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Responsible AI

1991 directly classified papers

Papers per year

Papers

How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study EMNLP 2020
AViD Dataset: Anonymized Videos from Diverse Countries NIPS 2020
Analyzing Gender Bias within Narrative Tropes EMNLP 2020
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models EMNLP 2020
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction EMNLP 2020
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias EMNLP 2020
Viable Threat on News Reading: Generating Biased News Using Natural Language Models EMNLP 2020
Scalable Differential Privacy with Certified Robustness in Adversarial Learning ICML 2020
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models IJCAI 2020
Decolonising Speech and Language Technology COLING 2020
Language (Technology) is Power: A Critical Survey of “Bias” in NLP ACL 2020
Certifai: A Toolkit for Building Trust in AI Systems IJCAI 2020
A Survey of the State of Explainable AI for Natural Language Processing AACL 2020
“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases ACL 2020
Integrating Ethics into the NLP Curriculum ACL 2020
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space NIPS 2020
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? ACL 2020
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models EMNLP 2020
Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News EMNLP 2020
Identifying Statistical Bias in Dataset Replication ICML 2020
Robust and Stable Black Box Explanations ICML 2020
Reviewing Natural Language Processing Research ACL 2020
Give Me Convenience and Give Her Death: Who Should Decide What Uses of NLP are Appropriate, and on What Basis? ACL 2020
Automatically Identifying Gender Issues in Machine Translation using Perturbations EMNLP 2020
What Can We Do to Improve Peer Review in NLP? EMNLP 2020