129] Emily Denton,et al. Social Biases in NLP Models as Barriers for Persons with Disabilities , 2020, ACL.
[130] Timnit Gebru,et al. Lessons from archives: strategies for collecting sociocultural data in machine learning , 2019, FAT*.
[131] Maranke Wieringa,et al. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability , 2020, FAT*.
[132] Grace Hui Yang,et al. More Diverse Dialogue Datasets via Diversity-Informed Data Collection , 2020, ACL.
[133] Abolfazl Asudeh,et al. Fair Active Learning , 2020, Expert Syst. Appl..
[134] Hanmeng Liu,et al. LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning , 2020, IJCAI.
[135] Emily Denton,et al. Bringing the People Back In: Contesting Benchmark Machine Learning Datasets , 2020, ArXiv.
[136] Benjamin Heinzerling,et al. NLP's Clever Hans Moment has Arrived , 2020 .
[137] Michael Herrmann,et al. From Principles to Practice : An interdisciplinary framework to operationalise AI ethics , 2020 .
[138] Andrew Smart,et al. Why Reliabilism Is not Enough: Epistemic and Moral Justification in Machine Learning , 2020, AIES.
[139] R. Stuart Geiger,et al. Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from? , 2019, FAT*.
[140] Caitlin Lustig,et al. How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis , 2020, Proc. ACM Hum. Comput. Interact..
[141] Ben Green,et al. Data Science as Political Action: Grounding Data Science in a Politics of Justice , 2018, J. Soc. Comput..
[142] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.
[143] Vinay Uday Prabhu,et al. Large image datasets: A pyrrhic win for computer vision? , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[144] Joel Walmsley,et al. Artificial intelligence and the value of transparency , 2020, AI & SOCIETY.
[145] Steven Euijong Whang,et al. A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective , 2018, IEEE Transactions on Knowledge and Data Engineering.
[146] Trevor Paglen,et al. Correction to: Excavating AI: the politics of images in machine learning training sets , 2021, AI & SOCIETY.
[147] Praveen K. Paritosh,et al. “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI , 2021, CHI.
[148] Hanna M. Wallach,et al. Measurement and Fairness , 2019, FAccT.
[149] Wolfram Wöß,et al. A Survey of Data Quality Measurement and Monitoring Tools , 2019, Frontiers in Big Data.