Increasing Trust in AI Services through Supplier's Declarations of Conformity
暂无分享,去创建一个
Kush R. Varshney | A. Mojsilovic | K. Ramamurthy | Alexandra Olteanu | S. Mehta | K. Varshney | M. Hind | R. Nair
[1] Paul Nemitz,et al. Constitutional democracy and technology in the age of artificial intelligence , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[2] Inioluwa Deborah Raji,et al. Model Cards for Model Reporting , 2018, FAT.
[3] Rachel K. E. Bellamy,et al. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias , 2018, ArXiv.
[4] Francesca Rossi,et al. Towards Composable Bias Rating of AI Services , 2018, AIES.
[5] Benjamin Edwards,et al. Adversarial Robustness Toolbox v0.2.2 , 2018, ArXiv.
[6] Martin Wistuba,et al. Adversarial Robustness Toolbox v1.0.0 , 2018, 1807.01069.
[7] Ahmed Hosny,et al. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards , 2018, Data Protection and Privacy.
[8] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.
[9] Emily M. Bender,et al. Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science , 2018 .
[10] Carlos Castillo,et al. Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries , 2019, Front. Big Data.
[11] Aditi Raghunathan,et al. Certified Defenses against Adversarial Examples , 2018, ICLR.
[12] Min Kyung Lee. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management , 2018, Big Data Soc..
[13] Kush R. Varshney,et al. The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection , 2017, WebSci.
[14] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[15] Andrew D. Selbst. Disparate Impact in Big Data Policing , 2017 .
[16] M. Schweitzer,et al. Who Is Trustworthy? Predicting Trustworthy Intentions and Behavior , 2017, Journal of personality and social psychology.
[17] Kush R. Varshney,et al. On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products , 2016, Big Data.
[18] Francesco Bonchi,et al. Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining , 2016, KDD.
[19] Hanna M. Wallach,et al. Transparency by Conformity: A Field Experiment Evaluating Openness in Local Governments , 2016 .
[20] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[21] Carlos Eduardo Scheidegger,et al. Certifying and Removing Disparate Impact , 2014, KDD.
[22] Cynthia Rudin,et al. Algorithms for interpretable machine learning , 2014, KDD.
[23] Lesley K. McAllister. Harnessing Private Regulation , 2014, Michigan Journal of Environmental & Administrative Law.
[24] A. Bifet,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[25] Daniel Port,et al. The Value of Certifying Software Release Readiness: An Exploratory Study of Certification for a Critical System at JPL , 2013, 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement.
[26] Kiri Wagstaff,et al. Machine Learning that Matters , 2012, ICML.
[27] J. Sims,et al. A Brief Review of the Belmont Report , 2010, Dimensions of critical care nursing : DCCN.
[28] Marko C. J. D. van Eekelen,et al. A software product certification model , 2010, Software Quality Journal.
[29] Sven Ove Hansson,et al. Principles of engineering safety: Risk and uncertainty reduction , 2008, Reliab. Eng. Syst. Saf..
[30] Xin Li,et al. Why do we trust new technology? A study of initial trust formation with organizational information systems , 2008, J. Strateg. Inf. Syst..
[31] Rob J Hyndman,et al. Another look at measures of forecast accuracy , 2006 .
[32] High-Level Expert Group on Artificial Intelligence – Draft Ethics Guidelines for Trustworthy AI , 2019 .
[33] Andrew D. Selbst,et al. Big Data's Disparate Impact , 2016 .
[34] Kush R. Varshney,et al. Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy , 2015 .
[35] Niklas Möller. The Concepts of Risk and Safety , 2012 .
[36] Skipper Seabold,et al. Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.
[37] George Bernard Shaw,et al. LONG-RANGE FORECASTING From Crystal Ball to Computer , 2010 .
[38] Gary McGraw,et al. An Approach for Certifying Security in Software Components , 1998 .
[39] Harlan D. Mills,et al. Certifying the reliability of software , 1986, IEEE Transactions on Software Engineering.
[40] A. Maslow. A Theory of Human Motivation , 1943 .