Filling gaps in trustworthy development of AI

By S. Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David M. Krueger, Jonathan Lebensold and 2 more. 2021

63 citations recorded by the source corpus.

Description Incident sharing, auditing, and other concrete mechanisms could help verify the trustworthiness of actors The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address those risks while eroding confidence in AI systems and the organizations that develop them. A 2019 study (1) found more than 80 organizations that have published and adopted “AI ethics principles,” and more have joined since. But the principles often leave a gap between the “what” and the “how” of trustworthy AI development. Such gaps have enabled questionable or ethically dubious behavior, which casts doubts on the trustworthiness of specific organizations, and the field more broadly. There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness through verifiable behavior. Below, we explore mechanisms [drawn from (2)] for creating an ecosystem where AI developers can earn trust—if they are trustworthy (see the figure). Better assessment of developer trustworthiness could inform user choice, employee actions, investment decisions, legal recourse, and emerging governance regimes.

Subfields: Artificial Intelligence, Health Informatics, Safety Research, Safety, Robustness & Interpretability

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Citations in the corpus

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022, methodology): “One potentially useful tool for addressing harm is red teaming—using manual or automated methods to adversarially probe a language model for harmful outputs, and then updating the model to avoid such outputs [42, 20, 3, 11].”
Auditing large language models: a three-layered approach (2023, background): “Several governance mechanisms designed to ensure that LLMs are legal, ethical, and safe have been proposed or piloted [78].”