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Domain Specific Metaheuristic Optimization

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For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.

Megan Hofmann, Nayha Auradkar, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Gene S.-H. Kim, Shriya Kurpad, Kathryn J. Lum, Kelly Mack, Anisha Nilakantan, Margaret Ellen Seehorn, Emily Warnock, Jennifer Mankoff, Scott E. Hudson: OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization. CHI 2023: 709:1-709:19

https://youtube.com/watch?v=wjQrFeLbOiw%3Fsi%3DkMTxEkEBjoUrQDJ3

COVID-19 Risk Negotation

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During the COVID-19 pandemic, risk negotiation became an important precursor to in-person contact. For young adults, social planning generally occurs through computer-mediated communication. Given the importance of social connectedness for mental health and academic engagement, we sought to understand how young adults plan in-person meetups over computer-mediated communication in the context of the pandemic. We present a qualitative study that explores young adults’ risk negotiation during the COVID-19 pandemic, a period of conflicting public health guidance. Inspired by cultural probe studies, we invited participants to express their preferred precautions for one week as they planned in-person meetups. We interviewed and surveyed participants about their experiences. Through qualitative analysis, we identify strategies for risk negotiation, social complexities that impede risk negotiation, and emotional consequences of risk negotiation. Our findings have implications for AI-mediated support for risk negotiation and assertive communication more generally. We explore tensions between risks and potential benefits of such systems.

Margaret E. Morris, Jennifer Brown, Paula S. Nurius, Savanna Yee, Jennifer Mankoff, Sunny Consolvo:
“I Just Wanted to Triple Check… They were all Vaccinated”: Supporting Risk Negotiation in the Context of COVID-19.ACM Trans. Comput. Hum. Interact. 30(4): 60:1-60:31 (2023)

Generative Artificial Intelligence’s Utility for Accessibility

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With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular.

To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.

Glazko, K. S., Yamagami, M., Desai, A., Mack, K. A., Potluri, V., Xu, X., & Mankoff, J. An Autoethnographic Case Study of Generative Artificial Intelligence’s Utility for Accessibility. ASSETS 2023. https://dl.acm.org/doi/abs/10.1145/3597638.3614548

News: Can AI help boost accessibility? These researchers tested it for themselves

Presentation (starts at about 20mins)

https://youtube.com/watch?v=S40-jPBH820%3Fsi%3DCm17oTaMaDnoQGvK%3F%23t%3D20m26s

How Do People with Limited Movement Personalize Upper-Body Gestures?

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Personalized upper-body gestures that can enable input from diverse body parts (e.g., head, neck, shoulders, arms, hands, and fingers), and match the abilities of each user, might make gesture systems more accessible for people with upper-body motor disabilities. Static gesture sets that make ability assumptions about the user (e.g., touch thumb and index finger together in midair) may not be accessible. In our work, we characterize the personalized gesture sets designed by 25 participants with upper-body motor disabilities. We found that the personalized gesture sets that participants designed were specific to their abilities and needs. Six participants mentioned that their inspiration for designing the gestures was based on “how I would do [the gesture] with the abilities that I have”. We suggest three considerations when designing accessible upper-body gesture interfaces: 

1) Track the whole upper body. Our participants used their whole upper-body to perform the gestures, and some switched back and forth from the left to the right hand to combat fatigue.

2) Use sensing mechanisms that are agnostic to the location and orientation of the body. About half of our participants kept their hand on or barely took their hand off of the armrest to decrease arm movement and fatigue.

3) Use sensors that can sense muscle activations without movement. Our participants activated their muscles but did not visibly move in 10% of the personalized gestures.   

Our work highlights the need for personalized upper-body gesture interfaces supported by multimodal biosignal sensors (e.g., accelerometers, sensors that can sense muscle activity like EMG). 

Race, Disability and Accessibility Technology

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Working at the Intersection of Race, Disability, and Accessibility

Examinations of intersectionality and identity dimensions in accessibility research have primarily considered disability separately from a person’s race and ethnicity. Accessibility work often does not include considerations of race as a construct, or treats race as a shallow demographic variable, if race is mentioned at all. The lack of attention to race as a construct in accessibility research presents an oversight in our field, often systematically eliminating whole areas of need and vital perspectives from the work we do. Further, there has been little focus on the intersection of race and disability within accessibility research, and the relevance of their interplay. When research in race or disability does not mention the other, this work overlooks the potential to better understand the full nuance of marginalized and “otherized” groups. To address this gap, we present a series of case studies exploring the potential for research that lies at the intersection of race and disability. We provide examples of how to integrate racial equity perspectives into accessibility research, through positive examples found in these case studies and reflect on teaching at the intersection of race, disability, and technology. This paper highlights the value of considering how constructs of race and disability work alongside each other within accessibility research studies, designs of socio-technical systems, and education. Our analysis provides recommendations towards establishing this research direction.

Christina N. Harrington, Aashaka Desai, Aaleyah Lewis, Sanika Moharana, Anne Spencer Ross, Jennifer Mankoff: Working at the Intersection of Race, Disability and Accessibility. ASSETS 2023: 26:1-26:18 (pdf)

https://youtube.com/watch?v=qRMYjdSTnZs%3Fsi%3D0yhLkUyGKu-WO4Na

Azimuth: Designing Accessible Dashboards for Screen Reader Users

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Dashboards are frequently used to monitor and share data across a breadth of domains including business, finance, sports, public policy, and healthcare, just to name a few. The combination of different components (e.g., key performance indicators, charts, filtering widgets) and the interactivity between components makes dashboards powerful interfaces for data monitoring and analysis. However, these very characteristics also often make dashboards inaccessible to blind and low vision (BLV) users. Through a co-design study with two screen reader users, we investigate challenges faced by BLV users and identify design goals to support effective screen reader-based interactions with dashboards. Operationalizing the findings from the co-design process, we present a prototype system, Azimuth, that generates dashboards optimized for screen reader-based navigation along with complementary descriptions to support dashboard comprehension and interaction. Based on a follow-up study with five BLV participants, we showcase how our generated dashboards support BLV users and enable them to perform both targeted and open-ended analysis. Reflecting on our design process and study feedback, we discuss opportunities for future work on supporting interactive data analysis, understanding dashboard accessibility at scale, and investigating alternative devices and modalities for designing accessible visualization dashboards.

Arjun Srinivasan, Tim Harshbarger, Darrell Hilliker and Jennifer Mankoff: University of Washington (2023): “Azimuth: Designing Accessible Dashboards for Screen Reader Users” ASSETS 2023.

Cross-Dataset Generalization for Human Behavior Modeling

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Overview; Data; Code

Overview of The Contributions of This Work. We systematically evaluate cross-dataset generalizability of 19 algorithms: 9 prior behavior modeling algorithm for depression detection, 8 recent domain generalization algorithms, and 2 two new algorithms proposed in this paper. Our open-source platform GLOBEM consolidates these 19 algorithms and support using, developing, evaluating various algorithms.

There is a growing body of research revealing that longitudinal passive sensing data from smartphones and wearable devices can capture daily behavior signals for human behavior modeling, such as depression detection. Most prior studies build and evaluate machine learning models using data collected from a single population. However, to ensure that a behavior model can work for a larger group of users, its generalizability needs to be verified on multiple datasets from different populations. We present the first work evaluating cross-dataset generalizability of longitudinal behavior models, using depression detection as an application. We collect multiple longitudinal passive mobile sensing datasets with over 500 users from two institutes over a two-year span, leading to four institute-year datasets. Using the datasets, we closely re-implement and evaluated nine prior depression detection algorithms. Our experiment reveals the lack of model generalizability of these methods. We also implement eight recently popular domain generalization algorithms from the machine learning community. Our results indicate that these methods also do not generalize well on our datasets, with barely any advantage over the naive baseline of guessing the majority. We then present two new algorithms with better generalizability. Our new algorithm, Reorder, significantly and consistently outperforms existing methods on most cross-dataset generalization setups. However, the overall advantage is incremental and still has great room for improvement. Our analysis reveals that the individual differences (both within and between populations) may play the most important role in the cross-dataset generalization challenge. Finally, we provide an open-source benchmark platform GLOBEM – short for Generalization of LOngitudinal BEhavior Modeling – to consolidate all 19 algorithms. GLOBEM can support researchers in using, developing, and evaluating different longitudinal behavior modeling methods. We call for researchers’ attention to model generalizability evaluation for future longitudinal human behavior modeling studies.

Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Jeremy F. Huckins, Margaret E. Morris, Paula S. Nurius, Eve A. Riskin, Shwetak N. Patel, Tim Althoff, Andrew Campbell, Anind K. Dey, and Jennifer Mankoff. GlOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(4): 190:1-190:34 (2022).

Xuhai Xu, Han Zhang, Yasaman S. Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin S. Kuehn, Mike A. Merrill, Paula S. Nurius, Shwetak N. Patel, Tim Althoff, Margaret Morris, Eve A. Riskin, Jennifer Mankoff, Anind K. Dey:
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. NeurIPS 2022

Maptimizer

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Megan Hofmann, Kelly Mack, Jessica Birchfield, Jerry Cao, Autumn G. Hughes, Shriya Kurpad, Kathryn J. Lum, Emily Warnock, Anat Caspi, Scott E. Hudson, Jennifer Mankoff:
Maptimizer: Using Optimization to Tailor Tactile Maps to Users Needs. CHI 2022: 592:1-592:15 [pdf]

Tactile maps can help people who are blind or have low vision navigate and familiarize themselves with unfamiliar locations. Ideally, tactile maps are created by considering an individual’s unique needs and abilities because of their limited space for representation. However, significant customization is not supported by existing tools for generating tactile maps. We present the Maptimizer system which generates tactile maps that are customized to a user’s preferences and requirements, while making simplified and easy to read tactile maps. Maptimizer uses a two stage optimization process to pair representations with geographic information and tune those representations to present that information more clearly. In a user study with six blind/low-vision participants, Maptimizer helped participants more successfully and efficiently identify locations of interest in unknown areas. These results demonstrate the utility of optimization techniques and generative design in complex accessibility domains that require significant customization by the end user.

A system diagram showing the maptimizer data flow setup. The inputs are geography sets, representations options, and user preferences. Geography types and representation options are paired and tuned using an optimizer. The output is a tactile map.

TypeOut: Just-in-Time Self-Affirmation for Reducing Phone Use

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Smartphone overuse is related to a variety of issues such as lack of sleep and anxiety. We explore the application of Self-Affirmation Theory on smartphone overuse intervention in a just-in-time manner. We present TypeOut, a just-in-time intervention technique that integrates two components: an in-situ typing-based unlock process to improve user engagement, and self-affirmation-based typing content to enhance effectiveness. We hypothesize that the integration of typing and self-affirmation content can better reduce smartphone overuse. We conducted a 10-week within-subject field experiment (N=54) and compared TypeOut against two baselines: one only showing the self-affirmation content (a common notification-based intervention), and one only requiring typing non-semantic content (a state-of-the-art method). TypeOut reduces app usage by over 50%, and both app opening frequency and usage duration by over 25%, all significantly outperforming baselines. TypeOut can potentially be used in other domains where an intervention may benefit from integrating self-affirmation exercises with an engaging just-in-time mechanism.

Typeout: Leveraging just-in-time self-affirmation for smartphone overuse reduction. Xuhai Xu, Tianyuan Zou, Xiao Han, Yanzhang Li, Ruolin Wang, Tianyi Yuan, Yuntao Wang, Yuanchun Shi, Jennifer Mankoff,and Anind K. Dey. 2022. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). ACM, New York, NY, USA.

Anticipate and Adjust: Cultivating Access in Human-Centered Methods

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In order for “human-centered research” to include all humans, we need to make sure that research practices are accessible for both participants and researchers with disabilities. Yet, people rarely discuss how to make common methods accessible. We interviewed 17 accessibility experts who were researchers or community organizers about their practices. Our findings emphasize the importance of considering accessibility at all stages of the research process and across different dimensions of studies like communication, materials, time, and space. We explore how technology or processes could reflect a norm of accessibility and offer a practical structure for planning accessible research.

Anticipate and Adjust: Cultivating Access in Human-Centered Methods. Kelly Mac, Emma J. McDonnell, Venkatesh Potluri, Maggie Xu, Jailyn Zabala, Jeffrey P. Bigham, Jennifer Mankoff, and Cynthia L. Bennett. CHI 2022