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Found 8 papers, 5 papers with code
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap.
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Context-Aware Legal Citation Recommendation using Deep Learning
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions.
Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections.
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
While a Transformer architecture (BERT) pretrained on a general corpus (Google Books and Wikipedia) improves performance, domain pretraining (using corpus of approximately 3. 5M decisions across all courts in the U. S. that is larger than BERT's) with a custom legal vocabulary exhibits the most substantial performance gains with CaseHOLD (gain of 7. 2% on F1, representing a 12% improvement on BERT) and consistent performance gains across two other legal tasks.
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Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed.
Affirmative Algorithms: The Legal Grounds for Fairness as Awareness
While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques.
