While different countries vary in their determination of copyrightability, jurisdictions like the United States currently do not allow an artist to copyright AI-generated content when they do not have creative control.One avenue for an author to support their case for copyright protections over work created with AI may then be to demonstrate their intent to "predict" outputs of the generative AI tool during the creation process, shifting elements of randomness from the AI to the human's own decision-making as much as possible.When this happens, the artist might claim to have expressed their idea with generative AI, and seek copyright protection for their work.We propose that generative AI co-creation tools can support this intention by keeping records of the predictability statistics at each generative AI iteration, and capturing the potential alternate options that can be later assessed for how predictably they matched the prompt.

Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving techniques in the development of AI systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support surveillance infrastructure under the guise of regulatory permissibility. Finally, we propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer. We conclude by highlighting the role that technologists could play in devising policies that combat surveillance AI technologies.

As AI industry matures, it is important to ensure that the organizations developing these systems have sufficient incentives to identify and mitigate risks and harm. Unfortunately, the profit motive is often misaligned with this goal. Successful work to identify or reduce risk rarely has direct tangible benefits. In this paper, we consider the use of regulatory penalty defaults as a way to counter these perverse incentives. A regulatory penalty default regime consists of two parts: a regulatory penalty default and a mechanism to bargain around the default. The regulatory penalty default induces private actors to research and mitigate potential harms in order to limit liability, making the benefits of risk mitigation tangible. The bargaining mechanism provides incentives for companies to go beyond achieving a prescriptive threshold of compliance in creating a compelling case for escape from the default. With a focus on the policy landscape in the United States, we propose and discuss potential regulatory penalty default regimes for AI systems. For each of our proposals, we also discuss accompanying regulatory pathways for the bargaining process. While regulatory penalty default regimes are not a panacea (we discuss several drawbacks of the proposed methods), they are an important tool to consider in the regulation of AI systems.

An agent, or a coalition of agents, faces an ethical dilemma between several statements if she is forced to make a conscious choice between which of these statements will be true. This paper proposes to capture ethical dilemmas as a modality in strategic game settings with and without limit on sacrifice and for perfect and imperfect information games. The authors show that the dilemma modality cannot be defined through the earlier proposed blameworthiness modality. The main technical result is a sound and complete axiomatization of the properties of this modality with sacrifice in games with perfect information.