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Found 10 papers, 2 papers with code
The Surveillance AI Pipeline
Moreover, the majority of these technologies specifically enable extracting data about human bodies and body parts.
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.
Evaluating the Social Impact of Generative AI Systems in Systems and Society
We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that has no predetermined application and what is able to be evaluated in society.
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.
Robots Enact Malignant Stereotypes
Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14].
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management
Trustworthy artificial intelligence (AI) has become an important topic because trust in AI systems and their creators has been lost.
The Values Encoded in Machine Learning Research
We present extensive textual evidence and identify key themes in the definitions and operationalization of these values.
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard visual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments.
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning
Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency.
