Dylan Cashman

Advancements in accessibility technologies such as low-cost swell form printers or refreshable tactile displays promise to allow blind or low-vision (BLV) people to analyze data by transforming visual representations directly to tactile representations. However, it is possible that design guidelines derived from experiments on the visual perception system may not be suited for the tactile perception system. We investigate the potential mismatch between familiar visual encodings and tactile perception in an exploratory study into the strategies employed by BLV people to measure common graphical primitives converted to tactile representations. First, we replicate the Cleveland and McGill study on graphical perception using swell form printing with eleven BLV subjects. Then, we present results from a group interview in which we describe the strategies used by our subjects to read four common chart types. While our results suggest that familiar encodings based on visual perception studies can be useful in tactile graphics, our subjects also expressed a desire to use encodings designed explicitly for BLV people. Based on this study, we identify gaps between the perceptual expectations of common charts and the perceptual tools available in tactile perception. Then, we present a set of guidelines for the design of tactile graphics that accounts for these gaps. Supplemental material is available at https://osf.io/3nsfp/?view_only=7b7b8dcbae1d4c9a8bb4325053d13d9f.

In this interactive article, we present an interactive game that represents the types of tasks solved by machine learning algorithms. We use this game to motivate the definition of Probably Approximately Correct (PAC) learning, illustrating a proof of PAC learnability for Empirical Risk Minimization (ERM). Then, we show three types of vulnerabilities of ERM that often occur in applied machine learning - domain mismatch, dependencies in data, and an incorrect model class. We conclude by arguing for the need for visualization to identify these issues in a modeling dataset.

Findings Assoc. Comput. Linguistics: EMNLP 2024 15447-15459 (2024) Large language model (LLM) agents show promise in an increasing number of
domains. In many proposed applications, it is expected that the agent reasons
over accumulated experience presented in an input prompt. We propose the OEDD
(Operationalize Experience Despite Distraction) corpus, a
human-annotator-validated body of scenarios with pre-scripted agent histories
where the agent must make a decision based on disparate experiential
information in the presence of a distractor. We evaluate three state-of-the-art
LLMs (GPT-3.5 Turbo, GPT-4o, and Gemini 1.5 Pro) using a minimal
chain-of-thought prompting strategy and observe that when (1) the input context
contains over 1,615 tokens of historical interactions, (2) a crucially
decision-informing premise is the rightful conclusion over two disparate
environment premises, and (3) a trivial, but distracting red herring fact
follows, all LLMs perform worse than random choice at selecting the better of
two actions. Our code and test corpus are publicly available at:
https://github.com/sonnygeorge/OEDD .

Tactile graphics are often adapted from visual chart designs, yet many of these encodings do not translate effectively to non-visual exploration. Blind and low-vision (BLV) people employ a variety of physical strategies such as measuring lengths with fingers or scanning for texture differences to interpret tactile charts. These observations suggest an opportunity to move beyond direct visual translation and toward a tactile-first design approach. We outline a speculative tactile design framework that explores how data analysis tasks may align with tactile strategies and encoding choices. While this framework is not yet validated, it offers a lens for generating tactile-first chart designs and sets the stage for future empirical exploration. We present speculative mockups to illustrate how the Tactile Perceptual Grammar might guide the design of an accessible COVID-19 dashboard. This scenario illustrates how the grammar can guide encoding choices that better support comparison, trend detection, and proportion estimation in tactile formats. We conclude with design implications and a discussion of future validation through co-design and task-based evaluation.

Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally-reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypotheses. Visualization researchers have thus proposed many dimensionality reduction methods and interactive systems designed to uncover latent structures. At the same time, different scientific domains have formulated guidelines or common workflows for using dimensionality reduction techniques and visualizations for their respective fields. In this work, we present a critical analysis of the usage of dimensionality reduction in scientific domains outside of computer science. First, we conduct a bibliometric analysis of 21,249 academic publications that use dimensionality reduction to observe differences in the frequency of techniques across fields. Next, we conduct a survey of a 71-paper sample from four fields: biology, chemistry, physics, and business. Through this survey, we uncover common workflows, processes, and usage patterns, including the mixed use of confirmatory data analysis to validate a dataset and projection method and exploratory data analysis to then generate more hypotheses. We also find that misinterpretations and inappropriate usage is common, particularly in the visual interpretation of the resulting dimensionally reduced view. Lastly, we compare our observations with recent works in the visualization community in order to match work within our community to potential areas of impact outside our community. By comparing the usage found within scientific fields to the recent research output of the visualization community, we offer both validation of the progress of visualization research into dimensionality reduction and a call for action to produce techniques that meet the needs of scientific users.

New consumer devices with refreshable tactile displays promise to allow visually impaired people to analyze data through tactile representations of graphical visualizations. To understand whether results based on visual perception translate to tactile perception, we present a study replicating the formative study by Cleveland and McGill (1984) on graphical perception to tactile representations suitable for visually impaired users. To assess how tactile graphics can convey complex graphical information, we investigate the effectiveness of tactile data visualizations compared to reported results on visual graphical primitives, examining the accuracy and inference times of visually impaired versus sighted users. We find that visually impaired users interpret simpler tactile formats such as bar charts with significantly greater accuracy and speed than more complex formats like bubble charts.

This interactive article presented a game that helps the reader understand how machine learning models can fail in applied machine learning use cases. It uses visualization to illustrate a thought experiment presented in a famous learning theory paper from the 1980s. I presented this article at the 6th workshop on Visualization for AI Explainability at the 2023 IEEE VIS conference. It received a Best Submission award.