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Found 20 papers, 5 papers with code
"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies
Indigenous languages are historically under-served by Natural Language Processing (NLP) technologies, but this is changing for some languages with the recent scaling of large multilingual models and an increased focus by the NLP community on endangered languages.
Cultural Incongruencies in Artificial Intelligence
Artificial intelligence (AI) systems attempt to imitate human behavior.
Underspecification in Scene Description-to-Depiction Tasks
Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have received little attention to date.
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.
Ranked #1 on
Text-to-Image Generation
on LAION COCO
Evaluation Gaps in Machine Learning Practice
Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Coreference Resolution
on Winograd Schema Challenge
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #113 on
Code Generation
on HumanEval
Thinking Beyond Distributions in Testing Machine Learned Models
Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn from the same distribution as the training dataset.
Re-imagining Algorithmic Fairness in India and Beyond
Instead, we re-imagine algorithmic fairness in India and provide a roadmap to re-contextualise data and models, empower oppressed communities, and enable Fair-ML ecosystems.
Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker Incentives
The same group of workers then votes on a bonus payment structure, to elicit preferences.
Non-portability of Algorithmic Fairness in India
Conventional algorithmic fairness is Western in its sub-groups, values, and optimizations.
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
In this paper, we introduce a rigorous framework for dataset development transparency which supports decision-making and accountability.
Social Biases in NLP Models as Barriers for Persons with Disabilities
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models.
Diversity and Inclusion Metrics in Subset Selection
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives.
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms.
Computers and Society
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language.
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias
Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking.
50 Years of Test (Un)fairness: Lessons for Machine Learning
We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged.
Model Cards for Model Reporting
Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.
