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Found 19 papers, 6 papers with code
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Evaluating bias, fairness, and social impact in monolingual language models is a difficult task.
An Effective Theory of Bias Amplification
Machine learning models may capture and amplify biases present in data, leading to disparate test performance across social groups.
Strong Model Collapse
Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised regression setting and establish the existance of a strong form of the model collapse phenomenon, a critical performance degradation due to synthetic data in the training corpus.
Understanding "Democratization" in NLP and ML Research
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the "democratization" of artificial intelligence.
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society.
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Throughout our analysis, we connect our findings to previously-proposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others.
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
In this survey, we review prior studies on dimensions of bias: Gender, Skintone, and Geo-Culture.
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
We further bridge GCN's preferential attachment bias with unfairness in link prediction and propose a new within-group fairness metric.
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities.
Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test
The expressive power of graph neural networks is usually measured by comparing how many pairs of graphs or nodes an architecture can possibly distinguish as non-isomorphic to those distinguishable by the $k$-dimensional Weisfeiler-Leman ($k$-WL) test.
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated.
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.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research.
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models.
Motif-Driven Contrastive Learning of Graph Representations
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
