The tech industry's shifting landscape and the growing precarity of its labor force have spurred unionization efforts among tech workers. These workers turn to collective action to improve their working conditions and to protest unethical practices within their workplaces. To better understand this movement, we interviewed 44 U.S.-based tech worker-organizers to examine their motivations, strategies, challenges, and future visions for labor organizing. These workers included engineers, product managers, customer support specialists, QA analysts, logistics workers, gig workers, and union staff organizers. Our findings reveal that, contrary to popular narratives of prestige and privilege within the tech industry, tech workers face fragmented and unstable work environments which contribute to their disempowerment and hinder their organizing efforts. Despite these difficulties, organizers are laying the groundwork for a more resilient tech worker movement through community building and expanding political consciousness. By situating these dynamics within broader structural and ideological forces, we identify ways for the CSCW community to build solidarity with tech workers who are mate
Software engineers are responsible for developing, maintaining, and innovating software. To hire software engineers, organizations employ a tech hiring pipeline. This process typically consists of a series of steps to evaluate the extent to which applicants meet job requirements and can effectively contribute to a development team -- such as resume screenings and technical interviews. However, research highlights substantial flaws with current tech hiring practices -- such as bias from stress-inducing assessments. As the landscape of software engineering (SE) is dramatically changing, assessing the technical proficiency and abilities of software engineers is an increasingly crucial task to meet technological needs and demands. In this paper, we outline challenges in current hiring practices and present future directions to promote fair and evidence-based evaluations in tech hiring pipelines. Our vision aims to enhance outcomes for candidates and assessments for employers to enhance the workforce in the tech industry.
This chapter examines the sociotechnical imaginaries of Brazilian tech workers, a group often overlooked in digital labor research despite their role in designing the digital systems that shape everyday life. Grounded in the idea of sociotechnical imaginaries as collectively constructed visions that guide technology development and governance, the chapter argues that looking from the Global South helps challenge data universalism and foregrounds locally situated values, constraints, and futures. Drawing on semi-structured interviews with 26 Brazilian professionals conducted between July and December 2023, it maps how workers make sense of responsibility, bias, and power in AI and platform development. The findings highlight recurring tensions between academic and industry discourse on algorithmic bias, the limits of corporate accountability regarding user harm and surveillance, and the contested meanings of digital sovereignty, including grassroots initiatives that seek alternative technological futures aligned with marginalized communities needs.
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been accompanied by growing ethical concerns and intensified societal and environmental impacts. This position paper argues that irresponsible AI development is strongly driven by big tech's influence and involvement in the field. First, we examine the growing and disproportionate influence of big tech in AI research and argue that its drive for scaling and general-purpose systems is fundamentally at odds with the responsible, ethical, and sustainable development of AI. Second, we review key current environmental and societal negative impacts of AI and trace their connections to big tech's influence. Third, we discuss the underlying economic forces driving big tech's actions. Finally, as a call to action, we invite AI researchers to counter big tech's influence in irresponsible AI development through strategies that build on the responsibility of implicated actors and collective action.
[Context] Online Recruitment and Selection (R&S) processes are often the first point of contact between early-career software engineers and the tech industry. Yet many candidates experience these processes as opaque, inefficient, or even discouraging. While prior research has extensively documented the flaws and biases in online tech hiring, little is known about the practices that create positive candidate experiences. [Objective & Method] This paper explores such practices, referred to as Constructive Patterns (CPs), from the perspective of early-career software engineers. Guided by Applicant Attribution-Reaction Theory, we conducted 22 semi-structured interviews in which participants collectively described over 470 online R&S experiences. [Results] Through thematic analysis, we identified 22 CPs that reflect positive practices such as comprehensive and transparent job advertisements (CP01), specific and developmental feedback (CP03), humanized and respectful interaction (CP06), and framing the process as a two-way street (CP18). [Conclusion] Our findings extend the conversation on tech hiring beyond diagnosing dysfunctions toward designing for human-centered and grow
Address Sanitizer (ASan) is a sharp weapon for detecting memory safety violations, including temporal and spatial errors hidden in C/C++ programs during execution. However, ASan incurs significant runtime overhead, which limits its efficiency in testing large software. The overhead mainly comes from sanitizer checks due to the frequent and expensive shadow memory access. Over the past decade, many methods have been developed to speed up ASan by eliminating and accelerating sanitizer checks, however, they either fail to adequately eliminate redundant checks or compromise detection capabilities. To address this issue, this paper presents Tech-ASan, a two-stage check based technique to accelerate ASan with safety assurance. First, we propose a novel two-stage check algorithm for ASan, which leverages magic value comparison to reduce most of the costly shadow memory accesses. Second, we design an efficient optimizer to eliminate redundant checks, which integrates a novel algorithm for removing checks in loops. Third, we implement Tech-ASan as a memory safety tool based on the LLVM compiler infrastructure. Our evaluation using the SPEC CPU2006 benchmark shows that Tech-ASan outperforms
This article introduces the special issue "Technology Ethics in Action: Critical and Interdisciplinary Perspectives". In response to recent controversies about the harms of digital technology, discourses and practices of "tech ethics" have proliferated across the tech industry, academia, civil society, and government. Yet despite the seeming promise of ethics, tech ethics in practice suffers from several significant limitations: tech ethics is vague and toothless, has a myopic focus on individual engineers and technology design, and is subsumed into corporate logics and incentives. These limitations suggest that tech ethics enables corporate "ethics-washing": embracing the language of ethics to defuse criticism and resist government regulation, without committing to ethical behavior. Given these dynamics, I describe tech ethics as a terrain of contestation where the central debate is not whether ethics is desirable, but what "ethics" entails and who gets to define it. Current approaches to tech ethics are poised to enable technologists and technology companies to label themselves as "ethical" without substantively altering their practices. Thus, those striving for structural improv
Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.
Over the past decade, Big Tech has faced increasing levels of worker activism. While worker actions have resulted in positive outcomes (e.g., cancellation of Google's Project Dragonfly), such successes have become increasingly infrequent. This is, in part, because corporations have adjusted their strategies to dealing with increased worker activism (e.g., increased retaliation against workers, and contracts clauses that prevent cancellation due to worker pressure). This change in company strategy prompts urgent questions about updating worker strategies for influencing corporate behavior in an industry with vast societal impact. Current discourse on tech worker activism often lacks empirical grounding regarding its scope, history, and strategic calculus. Our work seeks to bridge this gap by firstly conducting a systematic analysis of worker actions at Google and Microsoft reported in U.S. newspapers to delineate their characteristics. We then situate these actions within the long history of labour movements and demonstrate that, despite perceptions of radicalism, contemporary tech activism is comparatively moderate. Finally, we engage directly with current and former tech activists
Technologies are increasingly enrolled in projects to involve civilians in the work of policy-making, often under the label of 'civic technology'. But conventional forms of participation through transactions such as voting provide limited opportunities for engagement. In response, some civic tech groups organize around issues of shared concern to explore new forms of democratic technologies. How does their work affect the relationship between publics and public servants? This paper explores how a Civic Tech Toronto creates a platform for civic engagement through the maintenance of an autonomous community for civic engagement and participation that is casual, social, nonpartisan, experimental, and flexible. Based on two years of action research, including community organizing, interviews, and observations, this paper shows how this grassroots civic tech group creates a civic platform that places a diverse range of participants in contact with the work of public servants, helping to build capacities and relationships that prepare both publics and public servants for the work of participatory democracy. The case shows that understanding civic tech requires a lens beyond the mere analy
Europe is at a make-or-break moment in the global AI race, squeezed between the massive venture capital and tech giants in the US and China's scale-oriented, top-down drive. At this tipping point, where the convergence of AI with complementary and synergistic technologies, like quantum computing, biotech, VR/AR, 5G/6G, robotics, advanced materials, and high-performance computing, could upend geopolitical balances, Europe needs to rethink its AI-related strategy. On the heels of the AI Action Summit 2025 in Paris, we present a sharp, doable strategy that builds upon Europe's strengths and closes gaps.
Today's largest technology corporations, especially ones with consumer-facing products such as social media platforms, use a variety of unethical and often outright illegal tactics to maintain their dominance. One tactic that has risen to the level of the public consciousness is the concept of addictive design, evidenced by the fact that excessive social media use has become a salient problem, particularly in the mental and social development of adolescents and young adults. As tech companies have developed more and more sophisticated artificial intelligence (AI) models to power their algorithmic recommender systems, they will become more successful at their goal of ensuring addiction to their platforms. This paper explores how online platforms intentionally cultivate addictive user behaviors and the broad societal implications, including on the health and well-being of children and adolescents. It presents the usage of addictive design - including the usage of dark patterns, persuasive design elements, and recommender algorithms - as a tool leveraged by technology corporations to maintain their dominance. Lastly, it describes the challenge of content moderation to address the prob
The Virginia Tech Transportation Safety Index (VTTSI) is a real-time, cloud-native framework for quantifying intersection safety using multimodal connected-vehicle telemetry and multi-year VDOT crash history. Traditional crash-based methods rely on lagged, aggregated data and cannot reflect rapidly changing operational conditions. VTTSI addresses this gap through a hybrid modeling approach that fuses Empirical Bayes (EB) crash stabilization, uplift factors derived from speed and conflict behavior, and a CRITIC-weighted multi-criteria decision-making (MCDM) module combining SAW, EDAS, and CODAS. The system produces interpretable, exposure-adjusted safety scores on a 0--100 scale every 15 minutes. A cloud-deployed architecture built on FastAPI, PostgreSQL, PostGIS, and Streamlit supports interactive visualization of traffic volumes, VRU exposure, speed variance, and real-time incident activity. Validation across intersections demonstrates coherent diurnal patterns, consistency among MCDM methods, and sensitivity to observable operational turbulence. Sensitivity analysis further shows that the RT--SI is robust to parameter perturbations, with deviations typically remaining below one p
This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformative paradigm. We propose computational design synthesis (CDS), a framework utilising deep learning and generative AI to automate the creation of novel systems. Two case studies (e-drive system design and spatial dimensioning problem) serve as proof-points for this approach. The AI-driven methods used in the case studies represent a fundamental shift in engineering, advancing from simulation-based optimisation towards autonomous design with minimal human supervision.
Tech neck is a modern epidemic caused by prolonged device usage and it can lead to significant neck strain and discomfort. This paper addresses the challenge of detecting and preventing tech neck syndrome using non-invasive ubiquitous sensing techniques. We present NeckCare, a novel system leveraging hearable sensors, including IMUs and microphones, to monitor tech neck postures and estimate distance form screen in real-time. By analyzing pitch, displacement, and acoustic ranging data from 15 participants, we achieve posture classification accuracy of 96% using IMU data alone and 99% when combined with audio data. Our distance estimation technique is millimeter-level accurate even in noisy conditions. NeckCare provides immediate feedback to users, promoting healthier posture and reducing neck strain. Future work will explore personalizing alerts, predicting muscle strain, integrating neck exercise detection and enhancing digital eye strain prediction.
Guitar-related machine listening research involves tasks like timbre transfer, performance generation, and automatic transcription. However, small datasets often limit model robustness due to insufficient acoustic diversity and musical content. To address these issues, we introduce Guitar-TECHS, a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer's head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms. We provide empirical data to demonstrate the dataset's effectiveness in training robust models for Guitar Tablature Transcription.
In the face of rapidly advancing technologies, evidence of harms they can exacerbate, and insufficient policy to ensure accountability from tech companies, what are HCI opportunities for advancing policymaking of technology? In this paper, we explore challenges and opportunities for tech policymaking through a case study of app-based rideshare driving. We begin with background on rideshare platforms and how they operate. Next, we review literature on algorithmic management about how rideshare drivers actually experience platform features -- often to the detriment of their well-being -- and ways they respond. In light of this, researchers and advocates have called for increased worker protections, thus we turn to rideshare policy and regulation efforts in the U.S. Here, we differentiate the political strategies of platforms with those of drivers to illustrate the conflicting narratives policymakers face when trying to oversee gig work platforms. We reflect that past methods surfacing drivers' experiences may be insufficient for policymaker needs when developing oversight. To address this gap and our original inquiry -- what are HCI opportunities for advancing tech policymaking -- we
By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. In this tech report, we describe the most recent release of the C-RADIO family of models, C-RADIOv4, which builds upon AM-RADIO/RADIOv2.5 in design, offering strong improvements on key downstream tasks at the same computational complexity. We release -SO400M (412M params), and -H (631M) model variants, both trained with an updated set of teachers: SigLIP2, DINOv3, and SAM3. In addition to improvements on core metrics and new capabilities from imitating SAM3, the C-RADIOv4 model family further improves any-resolution support, brings back the ViTDet option for drastically enhanced efficiency at high-resolution, and comes with a permissive license.
Technology companies have gained unprecedented power and influence in recent years, resembling quasi-nation-states globally. Corporations with trillion-dollar market capitalizations are no longer just providers of digital services; they now wield immense economic power, influence global infrastructure, and significantly impact political and social dynamics. This thesis examines how these corporations have transcended traditional business models, adopting characteristics typically associated with sovereign states. They now enforce regulations, shape public discourse, and influence legal frameworks in various countries. This shift presents unique challenges, including the undermining of democratic governance, the exacerbation of economic inequalities, and the enabling of unregulated data exploitation and privacy violations. The study will examine critical instances of tech companies acting as quasi-governmental bodies and assess the risks associated with unchecked corporate influence in global governance. Ultimately, the thesis aims to propose policy frameworks and regulatory interventions to curb the overreach of tech giants, restoring the balance between democratic institutions and
Predicting stock price movements is a pivotal element of investment strategy, providing insights into potential trends and market volatility. This study specifically examines the predictive capacity of historical stock prices and technical indicators within the Global Industry Classification Standard (GICS) Information Technology Sector, focusing on companies established before 1980. We aim to identify patterns that precede significant, non-transient downturns - defined as declines exceeding 10% from peak values. Utilizing a combination of machine learning techniques, including multiple regression analysis, logistic regression, we analyze an enriched dataset comprising both macroeconomic indicators and market data. Our findings suggest that certain clusters of technical indicators, when combined with broader economic signals, offer predictive insights into forthcoming sector-specific downturns. This research not only enhances our understanding of the factors driving market dynamics in the tech sector but also provides portfolio managers and investors with a sophisticated tool for anticipating and mitigating potential losses from market downturns. Through a rigorous validation proce