The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection.

Mocha addresses two seemingly contradictory objectives: (1) generating labeled data that diversifies the training dataset to aid the model's learning, and (2) maintaining structural consistency across the batches of data presented to users to support their cognitive processes.

The results of our study indicate that participants spent significantly less time annotating batches of counterfactuals when they were rendered according to SAT compared to other conditions i.e., supporting the participants' selective focus on the varying phrases, rather than phrases that stay consistent.

Mocha exemplified the application of human cognition and concept learning theories in the interactive machine learning pipeline to support the negotiation of conceptual boundaries for bi-directional human-AI alignment.

This pattern of selective attention suggests that the visual cues provided by Mocha effectively guided participants to focus on more relevant information within the context of unchanged text when making their labeling decisions.

Overall, the incorporation of counterfactuals has generally improved the models' F1 scores, driven largely by the improvements in precision. This suggests that counterfactuals have effectively improved performance without necessitating a significant trade-off between precision and recall.

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives. This improvement suggests that the counterfactuals provided essential information that helped refine the models' decision boundaries.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26]. This can not only boosts the model's performance but also enable participants to validate or correct the model during the interactive training process.

Thus, the integration of both theories enables users to efficiently process and compare variations, leading to more informed decisions and a clearer understanding of the model's behavior.

By helping users see alignable differences, SAT-based rendering helps users focus on key variations that are essential to changing the data item's label, making it easier to interpret the effects of changes and their significance.

We argue that these two theories form a symbiotic relationship (Fig. 6). Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

Participants were able to efficiently focus on key differences between the original and counterfactual examples, which facilitated more efficient annotations.

The results from our user study suggest that both the participants and the model benefited from the Variation Theory (VT)-based counterfactuals and Structural Alignment Theory (SAT)-based rendering.

Thus, the integration of both the-ories enables users to efficiently process and compare variations,leading to more informed decisions and a clearer understanding ofthe model’s behavior.

pre-computing and reifying cross-document analogous relationships make it psychologically possible for users to engage—if they are willing to be guided by it. (Lower NFC users are more likely to fall into this category.)

Participants with lower NFC more frequently preferred and experienced less cognitive load when skimming with all the features enabled.

These results suggest that these three features lose their effectiveness when not used together.

Lower NFC participants were generally guided by emergent visual patterns created by the interactions between features, especially blocks of color spanning multiple sentences created when all three features are turned on.

Dialectical activities cannot be done on a user's behalf by AI; with variation affordances, AI is supporting the user's engagement with the data themselves.

In this sense, AbstractExplorer enables dialectical activities that users may otherwise have found to be too tedious or difficult to engage with.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

We demonstrate how slicing sentences according to roles and visually aligning them can help readers perceive cross-document relationships in a coherent manner.

In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.