conftrace_
← Core AI
Artificial Intelligence › Core AI ›

Responsible AI

2,003 papers

Papers per year

1
1
7
10
22
51
91
145
207
526
760
182

Papers

Reviewing Natural Language Processing Research ACL 2020
Model-Agnostic Counterfactual Explanations for Consequential Decisions AISTATS 2020
Obfuscation via Information Density Estimation AISTATS 2020
Deontological Ethics By Monotonicity Shape Constraints AISTATS 2020
Fairness Evaluation in Presence of Biased Noisy Labels AISTATS 2020
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays COLING 2020
Automatic Detection of Machine Generated Text: A Critical Survey COLING 2020
Decolonising Speech and Language Technology COLING 2020
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations COLING 2020
Detection and Resolution of Rumors and Misinformation with NLP COLING 2020
Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research COLING 2020
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models EMNLP 2020
Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News EMNLP 2020
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias EMNLP 2020
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction EMNLP 2020
Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling EMNLP 2020
What Can We Do to Improve Peer Review in NLP? EMNLP 2020
Automatically Identifying Gender Issues in Machine Translation using Perturbations EMNLP 2020
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models EMNLP 2020
UNQOVERing Stereotyping Biases via Underspecified Questions EMNLP 2020
Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020 EMNLP 2020
Moderating Our (Dis)Content: Renewing the Regulatory Approach EMNLP 2020
Defining Explanation in an AI Context EMNLP 2020
How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study EMNLP 2020
Viable Threat on News Reading: Generating Biased News Using Natural Language Models EMNLP 2020