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Found 18 papers, 3 papers with code
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models
Bias research in NLP seeks to analyse models for social biases, thus helping NLP practitioners uncover, measure, and mitigate social harms.
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks.
Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated.
Examining Political Rhetoric with Epistemic Stance Detection
Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others.
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult.
Risks of AI Foundation Models in Education
If the authors of a recent Stanford report (Bommasani et al., 2021) on the opportunities and risks of "foundation models" are to be believed, these models represent a paradigm shift for AI and for the domains in which they will supposedly be used, including education.
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets
Auditing NLP systems for computational harms like surfacing stereotypes is an elusive goal.
A Survey of Race, Racism, and Anti-Racism in NLP
Despite inextricable ties between race and language, little work has considered race in NLP research and development.
Beyond "Fairness:" Structural (In)justice Lenses on AI for Education
Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn.
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP
At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications.
Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP
We survey 146 papers analyzing {``}bias{''} in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing {``}bias{''} is an inherently normative process.
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process.
Twitter Universal Dependency Parsing for African-American and Mainstream American English
Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools.
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses
Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions.
A Dataset and Classifier for Recognizing Social Media English
While language identification works well on standard texts, it performs much worse on social media language, in particular dialectal language{---}even for English.
Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
We highlight an important frontier in algorithmic fairness: disparity in the quality of natural language processing algorithms when applied to language from authors of different social groups.
Demographic Dialectal Variation in Social Media: A Case Study of African-American English
Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language.
Visualizing textual models with in-text and word-as-pixel highlighting
We explore two techniques which use color to make sense of statistical text models.
