Markus Anderljung

TL;DR: This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems.

TL;DR: In this Perspective, a governance initiative by one of the world’s largest AI conferences is reflected on and insights are gained regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

TL;DR: In 2019, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research as mentioned in this paper.

TL;DR: In this article, the authors argue that ML and AI researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment, and that they should not be overlooked.

TL;DR: In this paper , the authors explain why model evaluation is critical for addressing extreme risks and why developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations").