One’s profession is an essential part of modern life. Traditionally, professional development has been criticized for excluding people with disabilities. People with visual impairments, for example, face disproportionately low employment rates, highlighting persistent gaps in professional opportunities. Recently, there has been growing research on social media platforms as spaces for more equitable career development approaches. In this paper, we present an interview study on the professional development experiences of 60 people with visual impairments on TikTok (also known as “BlindTokers”). We report BlindTokers’ goals, strategies, and challenges, supported by detailed examples and in-depth analysis. Based on the findings, we identified that BlindTokers’ practices reveal an alternative professional development approach that is more flexible, inclusive, personalized, and diversified than traditional models. Our study also extends professional development research by foregrounding emerging digital skills and proposing design implications to foster more equitable and inclusive professional opportunities.

Social media platforms are important venues for identity expression, and the Human-Computer Interaction community has been paying growing attention to how marginalized groups express their identities on these platforms. Joining the emerging literature on intersectional experiences, we study blind TikTokers (“BlindTokers”) who are also women and/or LGBTQ+. Using interview data from 41 participants, we identify their intersectional experiences as mediated by TikTok’s socio-technical affordances. We argue that BlindTokers’ intersectional marginalization is infrastructural: TikTok’s classification and moderation features interact with social norms in ways that push them aside and distort how they are treated on the platform. We use this infrastructure perspective to understand what these experiences are, how they were formed, and how they become harmful. We further recognize participants’ infrastructuring work to address these problems. This study guides future social media design with accessible creator tools, inclusive identity options, and context-aware moderation developed in partnership with communities.

Learning through experience is essential for high-level autonomous driving systems, as it has the potential to enhance driving performance in corner cases. However, current decision and control modules adopt an empirical design paradigm for engineering efficiency, relying heavily on expert rules or real-vehicle data, failing to fully cover and optimize edge scenarios. To address this gap, we propose an enhanced integrated decision and control method that leverages reinforcement learning as the optimal control problem solver, endowing high-level automated vehicles with experience data usage. Specifically, a constrained mixed policy gradient algorithm is developed, which combines and dynamically adjusts the application ratio of experience data and the environmental model during training. This approach achieves fast convergence while maintaining high performance even with inaccurate analytic models. Furthermore, an attention based encoding network is designed to accommodate diverse driving states in urban traffic, integrating an embedding network for feature extraction and a weighting network for feature fusion, realizing order-insensitive encoding and importance differentiation of road users. The trained policy is deployed on a fully functional autonomous vehicle. Experiments at a signalized intersection show that the proposed method can accurately identify critical surrounding obstacles and execute safe, efficient, and intelligent driving behaviors across 32 scenarios.

Facial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives.

Infrastructure is an indispensable part of human life. In the past decades, the Human-Computer Interaction (HCI) community has paid increasing attention to human interactions with infrastructure. In this paper, we conducted a systematic literature review on infrastructure studies in SIGCHI, one of the most influential communities in HCI. We collected a total of 190 primary studies; the corpus includes studies published between 2006 and 2024. Most of the studies are inspired by Susan Leigh Star's notion of infrastructure. We discover three themes of infrastructure studies, including growing infrastructure, appropriating infrastructure, and coping with infrastructure. We foreground the overall trend of infrastructure studies in SIGCHI, which focuses on informal infrastructural activities in various socio-technical contexts. Especially, we discuss studies that problematize infrastructures and alert the HCI community about the underlying harmful side of infrastructure.

In recent years, third-party platforms for trading virtual game goods have rapidly gained popularity in China. These platforms refer to those that do not belong to game companies but still allow players to trade virtual game goods with one another. However, the motivations and practices surrounding trading on these platforms remain largely underexplored. To address this gap, we conducted an interview-based study with experienced players engaged in virtual goods trading. We used thematic analysis to examine the experiences and practices of players on these third-party trading platforms. Our work found that these third-party platforms have become an important component of maintaining players' gaming experience and converting large numbers of players into traders who earn cash from the game. Our research also offers nuanced insights into players' efforts to reconcile the dynamics between platforms and their expectations for virtual goods trading.

Safety guarantee is an important topic when training real-world tasks with reinforcement learning (RL). During online environmental exploration, any constraint violation can lead to significant property damage and risks to personnel. Existing safe RL methods either exclusively address safety concerns after reaching optimality or incorporate a certain degree of tolerance for constraint violations during training. This article proposes a feasible policy iteration framework that can guarantee absolute safety during online exploration, i.e., constraint violations never happen in real-world interactions. The key to maintaining absolute safety lies in confining the environmental exploration at each step always within the feasible region of the current policy. This feasible region is described by a newly defined constraint decay function with uncertainty, ensuring the forward invariance of the feasible region under the worst case. Within the proposed framework, the feasible region maintains its monotonic expanding property and converges to its maximum extent, even though only local samples are available, i.e., the agent only has access to samples within the feasible region. Meanwhile, the trained policy also improves monotonically within its corresponding feasible region if one can use different updating rules inside and outside the feasible region. Finally, practical algorithms are designed with the actor-critic-scenery architecture, consisting of three modules: 1) safe exploration; 2) model error estimation; and 3) network update. Experimental results indicate that our algorithms achieve performance comparable to baselines while maintaining zero constraint violation throughout the entire training process. In contrast, the baseline algorithm typically requires thousands of constraint violations to achieve the same performance. These findings suggest a substantial potential for applying feasible policy iteration in real-world tasks, enabling the online evolution of intricate systems.

Evolutionary algorithms (EAs) have shown promise in solving sequential decision problems (SDPs) by simplifying them to static optimization problems and searching for the optimal policy parameters in a zeroth-order way. While these methods are highly versatile, they often suffer from high sample complexity due to their ignorance of the underlying temporal structures. In contrast, reinforcement learning (RL) methods typically formulate SDPs as Markov Decision Process (MDP). Although more sample efficient than EAs, RL methods are restricted to differentiable policies and prone to getting stuck in local optima. To address these issues, we propose a novel evolutionary framework Zeroth-Order Actor-Critic (ZOAC). We propose to use step-wise exploration in parameter space and theoretically derive the zeroth-order policy gradient. We further utilize the actor-critic architecture to effectively leverage the Markov property of SDPs and reduce the variance of gradient estimators. In each iteration, ZOAC employs samplers to collect trajectories with parameter space exploration, and alternates between first-order policy evaluation (PEV) and zeroth-order policy improvement (PIM). To evaluate the effectiveness of ZOAC, we apply it to a challenging multi-lane driving task, optimizing the parameters in a rule-based, non-differentiable driving policy that consists of three sub-modules: behavior selection, path planning, and trajectory tracking. We also compare it with gradient-based RL methods on three Gymnasium tasks, optimizing neural network policies with thousands of parameters. Experimental results demonstrate the strong capability of ZOAC in solving SDPs. ZOAC significantly outperforms EAs that treat the problem as static optimization and matches the performance of gradient-based RL methods even without first-order information, in terms of total average return across all tasks.

Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle this challenge, we propose a physics-inspired optimization algorithm called relativistic adaptive gradient descent (RAD), which enhances long-term training stability. By conceptualizing neural network (NN) training as the evolution of a conformal Hamiltonian system, we present a universal framework for transferring long-term stability from conformal symplectic integrators to iterative NN updating rules, where the choice of kinetic energy governs the dynamical properties of resulting optimization algorithms. By utilizing relativistic kinetic energy, RAD incorporates principles from special relativity and limits parameter updates below a finite speed, effectively mitigating abnormal gradient influences. In addition, RAD models NN optimization as the evolution of a multiparticle system where each trainable parameter acts as an independent particle with an individual adaptive learning rate. We prove RAD's sublinear convergence under general nonconvex settings, where smaller gradient variance and larger batch sizes contribute to tighter convergence. Notably, RAD degrades to the well-known adaptive moment estimation (ADAM) algorithm when its speed coefficient is chosen as one and symplectic factor as a small positive value. Experimental results show RAD outperforming nine baseline optimizers with five RL algorithms across twelve environments, including standard benchmarks and challenging scenarios. Notably, RAD achieves up to a 155.1% performance improvement over ADAM in Atari games, showcasing its efficacy in stabilizing and accelerating RL training.

The Human-Computer Interaction (HCI) community has consistently focused on the experiences of users moderated by social media platforms. Recently, scholars have noticed that moderation practices could perpetuate biases, resulting in the marginalization of user groups undergoing moderation. However, most studies have primarily addressed marginalization related to issues such as racism or sexism, with little attention given to the experiences of people with disabilities. In this paper, we present a study on the moderation experiences of blind users on TikTok, also known as "BlindToker," to address this gap. We conducted semi-structured interviews with 20 BlindTokers and used thematic analysis to analyze the data. Two main themes emerged: BlindTokers’ situated content moderation experiences and their reactions to content moderation. We reported on the lack of accessibility on TikTok’s platform, contributing to the moderation and marginalization of BlindTokers. Additionally, we discovered instances of harassment from trolls that prompted BlindTokers to respond with harsh language, triggering further moderation. We discussed these findings in the context of the literature on moderation, marginalization, and transformative justice, seeking solutions to address such issues.

There has been extensive research on the experiences of individuals with visual impairments on text- and image-based social media platforms, such as Facebook and Twitter. However, little is known about the experiences of visually impaired users on short-video platforms like TikTok. To bridge this gap, we conducted an interview study with 30 BlindTokers (the nickname of blind TikTokers). Our study aimed to explore the various activities of BlindTokers on TikTok, including everyday entertainment, professional development, and community engagement. The widespread usage of TikTok among participants demonstrated that they considered TikTok and its associated experiences as the infrastructure for their activities. Additionally, participants reported experiencing breakdowns in this infrastructure due to accessibility issues. They had to carry out infrastructuring work to resolve the breakdowns. Blind users' various practices on TikTok also foregrounded their perceptions of independence. We then discussed blind users' nuanced understanding of the TikTok-mediated independence; we also critically examined BlindTokers' infrastructuring work for such independence.

Identity work in Human-Computer Interaction (HCI) has examined the asset-based design of marginalized groups who use technology to improve their quality of life. Our study illuminates the identity work of people with disabilities, specifically, visual impairments. We interviewed 45 BlindTokers (blind users on TikTok) from various backgrounds to understand their identity work from a positive design perspective. We found that BlindTokers leverage the affordance of the platform to create positive content, express their identities, and build communities with the desire to flourish. We proposed flourishing labor to present the work conducted by BlindTokers for their community's flourishing with implications to support the flourishing labor. This work contributes to understanding blind users' experience in short video platforms and highlights that flourishing is not just an activity for any single blind user but also a collective effort that necessitates serious and committed contributions from platforms and the communities they serve.

There will be a time when automated vehicles coexist with human-driven ones. Understanding how drivers assess driving risks and modeling their differences is crucial for developing human-like and personalized behaviors in automated vehicles, gaining people’s trust and acceptance. However, existing driving risk models are usually developed at a statistical level, and no single model can accurately describe and explain the variations in risk perception among drivers. We propose a concise yet effective model known as the Potential Damage Risk (PODAR) model, which provides a universal and physically meaningful structure for estimating driving risk and explaining the reasons for differences in risk perception. Leveraging an open-access dataset collected from an obstacle avoidance experiment, this paper establishes individual risk perception models for drivers with high fitness performances. We conclude that the variations in risk perception among drivers stem from their assessments of potential damage, accounting for the uncertainty in both temporal and spatial dimensions. Our findings offer an explanation for human risk perceptions and present a promising risk model for autonomous vehicles to develop human-like behaviors and personalized services.

The effective design of state representation plays a pivotal role in bridging the gap between perception and downstream decision-making and control in autonomous driving systems based on reinforcement learning. Among perception observations, accurately representing the set of surrounding participants poses significant challenges due to its variable size and unordered nature. These challenges result in dimension sensitivity and permutation sensitivity issues, respectively. To systematically tackle these complexities, we introduce a general framework for learning a state representation module, which can be regarded as a combination of any transformation and aggregation modules. Specifically, we employ a transformer encoder as the transformation module to attract features individually and design a novel aggregation module called Feature-wise Sorted Query (FSQ) to effectively aggregate the feature set into a state representation vector while adaptively attending to essential participant features. In FSQ, the feature-wise sort and latent query attention mechanisms can address permutation sensitivity and dimension sensitivity, respectively. Additionally, we propose an offline training paradigm to decouple state representation learning from downstream reinforcement learning tasks, enhancing stability and avoiding overfitting for particular scenarios. Simulation and experimental results demonstrate the superior performance and a more stable training process of our method across six varying-size set regression tasks and downstream autonomous driving tasks, particularly when leveraging the offline training paradigm.

., agents, provide real-time assistance to blind users via video-chat-like communication. Prior work identified several challenges for the agents to provide navigational assistance to users and proposed computer vision-mediated RSA service to address those challenges. We present an interactive system implementing a high-fidelity prototype of RSA service using augmented reality (AR) maps with localization and virtual elements placement capabilities. The paper also presents a confederate-based study design to evaluate the effects of AR maps with 13 untrained agents. The study revealed that, compared to baseline RSA, agents were significantly faster in providing indoor navigational assistance to a confederate playing the role of users, and agents' mental workload was significantly reduced-all indicate the feasibility and scalability of AR maps in RSA services.

Digital contact tracing is an ICT approach for controlling public health crises. It identifies users’ risk of infection based on their healthcare and travel information. In the COVID-19 pandemic, many countries implemented digital contact tracing to contain the coronavirus outbreak. However, the adoption rates vary significantly across different countries. In this study, we investigate Chinese people’s adoption of digital contact tracing. We aim at finding the influence of Chinese culture on people’s attitudes and behaviors toward the technology. We interviewed 26 Chinese participants and used thematic analysis to interpret the data. Our findings showed that Chinese culture shaped citizens’ interactions with the digital contact tracing at multiple levels; driven by the culture, Chinese citizens accepted digital contact tracing and contributed to making digital contact tracing a socio-technical infrastructure of people’s daily lives. We also discuss such cultural influences with the growing literature of human infrastructure and crisis informatics.