Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure

The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.

Push and Pull: A Framework for Measuring Attentional Agency

We propose a framework for measuring attentional agency - the ability to allocate one's attention according to personal desires, goals, and intentions - on digital platforms. Platforms extend people's limited powers of attention by extrapolating their preferences to large collections of previously unconsidered informational objects. However, platforms typically also allow people to influence one another's attention. We introduce a formal framework for measuring how much a given platform empowers people to both pull information into their own attentional field and push information into the attentional fields of others. We also use these definitions to shed light on the implications of generative foundation models, which enable users to bypass the implicit "attentional bargain" that underlies embedded advertising and other methods for capturing economic value from informational goods. We conclude with a set of policy strategies that can be used to understand and reshape the distribution of attentional agency online.

Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus

Recent literature has underscored the importance of dataset documentation work for machine learning, and part of this work involves addressing "documentation debt" for datasets that have been used widely but documented sparsely. This paper aims to help address documentation debt for BookCorpus, a popular text dataset for training large language models. Notably, researchers have used BookCorpus to train OpenAI's GPT-N models and Google's BERT models, even though little to no documentation exists about the dataset's motivation, composition, collection process, etc. We offer a preliminary datasheet that provides key context and information about BookCorpus, highlighting several notable deficiencies. In particular, we find evidence that (1) BookCorpus likely violates copyright restrictions for many books, (2) BookCorpus contains thousands of duplicated books, and (3) BookCorpus exhibits significant skews in genre representation. We also find hints of other potential deficiencies that call for future research, including problematic content, potential skews in religious representation, and lopsided author contributions. While more work remains, this initial effort to provide a datasheet for BookCorpus adds to growing literature that urges more careful and systematic documentation for machine learning datasets.