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1. A Nutritional Label for Rankings

   https://doi.org/10.1145/3183713.3193568  

   Yang, Ke; Stoyanovich, Julia; Asudeh, Abolfazl; Howe, Bill; Jagadish, HV; Miklau, Gerome (July 2018, Proceedings of the 2018 International Conference on Management of Data)

   Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dating partners. While automatic and seemingly objective, ranking algorithms can discriminate against individuals and protected groups, and exhibit low diversity. Furthermore, ranked results are often unstable -- small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate. Similar concerns apply in cases where items other than individuals are ranked, including colleges, academic departments, or products. Despite the ubiquity of rankers, there is, to the best of our knowledge, no technical work that focuses on making rankers transparent. In this demonstration we present Ranking Facts, a Web-based application that generates a "nutritional label" for rankings. Ranking Facts is made up of a collection of visual widgets that implement our latest research results on fairness, stability, and transparency for rankings, and that communicate details of the ranking methodology, or of the output, to the end user. We will showcase Ranking Facts on real datasets from different domains, including college rankings, criminal risk assessment, and financial services. 

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2. A Nutritional Label for Rankings

   Stoyanovich, J; Yang, K; Asudeh, A; Howe, B; Jagadish, HV; Miklau, G (January 2018, Proceedings of the ACM SIGMOD Conference)
3. Nutritional geometry of paternal effects on embryo mortality

   https://doi.org/10.1098/rspb.2017.1492  

   Polak, Michal; Simmons, Leigh W.; Benoit, Joshua B.; Ruohonen, Kari; Simpson, Stephen J.; Solon-Biet, Samantha M. (October 2017, Proceedings of the Royal Society B: Biological Sciences)
4. Nutritional Labels for Data and Models

   Stoyanovich, Julia; Howe, Bill (September 2019, A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering)

   An essential ingredient of successful machine-assisted decision-making, particularly in high-stakes decisions, is interpretability –– allowing humans to understand, trust and, if necessary, contest, the computational process and its outcomes. These decision-making processes are typically complex: carried out in multiple steps, employing models with many hidden assumptions, and relying on datasets that are often used outside of the original context for which they were intended. In response, humans need to be able to determine the “fitness for use” of a given model or dataset, and to assess the methodology that was used to produce it. To address this need, we propose to develop interpretability and transparency tools based on the concept of a nutritional label, drawing an analogy to the food industry, where simple, standard labels convey information about the ingredients and production processes. Nutritional labels are derived automatically or semi-automatically as part of the complex process that gave rise to the data or model they describe, embodying the paradigm of interpretability-by-design. In this paper we further motivate nutritional labels, describe our instantiation of this paradigm for algorithmic rankers, and give a vision for developing nutritional labels that are appropriate for different contexts and stakeholders. 

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5. Nutritional inequalities structure worker division of labor in social insects

   https://doi.org/10.1016/j.cois.2023.101059  

   Walton, Alexander; Toth, Amy L (August 2023, Current Opinion in Insect Science)