Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads
Anja Lambrecht and Catherine Tucker. 2019 · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Original
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Later among the works it cites.
Designing monitoring systems for continuous certification of cloud services: deriving meta-requirements and design guidelines
Sebastian Lins, Stephan Schneider, Jakub Szefer, Shafeeq Ibraheem, and Ali Sunyaev. 2019 · 2019
Later among the works it cites.
Say What I Want: Towards the Dark Side of Neural Dialogue Models
Original
Haochen Liu, Tyler Derr, Zitao Liu, and Jiliang Tang. 2019 · 2019
Later among the works it cites.
Ethical implications and accountability of algorithms
Kirsten Martin. 2019 · 2019
Later among the works it cites.
On measuring social biases in sentence encoders
Original
Chandler May, Alex Wang, Shikha Bordia, Samuel R Bowman, and Rachel Rudinger. 2019a · 2019
Later among the works it cites.
On measuring social biases in sentence encoders
Original
Chandler May, Alex Wang, Shikha Bordia, Samuel R Bowman, and Rachel Rudinger. 2019b · 2019
Later among the works it cites.
A survey on bias and fairness in machine learning
Original
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 2019
Later among the works it cites.
Are sixteen heads really better than one?
Original
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
Later among the works it cites.
Explaining explanations in AI. In Proceedings of the conference on fairness, accountability, and transparency . 279–288
Brent Mittelstadt, Chris Russell, and Sandra Wachter. 2019 · 2019
Later among the works it cites.
How police technology aggravates racial inequity: A taxonomy of problems and a path forward
Laura Moy. 2019 · 2019
Later among the works it cites.
InterpretML: A Unified Framework for Machine Learning Interpretability
Original
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019 · 2019
Later among the works it cites.
Social data: Biases, methodological pitfalls, and ethical boundaries
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. 2019 · 2019
Later among the works it cites.
Timeloop: A systematic approach to dnn accelerator evaluation. In 2019 IEEE international symposium on performance analysis of systems and software (ISPASS) . IEEE, 304–315
Angshuman Parashar, Priyanka Raina, Yakun Sophia Shao, Yu-Hsin Chen, Victor A Ying, Anurag Mukkara, Rangharajan Venkatesan, Brucek Khailany, Stephen W Keckler, and Joel Emer. 2019 · 2019
Later among the works it cites.
Assessing gender bias in machine translation: a case study with google translate
Marcelo OR Prates, Pedro H Avelar, and Luís C Lamb. 2019 · 2019
Later among the works it cites.
Interpretable deep learning in drug discovery
Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, and Thomas Unterthiner. 2019 · 2019
Later among the works it cites.
Toward a better trade-off between performance and fairness with kernel-based distribution matching
Original
Flavien Prost, Hai Qian, Qiuwen Chen, Ed H Chi, Jilin Chen, and Alex Beutel. 2019 · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
Later among the works it cites.
The risk of racial bias in hate speech detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 1668–1678
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A Smith. 2019 · 2019
Later among the works it cites.
How do fairness definitions fare? Examining public attitudes towards algorithmic definitions of fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 99–106
Nripsuta Ani Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David C Parkes, and Yang Liu. 2019 · 2019
Later among the works it cites.
Adversarial training for free!
Original
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 · 2019
Later among the works it cites.
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Original
Deven Shah, H Andrew Schwartz, and Dirk Hovy. 2019 · 2019
Later among the works it cites.
The Woman Worked as a Babysitter: On Biases in Language Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 3407–3412
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2019 · 2019
Later among the works it cites.
The eu approach to ethics guidelines for trustworthy artificial intelligence
Nathalie A Smuha. 2019 · 2019
Later among the works it cites.
Privacy risks of securing machine learning models against adversarial examples. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security . 241–257
Liwei Song, Reza Shokri, and Prateek Mittal. 2019 · 2019
Later among the works it cites.
Evaluating gender bias in machine translation
Original
Gabriel Stanovsky, Noah A Smith, and Luke Zettlemoyer. 2019 · 2019
Later among the works it cites.
Energy and policy considerations for deep learning in NLP
Original
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Later among the works it cites.
Patient knowledge distillation for bert model compression
Original
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019 · 2019
Later among the works it cites.
Distilling task-specific knowledge from bert into simple neural networks
Original
Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, and Jimmy Lin. 2019 · 2019
Later among the works it cites.
Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
Later among the works it cites.
Getting gender right in neural machine translation
Original
Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2019 · 2019
Later among the works it cites.
The role and limits of principles in AI ethics: towards a focus on tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 195–200
Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019 · 2019
Later among the works it cites.
Wasserstein adversarial examples via projected sinkhorn iterations. In International Conference on Machine Learning . PMLR, 6808–6817
Eric Wong, Frank Schmidt, and Zico Kolter. 2019 · 2019
Later among the works it cites.
Accelergy: An architecture-level energy estimation methodology for accelerator designs. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) . IEEE, 1–8
Yannan Nellie Wu, Joel S Emer, and Vivienne Sze. 2019 · 2019
Later among the works it cites.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
Later among the works it cites.
Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy. In International Conference on Machine Learning . PMLR, 7472–7482
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019 · 2019
Later among the works it cites.
Gender bias in contextualized word embeddings
Original
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 2019
Later among the works it cites.
Adversarial attacks on graph neural networks via meta learning
Original
Daniel Zügner and Stephan Günnemann. 2019 · 2019
Later among the works it cites.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
Later among the works it cites.
Ai explainability 360: An extensible toolkit for understanding data and machine learning models
Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilovic, et al · 2020
Later among the works it cites.
Towards mitigating gender bias in a decoder-based neural machine translation model by adding contextual information. In Proceedings of the The Fourth Widening Natural Language Processing Workshop . Association for Computational Linguistics, 99–102
Christine Raouf Saad Basta, Marta Ruiz Costa-Jussà, and José Adrián Rodríguez Fonollosa. 2020 · 2020
Later among the works it cites.
Principles and practice of explainable machine learning
Original
Vaishak Belle and Ioannis Papantonis. 2020 · 2020
Later among the works it cites.
Language (Technology) is Power: A Critical Survey of “Bias” in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 5454–5476
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Later among the works it cites.
Toward trustworthy AI development: mechanisms for supporting verifiable claims
Original
Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, et al · 2020
Later among the works it cites.
Fairness in Machine Learning: A Survey
Original
Simon Caton and Christian Haas. 2020 · 2020
Later among the works it cites.
Bias and Debias in Recommender System: A Survey and Future Directions
Original
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020b · 2020
Later among the works it cites.
A survey of accelerator architectures for deep neural networks
Yiran Chen, Yuan Xie, Linghao Song, Fan Chen, and Tianqi Tang. 2020c · 2020
Later among the works it cites.
Conversational assistants and gender stereotypes: Public perceptions and desiderata for voice personas. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing . 72–78
Amanda Cercas Curry, Judy Robertson, and Verena Rieser. 2020 · 2020
Later among the works it cites.
A survey of the state of explainable AI for natural language processing
Original
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020 · 2020
Later among the works it cites.
An overview of privacy in machine learning
Original
Emiliano De Cristofaro. 2020 · 2020
Later among the works it cites.
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 8173–8188
Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston. 2020 · 2020
Later among the works it cites.
A Graph Neural Network Framework for Social Recommendations
Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin. 2020 · 2020
Later among the works it cites.
The Future of Jobs Report 2020. World Economic Forum, Geneva, Switzerland
World Economic Forum. 2020 · 2020
Later among the works it cites.
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 3356–3369
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2020
Later among the works it cites.
FedML: A Research Library and Benchmark for Federated Machine Learning
Original
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. 2020 · 2020
Later among the works it cites.
Multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition
Original
Xiaolei Huang, Linzi Xing, Franck Dernoncourt, and Michael J Paul. 2020 · 2020
Later among the works it cites.
Auditing Differentially Private Machine Learning: How Private is Private SGD?. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 22205–22216
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020 · 2020
Later among the works it cites.
Identifying and correcting label bias in machine learning. In International Conference on Artificial Intelligence and Statistics . PMLR, 702–712
Heinrich Jiang and Ofir Nachum. 2020 · 2020
Later among the works it cites.
Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, and Gisbert Schneider. 2020 · 2020
Later among the works it cites.
Adversarial attacks and defenses on graphs: A review and empirical study
Original
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang. 2020a · 2020
Later among the works it cites.
Secure, privacy-preserving and federated machine learning in medical imaging
Georgios A Kaissis, Marcus R Makowski, Daniel Rückert, and Rickmer F Braren. 2020 · 2020
Later among the works it cites.
SCAFFOLD: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning . PMLR, 5132–5143
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020 · 2020
Later among the works it cites.
Tighter theory for local SGD on identical and heterogeneous data. In International Conference on Artificial Intelligence and Statistics . PMLR, 4519–4529
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik. 2020 · 2020
Later among the works it cites.
Keystone: An Open Framework for Architecting Trusted Execution Environments. In Proceedings of the Fifteenth European Conference on Computer Systems (EuroSys ’20)
Dayeol Lee, David Kohlbrenner, Shweta Shinde, Krste Asanovic, and Dawn Song. 2020 · 2020
Later among the works it cites.
Federated Optimization in Heterogeneous Networks. In Proceedings of Machine Learning and Systems 2020, MLSys 2020, Austin, TX, USA, March 2-4, 2020 , Inderjit S. Dhillon, Dimitris S. Papailiopoulos, and Vivienne Sze (Eds.). mlsys.org
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020c · 2020
Later among the works it cites.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 893–903
Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, and Jiliang Tang. 2020b · 2020
Later among the works it cites.
Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2020 · 2020
Later among the works it cites.
Parameterized explainer for graph neural network
Original
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
Later among the works it cites.
Interpretable machine learning
Christoph Molnar. 2020 · 2020
Later among the works it cites.
Bias in word embeddings. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 446–457
Orestis Papakyriakopoulos, Simon Hegelich, Juan Carlos Medina Serrano, and Fabienne Marco. 2020 · 2020
Later among the works it cites.
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 33–44
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020 · 2020
Later among the works it cites.
Overfitting in adversarially robust deep learning. In International Conference on Machine Learning . PMLR, 8093–8104
Leslie Rice, Eric Wong, and Zico Kolter. 2020 · 2020
Later among the works it cites.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
Later among the works it cites.
A survey of privacy attacks in machine learning
Original
Maria Rigaki and Sebastian Garcia. 2020 · 2020
Later among the works it cites.
Adversarial attacks on copyright detection systems. In International Conference on Machine Learning . PMLR, 8307–8315
Parsa Saadatpanah, Ali Shafahi, and Tom Goldstein. 2020 · 2020
Later among the works it cites.
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 5248–5264
Deven Santosh Shah, H Andrew Schwartz, and Dirk Hovy. 2020 · 2020
Later among the works it cites.
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J Sheller, Brandon Edwards, G Anthony Reina, Jason Martin, Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, Daniel Marcus, Rivka R Colen, et al · 2020
Later among the works it cites.
Trustworthy artificial intelligence
Scott Thiebes, Sebastian Lins, and Ali Sunyaev. 2020 · 2020
Later among the works it cites.
A survey on explainable artificial intelligence (xai): Toward medical xai
Erico Tjoa and Cuntai Guan. 2020 · 2020
Later among the works it cites.
Finprivacy: A privacy-preserving mechanism for fingerprint identification
Tao Wang, Zhigao Zheng, A Bashir, Alireza Jolfaei, and Yanyan Xu. 2020c · 2020
Later among the works it cites.
Benchmarking the performance and energy efficiency of ai accelerators for ai training. In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) . IEEE, 744–751
Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, and Xiaowen Chu. 2020b · 2020
Later among the works it cites.
Federated learning with differential privacy: Algorithms and performance analysis
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor. 2020 · 2020
Later among the works it cites.
What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 1–18
Maranke Wieringa. 2020 · 2020
Later among the works it cites.
Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang. 2020b · 2020
Later among the works it cites.
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Original
Han Xu, Xiaorui Liu, Yaxin Li, and Jiliang Tang. 2020a · 2020
Later among the works it cites.
Adversarial attacks and defenses in images, graphs and text: A review
Han Xu, Yao Ma, Hao-Chen Liu, Debayan Deb, Hui Liu, Ji-Liang Tang, and Anil K Jain. 2020b · 2020
Later among the works it cites.
Defining and Evaluating Fair Natural Language Generation. In Proceedings of the The Fourth Widening Natural Language Processing Workshop . 107–109
Catherine Yeo and Alyssa Chen. 2020 · 2020
Later among the works it cites.
Explainability in Graph Neural Networks: A Taxonomic Survey
Original
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020b · 2020
Later among the works it cites.
Systematic review of privacy-preserving distributed machine learning from federated databases in health care
Fadila Zerka, Samir Barakat, Sean Walsh, Marta Bogowicz, Ralph TH Leijenaar, Arthur Jochems, Benjamin Miraglio, David Townend, and Philippe Lambin. 2020 · 2020
Later among the works it cites.
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
Original
Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, and Tiejun Zhao. 2020a · 2020
Later among the works it cites.
Adversarial attacks on deep-learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. 2020c · 2020
Later among the works it cites.
idlg: Improved deep leakage from gradients
Original
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2020
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu and Song Han. 2020 · 2020
Later among the works it cites.
The Montreal Declaration of Responsible AI
2017 · 2021
Closest in time.
Governance Principles for the New Generation Artificial Intelligence–Developing Responsible Artificial Intelligence
2019 · 2021
Closest in time.
Federated AI Technology Enabler
2021 · 2021
Closest in time.
LEAF: A Benchmark for Federated Settings
2021 · 2021
Closest in time.
A list of Homomorphic Encryption libraries, software or resources
2021 · 2021
Closest in time.
A list of MPC software or resources
2021 · 2021
Closest in time.
OenDP: Open Source Tools for Differential Privacy
2021 · 2021
Closest in time.
Opacus: Train PyTorch models with Differential Privacy
2021 · 2021
Closest in time.
Paddle Federated Learning
2021 · 2021
Closest in time.
A Technical Analysis of Confidential Computing
2021 · 2021
Closest in time.
TensorFlow Federated
2021 · 2021
Closest in time.
TensorFlow Privacy
2021 · 2021
Closest in time.
Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021 · 2021
Closest in time.
Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 680–688
Enyan Dai and Suhang Wang. 2021 · 2021
Closest in time.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) . IEEE, 1583–1594
Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jianping Wang, Jiliang Tang, and Qing Li. 2021 · 2021
Closest in time.
Membership Inference Attacks on Machine Learning: A Survey
Original
Hongsheng Hu, Zoran Salcic, Gillian Dobbie, and Xuyun Zhang. 2021 · 2021
Closest in time.
Explainable ai: A review of machine learning interpretability methods
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021 · 2021
Closest in time.
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
Original
Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, and Jiliang Tang. 2021a · 2021
Closest in time.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Original
Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, et al · 2021
Closest in time.
Advances and Open Problems in Federated Learning
H Brendan McMahan et al · 2021
Closest in time.
An Empirical Study on the Relation Between Network Interpretability and Adversarial Robustness
Adam Noack, Isaac Ahern, Dejing Dou, and Boyang Li. 2021 · 2021
Closest in time.
Asilomar AI principles
Future of Life Institute. 2017 · 2021
Closest in time.
Federated learning for healthcare informatics
Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang. 2021 · 2021
Closest in time.
Graph Embedding for Recommendation against Attribute Inference Attacks
Original
Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, and Xiangliang Zhang. 2021 · 2021
Closest in time.