29 research outputs found
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as the
systematic exclusion of data from a certain subpopulation. Previous work in
this area focused on the derivation of identifiability conditions. We propose
instead a first algorithm to address both identifiable and unidentifiable
queries. We prove that, in spite of the missingness induced by the selection
bias, the likelihood of the available data is unimodal. This enables us to use
the causal expectation-maximisation scheme to obtain the values of causal
queries in the identifiable case, and to compute bounds otherwise. Experiments
demonstrate the approach to be practically viable. Theoretical convergence
characterisations are provided.Comment: Eleventh International Conference on Probabilistic Graphical Models
(PGM 2022
AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge
AI for Science (AI4Science), particularly in the form of self-driving labs,
has the potential to sideline human involvement and hinder scientific discovery
within the broader community. While prior research has focused on ensuring the
responsible deployment of AI applications, enhancing security, and ensuring
interpretability, we also propose that promoting openness in AI4Science
discoveries should be carefully considered. In this paper, we introduce the
concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science
with the core principle of maximizing open knowledge translation throughout the
scientific enterprise rather than a single organizational unit. We use the
established principles of Knowledge Discovery and Data Mining (KDD) to
formalize a language around AI4OS. We then discuss three principle stages of
knowledge translation embedded in AI4Science systems and detail specific points
where openness can be applied to yield an AI4OS alternative. Lastly, we
formulate a theoretical metric to assess AI4OS with a supporting ethical
argument highlighting its importance. Our goal is that by drawing attention to
AI4OS we can ensure the natural consequence of AI4Science (e.g., self-driving
labs) is a benefit not only for its developers but for society as a whole.Comment: NeurIPS AI For Science Workshop 2023. 11 pages, 2 figure
Selection Induced Collider Bias: A Gender Pronoun Uncertainty Case Study
In this paper, we cast the problem of task underspecification in causal
terms, and develop a method for empirical measurement of spurious associations
between gender and gender-neutral entities for unmodified large language
models, detecting previously unreported spurious correlations. We then describe
a lightweight method to exploit the resulting spurious associations for
prediction task uncertainty classification, achieving over 90% accuracy on a
Winogender Schemas challenge set. Finally, we generalize our approach to
address a wider range of prediction tasks and provide open-source demos for
each method described here.Comment: 16 pages, 21 figures. arXiv admin note: text overlap with
arXiv:2208.1006