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Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer "Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?" Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning.

A polyphasic taxonomic approach, incorporating analysis of phenotypic features, cellular fatty acid profiles, 16S rRNA gene sequences, and determination of average nucleotide identity (ANI) plus digital DNA-DNA hybridization (dDDH), was applied to characterize an anaerobic bacterial strain designated KD22 T isolated from human feces. 16S rRNA gene-based phylogenetic analysis showed that strain KD22 T was found to be most closely related to species of the genus Gabonibacter. At the 16S rRNA gene level, the closest species from the strain KD22 T corresponded with Gabonibacter massiliensis GM7 T , with a similarity of 97.58%. Cells of strain KD22 T  were Gram-negative coccobacillus, positive for indole and negative for catalase, nitrate reduction, oxidase, and urease activities. The fatty acid analysis demonstrated the presence of a high concentration of iso-C 15: 0 (51.65%). Next, the complete whole-genome sequence of strain KD22 T  was 3,368,578 bp long with 42 mol% of DNA G + C contents. The DDH and ANI values between KD22 T  and type strains of phylogenetically related species were 67.40% and 95.43%, respectively. These phylogenetic, phenotypic, and genomic results supported the affiliation of strain KD22 T as a novel bacterial species within the genus Gabonibacter. The proposed name is Gabonibacter chumensis and the type strain is KD22 T (= CSUR Q8104 T  = DSM 115208  T )., (© 2023. The Author(s).)