Anticipate and Adjust

Kelly MackEmma McDonnellVenkatesh PotluriMaggie XuJailyn ZabalaJeffrey Bigham, Jennifer Mankoff, Cynthia L. Bennett:
Anticipate and Adjust: Cultivating Access in Human-Centered Methods. CHI 2022: 603:1-603:18 [pdf] [Plain Language Summary]

“Human-centered research” must 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.

Gender in Online Doctor Reviews

Dunivin Z, Zadunayski L, Baskota U, Siek K, Mankoff J. Gender, Soft Skills, and Patient Experience in Online Physician Reviews: A Large-Scale Text Analysis. Journal of Medical Internet Research. 2020;22(7):e14455.

This study examines 154,305 Google reviews from across the United States for all medical specialties. Many patients use online physician reviews but we need to understand effects of gender on review content. Reviewer gender was inferred from names.

Reviews were coded for overall patient experience (negative or positive) by collapsing a 5-star scale and for general categories (process, positive/negative soft skills). We estimated binary regression models to examine relationships between physician rating, patient experience themes, physician gender, and reviewer gender.

We found considerable bias against female physicians: Reviews of female physicians were considerably more negative than those of male physicians (OR 1.99; P<.001). Critiques of female physicians more often focused on soft skills such as amicability, disrespect and candor. Negative reviews typically have words such as “rude, arrogant, and condescending”

Reviews written by female patients were also more likely to mention disrespect (OR 1.27, P<.001), but female patients were less likely to report disrespect from female doctors than expected.

Finally, patient experiences with the bureaucratic process also impacted reviews. This includes issues like cost of care. Overall, lower patient satisfaction is correlated with high physician dominance (e.g., poor information sharing or using medical jargon)

Limitations of our work include the lack of definitive (or non-binary) information about gender; and the fact that we do not know about the actual outcomes of treatment for reviewers.

Even so, it seems critical that readers attend to the who the reviewers are when reading online reviews. Review sites may also want to provide information about gender differences, control for gender when presenting composite ratings for physicians, and helping users write less biased reviews. Reviewers should be aware of their own gender biases and assess reviews for this (http://slowe.github.io/genderbias/).

Passively-sensing Discrimination

See the UW News article featuring this study!

A deeper understanding of how discrimination impacts psychological health and well-being of students would allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify the experience, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend less time in bed on the day of the event. Some of these patterns continue the next day.

Passively-sensed Behavioral Correlates of Discrimination Events in College Students. Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Tim Althoff, Anne Browning, Eve Ann Riskin, Paula S. Nurius, Anind K Dey, Jennifer Mankoff. CSCW 2019.

A bar plot sorted by number of reports, with about 100 reports of unfair treatment based on national origin, 90 based on intelligence, 70 based on gender, 60 based on apperance, 50 on age, 45 on sexual orientation, 35 on major, 30 on weight, 30 on height, 20 on income, 10 on disability, 10 on religion, and 10 on learning
Breakdown of 448 reports of unfair treatment by type. National, Orientation, and Learning refer to ancestry or national origin, sexual orientation, and learning disability respectively. See Table 3 for details of all categories. Participants were able to report multiple incidents of unfair treatment, possibly of different types, in each report. As described in the paper, we do not have data on unfair treatment based on race.
A heatplot showing sensor data collected by day in 5 categories: Activity, screen, locations, fitbit, and calls.
A heatplot showing compliance with sensor data collection. Sensor data availability for each day of the study is shown in terms of the number of participants whose data is available on a given day. Weeks of the study are marked on the horizontal axis while different sensors appear on the vertical axis. Important calendar dates (e.g., start / end of the quarter and exam periods) are highlighted as are the weeks of daily surveys. The brighter the cells for a sensor the larger the number of people contributing data for that sensor. Event-based sensors (e.g., calls) are not as bright as sensors continuously sampled (e.g., location) as expected. There was a technical issue in the data collection application in the middle of study, visible as a dark vertical line around the beginning of April.
A diagram showing compliance in surveys, organized by nweek of study. One line shows compliance in the large surveys given at pre, mid and post, which drops from 99% to 94% to 84%. The other line shows average weekly compliance in EMAs, which goes up in the second week to 93% but then drops slowly (with some variability) to 89%
Timeline and completion rate of pre, mid, and post questionnaires as well as EMA surveys. Y axis
shows the completion rates and is narrowed to the range 50-100%. The completion rate of pre, mid, and post questionnaires are percentages of the original pool of 209 participants, whereas EMA completion rates are based on the 176 participants who completed the study. EMA completion rates are computed as the average completion rate of the surveys administered in a certain week of the study. School-related events (i.e., start and end of quarters as well as exam periods) are marked. Dark blue bars (Daily Survey) show the weeks when participants answered surveys every day, four times a day
Barplot showing significance of morning screen use, calls, minutes asleep, time in bed, range of activities, number of steps, anxiety, depression, and frustration on the day before, of, and after unfair treatment. All but minutes asleep are significant at p=.05 or below on the day of discrimination, but this drops off after.
Patterns of feature significance from the day before to two days after the discrimination event. The
shortest bars represent the highest significance values (e.g., depressed and frustrated on day 0; depressed on day 1; morning screen use on day 2). There are no significant differences the day before. Most short-term relationships exist on the day of the event, a few appear on the next day (day 1). On the third day one
significant difference, repeated, from the first day is observed.

Who Gets to Future?

Picture of potted plants and a bench with the word Africatown in the background, painted in bright red and green colors

Who Gets to Future? Race, Representation, and Design Methods in Africatown

Jasper Tran O’Leary, Sara Zewde, Jennifer Mankoff , Daniela K. Rosner
CHI 2019

This paper draws on a collaborative project called the Africatown Activation to examine the role design practices play in contributing to (or conspiring against) the flourishing of the Black community in Seattle, Washington. Specifically, we describe the efforts of a community group called Africatown to design and build an installation that counters decades of disinvestment and ongoing displacement in the historically Black Central Area neighborhood. Our analysis suggests that despite efforts to include community, conventional design practices may perpetuate forms of institutional racism: enabling activities of community engagement that may further legitimate racialized forms of displacement. We discuss how focusing on amplifying the legacies of imagination already at work may help us move beyond a simple reading of design as the solution to systemic forms of oppression.

Understanding gender equity in author order assignment

Academic success and promotion are heavily influenced by publication record. In many fields, including computer science, multi-author papers are the norm. Evidence from other fields shows that norms for ordering author names can influence the assignment of credit. We interviewed 38 students and faculty in human- computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the field of computer science. We found that women were concerned with author order earlier in the process:

Our female interviews reported raising author order in discussion earlier in the process than men.

Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. We found expected results overall — being the most junior author increased the likelihood of first authorship, while being the most senior author increased the likelihood of last authorship. However, these effects disappeared or even reversed for women authors:

Comparison of regression weights for author rank (blue) with author rank crossed with gender (orange). Regression was predicting author position (first, middle, last)

Based on our findings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors affect women.