Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a “counterventional approach” to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that are fundamentally divergent from dominant approaches. We then explicate characteristics of “counterventions” – projects that aim to contest dominant sociotechnical paradigms through privileging community and participants in research inquiry, interaction design, and analysis of outcomes. These divergent research imaginaries have transformative implications for how interventionist HCI might be conducted in future.

Disinformation is false information created to mis-lead public belief. In politics, disinformation is often used to persuade public opinion and achieve political victory. False information can be spread through word of mouth, news articles, images, and videos. The use of manipulated media is becoming a source of concern for the general public. Researchers are exploring the impact of manipulated media on public opinion. While prior research has investigated the effects of various news articles and images, to our knowledge, researchers have yet to explore the impact of manipulated videos of political figures on public opinion. We conducted a between-subjects user study with 420 participants. First, participants viewed edited or unedited videos related to a political candidate and then answered some survey questions regarding their opinion on the political figures. Our findings suggest that video headlines and news sources have influenced user interpretations and beliefs. However, many participants were skeptical about the authenticity of the videos. Overall, we see that a viewer’s political leanings do affect their judgment of a video’s authenticity but the Figure involved and the context of the video matter as well.

It has been nearly 20 years since the U.S. Supreme Court ruled on the use of race/ethnicity, gender, and national origin in university admissions in the University of Michigan cases. It is now 2021 and universities are still struggling with how to diversify their admissions offers within the bounds of the law. In response to this ongoing issue, I created Applications Quest, an equitable AI tool that adheres to the legal use of race, gender, national origin, etc. in admissions and hiring decisions. In this keynote address, I will explain how AI, specifically, Applications Quest, can be used to create equitable recommendations for admissions decisions. I will give a demonstration of how Applications Quest increases diversity compared to admissions committees while achieving the same academic achievement levels as the committee. Given Applications Quest is an unsupervised AI, it has the advantage of ignorance of race/ethnicity, gender, national origin, etc. Therefore, when used in admissions, it provides unbiased recommendations that can be interrogated by human evaluators. Applications Quest is a human-centered AI tool for achieving equity in admissions.

According to the U.S. Census, the United States of America will become a majority-minority country by 2045. As the U.S. experiences this demographic shift, what will happen to computer science? Will our discipline become more inclusive? What shifts will occur in computer science? Computer science does not have a good record addressing its diversity challenges. A simple Google search for "Silicon Valley's Diversity Problem" really makes this clear. Further exploration into data from the Computing Research Association (CRA), shows how underrepresented minority groups are in computing at the Bachelor, Master, Doctoral, and faculty levels. Can computer science improve its diversity during the demographic shift? Dr. Gilbert believes it is possible to change the demographics in computer science, but it will require a cultural shift in computing along with institutional change, starting with computer science education.

The demand for machine learning and data science has grown exponentially in recent years. Yet, as the influence of these fields reach farther into daily life, the disparate impacts of these algorithms and models on more marginalized populations have also begun to surface rapidly. To address this emerging crisis, it is necessary to equip the next generation of computer scientists with the ethical tools needed to tackle these issues. Thus, an exploratory study was conducted to investigate how students who are currently enrolled in computing-related programs evaluate and understand the ethical and social impact of technology. 43 students in computing majors were presented with 5 scenarios of different technologies that utilizes machine learning to address potentially sensitive areas (e.g. policing, medical diagnosing). The long-format responses to these scenarios were qualitatively analyzed. Additionally, quantitative analysis was conducted after qualitatively coding the long-format responses into four sentiments. Ultimately, we found that participants were able to decipher the social implications of technology. However, many issues of systemic discrimination were missing from participants' analysis. Alarmingly, our findings also indicated that 50% or more of participants were not exposed to most of the technologies highlighted in the scenarios, which highlights a potential gap in computing curriculum of connecting ethics as well as racial, cultural, and socioeconomic understanding to computer science. Based on these results, we suggest that computing-related curriculum be reevaluated with ethical training in mind.

The computer science education community has long strived to create more equitable opportunities for students, such as initiatives to foster inclusion of women and other people from historically marginalized groups in CS. Despite these efforts, the gender gap has persisted, with less than a quarter of CS Bachelor's degrees awarded to women in the United States in 2019. As a community, we must strive to improve women's experiences in CS. This paper describes work conducted at a large research university which has traditionally offered CS1 through lecture sections ranging in size from 400-650 students. In Fall 2019, we offered an alternative small all-women's class (35 students) in addition to the traditional lecture class (601 students; 149 women). Both classes covered the same CS concepts but were led by different instructors. Students reported on their experience through a survey administered at the end of the semester. Students in the all-women's class reported significantly greater social connections and comfort collaborating with their peers compared to women in the traditional class. They also reported significantly greater feelings of support within their class, more confidence in their CS knowledge, and a more welcoming classroom environment compared to women in the traditional class. Additionally, the drop rate for students in the all-women's class was significantly lower (5.7%) than the drop rate for women in the traditional class (24.8%). In light of these positive results, we provide actionable insights for CS educators and discuss how to better support women in their CS endeavors.

This paper aims to contribute to the literature by presenting a user-centered design research methodology for the design of police-community tools and by introducing the Virtual Traffic Stop mobile system. Though traffic stops may be an irregular occurrence to the everyday driver, when the driving population is looked at as a whole, they are, in fact, a frequent affair. Traffic stops make up the most common type of contact civilians have with the police. In recent years, media footage of traffic stops has shown how dangerous these encounters can be. Researchers conducted unstructured group interviews and participated in ride-along drives with local law enforcement officials. From this method, four user requirements are presented to the community at large: the system does not impair the safety of officers, the system allows for community engagement, the system should serve as a de-escalation tool, and the system should be cost-effective and easily adaptable for agencies and citizens alike.

Rural communities are more dependent on transportation than their urban peers. Transportation within rural areas is necessary to access food, healthcare, educational opportunities, and employment, especially since rural residents have longer distances to travel to access them. Therefore, the availability of efficient and affordable transportation can lead to economic growth in rural areas and ensure that people can obtain the services they need. Autonomous Vehicles (AV) can improve accessibility and mobility in these communities. However, research that discusses autonomous vehicles' impact mostly focuses on urban transportation with limited focus on rural areas. In this paper, the barriers to adopting autonomous vehicles in rural areas are discussed by examining the current struggles of rural communities concerning finance, transportation infrastructure, policy, and demographics. First, we suggest that companies that design and create autonomous vehicles investigate efficient ways to ensure they are affordable to rural communities. Additionally, when using AVs for ridesharing, it must be ensured that rural communities have the financial means to use them. Second, companies must be attentive to these vehicles' accessibility to ensure that all individuals, including disabled and older adults, can use them without difficulty in rural communities. To maximize these vehicles' accessibility, they need to embrace the community in the design and policy-making processes. Lastly, any technologies used in these vehicles, such as facial recognition systems, need to address the potential of bias against minorities in rural communities to avoid the future disparate impacts and injustice toward particular groups of people in the society.

Artificial Intelligence (Al) and Machine Learning (ML) have been influential across many industries. Companies, nearly every day, are finding new means and methods of benefiting from these technologies. Despite this prevalence, individuals still report a significant level of distrust towards Al and its applications. To rehabilitate the relationship between Al and its consumers, developers must expose these new technologies to consumers and include them in the process of critiquing and assisting in the improvement of such technologies. The goal of this work is to introduce a new initiative towards an Ethical Al society. Participants are given the opportunity to learn about modem applications of Al and the space to reflect on these technologies. It is found that across the exampled technologies, differences of opinions are significantly correlated to specific dispositional identities, such as gender and computing experience. Furthermore, trends of trust across the general public are compared to that of students enrolled in a computer science course. These results depict vastly differing opinions across technologies which validate the need for public exposure and critique. This work highlights the need for researchers and developers to investigate opinions across dispositional identities, including race, gender, socioeconomic status, etc. The study has shown to be beneficial, with over 70% of individuals reporting having learned about a new application of Al.

Robocalls interrupt daily activity, cause financial harm, and influence users to ignore calls from unfamiliar numbers. Service providers and developers have created Anti-Robocall applications to attempt to restore trust in the phone and decrease the impact of robocalls on daily life. However, whether or not such applications meet accessibility standards and are therefore usable by vulnerable populations, particularly the visually impaired, is unknown. In this paper, we use a combination of the W3C's Mobile Web Content Accessibility Guidelines (MWCAG) and interviews with 11 visually impaired users to establish accessibility metrics for Anti-Robocall applications. We then evaluate 56 Anti-Robocall applications for Android to assess whether they met the needs of the visually impaired community. Our results indicate that 100% of the applications fail to meet all basic accessibility guidelines including minimum color contrast, button labels (to assist screen readers), and automatic audible alerts. As a result, we show that despite the availability of a variety of tools to help developers identify and correct these problems, this important class of applications does not meet basic accessibility requirements. We conclude by suggesting viable paths forward that ensure inclusion and protection for the visually impaired community.

According to the U.S. Census, the United States of America will become a majority-minority country by 2045. As the U.S. experiences this demographic shift, what will happen to computer science? Will our discipline become more inclusive? What shifts will occur in computer science? Computer science does not have a good record addressing its diversity challenges. A simple Google search for "Silicon Valley's Diversity Problem", really makes this clear. Further exploration into data from the Computing Research Association (CRA), shows how underrepresented minority groups are in computing at the Bachelor, Master, Doctoral, and faculty levels. Can computer science improve its diversity during the demographic shift? Dr. Gilbert believe it is possible to change the demographics in computer science, but it will require a cultural shift in computing along with institutional change, starting with computer science education.

Software is a critical component of brain-computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.

In a world that demands independent and cooperative problem solving to address complex social, economic, ethical, and personal concerns, core social studies content is as basic for success as reading, writing, math, and computing. Nationally, only 20% of 4th grade students are at or above Proficient level in U.S. History, the lowest among the core disciplines of social studies. Reaching proficiency requires students to ask more profound questions of the past as well as construct deeper understandings of it. This research explores the intersection of natural language dialogue and intelligent tutoring systems to enhance history learning of upper elementary students in 3rd and 4th grade. Elementary and middle school students were participants in a participatory design study to extract their design needs for the development of an educational learning technology for social studies.

This paper presents the initial results of an ongoing research project to develop a Math Word Problem (MWP) generator with engaging personalized content to improve students' math achievements and attitudes. A mixed methods study was used to observe the interests of 5th-grade students' and their attitudes and challenges as it relates to MWPs. Among this study's sample of students, we found contrasting interests that are specific to several of their task values. The baseline data and interests received through the interviews and the participatory drawing session will be used to inform the development of the prototype.

Fully autonomous or “self-driving” vehicles are an emerging technology that may hold tremendous mobility potential for individuals who are visually impaired who have been previously disadvantaged by an inability to operate conventional motor vehicles. Prior studies however, have suggested that these consumers have significant concerns regarding the accessibility of this technology and their ability to effectively interact with it. We present the results of a quasi-naturalistic study, conducted on public roads with 20 visually impaired users, designed to test a self-driving vehicle human–machine interface. This prototype system, ATLAS, was designed in participatory workshops in collaboration with visually impaired persons with the intent of satisfying the experiential needs of blind and low vision users. Our results show that following interaction with the prototype, participants expressed an increased trust in self-driving vehicle technology, an increased belief in its likely usability, an increased desire to purchase it and a reduced fear of operational failures. These findings suggest that interaction with even a simulated self-driving vehicle may be sufficient to ameliorate feelings of distrust regarding the technology and that existing technologies, properly combined, are promising solutions in addressing the experiential needs of visually impaired persons in similar contexts.

Brain-Computer Interface (BCI) hardware is becoming more affordable and accessible. However, there is limited work investigating ways to design software that broadens participation with BCI technology. In this article, we present a block-based programming environment designed to assist novice programmers with creating BCI applications. We also discuss learning barriers encountered by novice programmers developing neurofeedback applications. Our findings suggest that visual programming assists novice programmers with building basic BCI applications; however, students may experience understanding and learning barriers initially.

Algebra is deemed necessary for access to STEM courses and career fields. Research suggests that the low performance experienced nationwide is due to a weakened arithmetic foundation needed before the transition to algebraic thinking. African-Americans, in particular, are underperforming in Algebra, and many other levels of math. This research explores the intersection of African-American culture and educational technology to strengthen their foundation by improving skills necessary for success in Algebra 1. 6th and 7th grade African-American students were participants in a gesture elicitation to garner gestural input for the development of an educational technology designed to assist in pre-Algebra practice. Preliminary classification results suggested agreement for eight of the nine functional tasks assigned to the participants.

As CS + X courses become more common, it is important for us to investigate ways to leverage interdisciplinary learning tools to expand the types of experiences available to students. This paper discusses our experiences introducing CS undergraduates to basic Brain-Computer Interface (BCI) concepts using NeuroBlock. Neuroblock is a visual programming environment that allows users to build applications driven by near-real-time neurophysiological (i.e., brainwaves) data. Brainwave data is captured using a commercial-grade BCI device. Students use brainwave data from the BCI device to create interactive hybrid-BCI applications (e.g., games) featuring objects that respond to students' affective states (e.g. engagement, relaxation, and attention) and keyboard events. In this paper, we describe NeuroBlock, three example activities, and results from an exploratory empirical study that suggests exposure to NeuroBlock increased students' confidence in their ability to develop applications that leverage neurophysiological signals. NeuroBlock and the discussed activities have the potential to supplement future CS + X courses by providing students hands-on experiences with emerging physiological devices.

Fully autonomous vehicles, commonly referred to as self-driving vehicles, are an emerging technology that may hold tremendous mobility potential for individuals who are blind or visually impaired who have been previously disadvantaged by an inability to operate conventional motor vehicles. This study explores the opinions of 38 participants who are blind and low vision, through the use of focus group methodology, regarding this emerging self-driving vehicle technology. Participants were overwhelmingly optimistic about the potential for independence and mobility that self-driving vehicles may provide but were concerned that the needs of individuals with visual impairments were not being adequately considered in the development of the technology. Participants also raised questions about how the technology would satisfy their need for situational awareness, how the technology would enable blind or visually impaired operators to verify their arrival at their desired location and a host of issues related to parking, vehicle location and roadside assistance. Participants also expressed a preference for smartphone and speech input capabilities as a primary means of system interaction. These findings suggest that at a minimum more needs to be done to engage individuals with visual impairments in the development of self-driving vehicle technology and to increase awareness of manufacturer efforts.

In 2012, about one-third of voters with disabilities reported having issues when voting in a polling place. Although the Help America Vote Act (HAVA) was passed in 2002, it is clear that there is room for improvement within the domain of accessible voting. Prime III is a voting technology that addresses many issues that plague other accessible voting systems. By addressing the needs of different communities, Prime III has become a ballot marking system that allows all voters to vote on one machine. This demonstration will showcase the accessibility features of Prime III and how it can be used in elections.

Dynamicity and infrastructure-less nature of MANETs expose the routing in such networks to a variety of attacks, and moreover, make the conventional fixed policy routing algorithms inefficient. To deal with the routing challenges and varying behavior of malicious nodes in such networks, employing reinforcement learning algorithms and proper trust models seem promising. In this paper, we introduce a cognition layer in parallel and interacting with the network layer which comprises two cognitive processes: path learning (routing) and trust learning. The first process is based on machine learning algorithms and the latter is based on trust management. We compare our algorithm, TQOR, with a well known trust-based routing protocol, TQR, in terms of three measures of performance. The simulation results show better end-to-end delay and communication overhead which further improve as time progresses, without sacrificing the data packet delivery ratio.

Brain-Computer Interface (BCI) applications are gaining popularity as Electroencephalography (EEG) hardware becomes more accessible. BCI technology is used for various purposes such as neurophysiological evaluation, device control, user-state monitoring, and cognitive improvement. Although BCI software platforms exist, there are few systems designed to assist novice programmers with creating BCI applications. We present “NeuroBlock”, a block-based programming approach to neurofeedback application development. Motivated by insights presented in BCI and visual languages literature, our system enables novice programmers to build applications that adapt to users' affective state. We evaluated the system's appropriateness by tasking novice programmers with developing neurofeedback applications inspired by previous BCI studies. Our exploratory study with 40 participants demonstrates that novice programmers are capable of developing neurofeedback applications using NeuroBlock. Furthermore, participants had a positive perception of NeuroBlock's usability.

The overall purpose of our research is to identify unique benefits and challenges in gamifying personalized augmented reality experiences in K -- 12 education for minority students. Gamification is applying game-like elements and principles in a non-gaming environment with the desired outcome of increasing engagement, motivation and learning. Augmented reality (AR) technologies project virtual objects onto the real world. Personalized AR potentially creates new levels of engagement for students while simultaneously accounting for their skill level, strengths, challenges and personal as well as cultural needs to increase learning. Using gamification in augmented or virtual environments is not a new phenomenon for researchers, however, the model presented in this work will leverage gamified AR experiences while incorporating cultural relevance to bring a new perspective for keeping minority students engaged and increasing learning outcomes.

This paper presents an online web-based culturally relevant algebra tutor. It is designed for African American students to improve their mathematical skills. As a critical part of a culturally relevant tutor framework, AADMLSS (African-American Distributed Multiple Learning Styles System) focuses on practicing algebra, but can be adapted to other courses. The Algebra Problems Practice (APP) module is the practice environment where students can extract an algebra problem and practice solving it through interaction with a 3D virtual tutor. Practice on this system, tailored with familiar cultural elements, is expected to improve students' ability and skills in mathematics.

Manuals were designed to provide support and information about the usage and maintenance of the vehicle. In many cases user's manuals are the driver's only guidance. However, lack of clarity and efficiency of manuals lead to user dissatisfaction. In vehicles this problem is even more crucial given that driving a motor vehicle is, for many people, the most complex and potentially dangerous task they will perform during their lifetime. In this paper we present a voice interfaced driver manual that can potentially fix the deficiencies of its alternatives. In addition we aim to provide a case for the integration of such technology in a vehicle to reduce driver distraction, increase driver satisfaction, and manual usability, while also benefiting Original Equipment Manufacturers (OEMs) in lowering costs and reducing the documentation process.

Voting technology today has not addressed the issues that disabled voters are confronted with at the polls. Because approximately 17% of the voting population is disabled, their issues should be handled with a solution geared towards their needs. Disabled voters need to be able to cast their vote without the assistance of others. The Prime III multimodal voting system [2] addresses these issues. This demonstration will illustrate the use of the Prime III system, a virtual reality (VR) version (Prime V), and a similar version created using a voice user interface (VUI).

The Montgomery county corrections program is a program designed to address the problem of overcrowded jails by providing an out-of-jail rehabilitative program as an alternative. The candidate offenders chosen for this program are offenders convicted on nonviolent charges and are currently chosen subjectively with little statistical basis. In addition, historical data has been recorded on offenders who have passed through the program, making the program a good candidate for case-based reasoning. Using such reasoning, county officials would like an objective measurement which will predict the success or failure of a candidate offender based on past offender history. The four case-based reasoning algorithms chosen for this prediction are discrete, continuous and distance weighted k-nearest neighbors and a general regression neural network (GRNN). Although all four algorithms prove to be an improvement on the current system, the GRNN performs the best, with an average accuracy rate of 68%.

Microarray Classification compares thousands of genes of an unknown patient with the genes of known patients in order to predict and diagnose diseases. Since first introduced, many algorithms and techniques have been applied in search of the best solution. In response, the Seventh International Conference on Machine Learning and Applications (ICMLA) is holding a competition in search of the best classification technique. As an entry, the authors use a General Regression Neural Network, in conjunction with a Particle Swarm Optimizer to predict microarray classifications. The GRNN is trained and tested on two datasets: colon cancer and leukemia. The algorithm is evaluated using two measures: Area Under the Receiver Operating Characteristics Curve (AUROC) and Accuracy. The algorithmpsilas best averages for the colon cancer dataset are an AUROC value of 0.90044 and an accuracy of 78.4% and for the leukemia dataset, 0.978214 and 89.5%, proving it to be a useful tool.

The research proposed here concentrates on the problem of designing and developing a spoken query retrieval (SQR) system to access large document databases via voice. The main challenge is to identify and address issues related to designing an effective and efficient speech user interface (SUI), especially if the aim is to facilitate spoken queries of large document databases. Furthermore, the task of presenting large query result sets aurally should be performed such that the user's short term memory is not overloaded. In this paper, a framework allowing information retrieval to large document databases via voice is presented and findings from a research study using the framework will be discussed as well.

Web based instruction is growing at an incredible rate. Teachers, instructors, trainers and several others are putting their instructional content online. The format, style and media types vary from instructor to instructor. In essence, Web based instruction is being done by an unknown number of people in an unknown number of ways, using different media types and platforms. An instruction repository is introduced that has the ability to unite all Web based instruction under one umbrella. The media types, platforms, instructors and formats will remain independent. The repository will create a new instructional model for Web based instruction.