Medical datasets play a crucial role in advancing healthcare research and supporting clinical decision-making. At the same time, the reliability of responsible and accountable AI systems is directly dependent on the integrity and transparency of the datasets on which they are built. The data subcard implements the System Cards framework's data assessment dimension to evaluate tabular medical datasets across four criteria: privacy, fairness, quality, and protection. It combines data-level profiling with optional model-based diagnostics, selected to fit each dataset, to assess completeness, duplication, outliers, demographic dispar-ities, re-identification risk, and compliance readiness. Applied to the UCI Heart Disease and Diabetes Readmission datasets, the method flags privacy risks, fairness imbalances, quality defects, and protection gaps that warrant review before modeling. The data subcard produces quantitative scores and visual summaries, providing a structured and interpretable mechanism for dataset accountability within the System Cards framework.

Electronic health record (EHR) datasets come in various schemas and can contain a range of data types, measurement units, and variables that share duplicate semantic content. The process of bringing such datasets into a common schema with consistent values, so that it is possible to perform queries uniformly, is known as harmonization. However, performing this process manually can be both time-consuming and prone to errors. In this work, we present a web-based platform that semi-automates the harmonization and linking of EHR datasets through a human-in-the-loop framework, guiding users with the use of large language models (LLMs). Our solution is a two-stage harmonization pipeline that keeps schema metadata processing online while handling patient-level data locally, to align with HIPAA data privacy principles. In the first stage, users harmonize and link only non-identifiable schema information. In the second stage, sensitive value-level harmonization occurs entirely on the user's system, so no private and protected health information ever leaves their environment. Throughout both stages, we expect that LLM-powered suggestions could potentially speed up the harmonization and linking processes.

Algorithms play an essential and expanding role in public policy decisions, including those in criminal justice. This short paper reports on the first author’s summer research project characterizing the tradeoff between accuracy and fairness in parole decision predictions. The dataset employed in this study contains over 30,000 parole decisions made by the New York State Division of Criminal Justice Services. Each decision contains information on the subject, such as sex, race/ethnicity, and parole decision, as well as predictive features describing the crime committed by the subject and the parole interview held. Logistic regression, decision tree, support vector machine, and random forest models are trained and utilized to analyze parole decision predictions based on the available features. Most models fail to pass standard fairness tests for most fairness metrics. Moreover, while there may be an overall tradeoff between fairness and accuracy, the obtained differences in accuracy are too small to make a well-supported claim. Future research may enhance the preliminary work introduced in this paper by using multiple real-world datasets to investigate the tradeoff between accuracy and fairness.

Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions. These models take in large amounts of data about an individual and then predict whether an individual would commit a crime if released on parole. Although such models are not the only or primary factor in making the final parole decision, questions have been raised about their accuracy, fairness, and interpretability. In this paper, various machine learning-based criminal recidivism models are created based on a real-world parole decision dataset from the state of Georgia in the United States. The recidivism models are comparatively evaluated for their accuracy, fairness, and interpretability. It is found that there are noted differences and trade-offs between accuracy, fairness, and being inherently interpretable. Therefore, choosing the best model depends on the desired balance between accuracy, fairness, and interpretability, as no model is perfect or consistently the best across different criteria.