Generative AI (GenAI) is a powerful technology poised to reshape Trust & Safety. While misuse by attackers is a growing concern, its defensive capacity remains underexplored. This paper examines these effects through a qualitative study with 43 Trust & Safety experts across five domains: child safety, election integrity, hate and harassment, scams, and violent extremism. Our findings characterize a landscape in which GenAI empowers both attackers and defenders. GenAI dramatically increases the scale and speed of attacks, lowering the barrier to entry for creating harmful content, including sophisticated propaganda and deepfakes. Conversely, defenders envision leveraging GenAI to detect and mitigate harmful content at scale, conduct investigations, deploy persuasive counternarratives, improve moderator wellbeing, and offer user support. This work provides a strategic framework for understanding GenAI’s impact on Trust & Safety and charts a path for its responsible use in creating safer online environments.

We introduce a risk assessment framework for digital identification systems, as well as recommended best practices to enhance privacy, security, and other desirable properties in these systems. To generate these resources, we created a casebook of a wide range of digital identification systems, and we then applied expert analysis and critique to identify patterns. We piloted the framework on several reviews within our organization over a period of approximately one year, and found it to be robust and helpful for those reviews. This work is intended to inform product review and development, product policy, and standards efforts, and to help guide a consistent responsible approach to digital identification across the broader digital identification ecosystem.

Data workers play a key role in the big data industry. Clients hire data workers to collect and annotate data with human identity concepts, like demographic categories or clothing items. Often, such workers are treated as computational-they are expected to quickly and objectively conduct their work, with the goal of having huge, unbiased datasets for training models. Computer vision is especially interested in fair and impartial data due to biases and unethical practices in the field. However, far from impartial, data workers imbue computer vision data with ''biases'' beyond correct versus incorrect answers. Data workers embed their own specific positional perspectives about identity concepts in both collection and annotation processes. Through interviews and ethnographic observations of data workers (freelance and business process outsourcing (BPO) employees), we show how worker positionality influences decisions during data work. We also show the unintended outcomes, like social biases, that occur when positionality is not explicitly attended to in client instructions. We discuss how employing a lens of positionality in data work reveals the gulfs between data worker perspectives and client expectations, which are colored by a web of positional actors beyond isolated data workers. We propose positional (il)legibility as an approach to data work that embraces the reality of positionality in classification practices and addresses the failures of positivist bias mitigation practices.

Generative AI is expected to have transformative effects in multiple knowledge industries. To better understand how knowledge workers expect generative AI may affect their industries in the future, we conducted participatory research workshops for seven different industries, with a total of 54 participants across three US cities. We describe participants’ expectations of generative AI’s impact, including a dominant narrative that cut across the groups’ discourse: participants largely envision generative AI as a tool to perform menial work, under human review. Participants do not generally anticipate the disruptive changes to knowledge industries currently projected in common media and academic narratives. Participants do however envision generative AI may amplify four social forces currently shaping their industries: deskilling, dehumanization, disconnection, and disinformation. We describe these forces, and then we provide additional detail regarding attitudes in specific knowledge industries. We conclude with a discussion of implications and research challenges for the HCI community.

We describe "Discover AI in Daily Life", a lesson in Google's Applied Digital Skills curriculum. The lesson introduces elements of AI literacy and is freely available online at g.co/DiscoverAI. It is designed for middle school students while also supporting high school and adult learners.

As the influence and use of artificial intelligence (AI) have grown and its transformative potential has become more apparent, many questions have been raised regarding the economic, political, social, and ethical implications of its use. Public opinion plays an important role in these discussions, influencing product adoption, commercial development, research funding, and regulation. In this paper we present results of an in-depth survey of public opinion of artificial intelligence conducted with 10,005 respondents spanning eight countries and six continents. We report widespread perception that AI will have significant impact on society, accompanied by strong support for the responsible development and use of AI, and also characterize the public's sentiment towards AI with four key themes (exciting, useful, worrying, and futuristic) whose prevalence distinguishes response to AI in different countries.

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road. In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product.

Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

In this paper we consider various genres of citizen science from the perspective of citizen participants. As a mode of scientific inquiry, citizen science has the potential to "scale up" scientific data collection efforts and increase lay engagement with science. However, current technological directions risk losing sight of the ways in which citizen science is actually practiced. As citizen science is increasingly used to describe a wide range of activities, we begin by presenting a framework of citizen science genres. We then present findings from four interlocking qualitative studies and technological interventions of community air quality monitoring efforts, examining the motivations and capacities of citizen participants and characterizing their alignment with different types of citizen science. Based on these studies, we suggest that data acquisition involves complex multi-dimensional tradeoffs, and the commonly held view that citizen science systems are a win-win for citizens and science may be overstated.

In this paper, we report on a qualitative study of how users manage their reputation online. We focus particularly on people who are bothered by content online about themselves and how they manage reputation damage and repair. We describe how users view reputation management chores as necessary but unpleasant, and how they feel disempowered to repair their online reputation. Participants were unable to identify feasible repair mechanisms and ultimately failed to resolve their problems. Given the current state of dysfunction indicated by our findings, we advocate for increased HCI research attention to this area.

Researchers are developing mobile sensing platforms to facilitate public awareness of environmental conditions. However, turning such awareness into practical community action and political change requires more than just collecting and presenting data. To inform research on mobile environmental sensing, we conducted design fieldwork with government, private, and public interest stakeholders. In parallel, we built an environmental air quality sensing system and deployed it on street sweeping vehicles in a major U.S. city; this served as a research vehicle"by grounding our interviews and affording us status as environmental action researchers. In this paper, we present a qualitative analysis of the landscape of environmental action, focusing on insights that will help researchers frame meaningful technological interventions.

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.

We present a qualitative study of 35 United States households whose occupants have made significant accommodations to their homes and behaviors in order to be more environmentally responsible. Our goal is to inform the design of future sustainable technologies through an exploration of existing “green ” lifestyles. We describe the motivations, practices, and experiences of the participants. The participants had diverse motivations ranging from caring for the Earth to frugal minimalism, and most participants also evidenced a desire to be unique. Most participants actively and consciously managed their homes and their daily practices to optimize their environmental responsibility. Their efforts to be environmentally responsible typically required significant dedication of time, attention, and other resources. As this level of commitment and desire to be unique may not generalize readily to the broader population, we discuss the importance of interactive technologies that influence surrounding infrastructure and circumstances in order to facilitate environmental responsibility. Author Keywords Sustainable interaction design, green, environmental

Due to several recent trends, the domestic environment has become more homogeneous and undifferentiated. Drawing on concepts from environmental psychology, we critique these trends. We propose heterogeneity as a new framework for domestic design, and we present design sketches that illustrate how ubiquitous computing technologies can interact with the domestic environment to create a more varied and restorative environment. This work speaks to a number of core issues in ubiquitous computing, such as how the increased presence of devices impacts quality of life, the desirability or undesirability of ubiquitous temporal and spatial availability of devices, and the advantages and disadvantages of device convergence (all-in-one devices) versus device proliferation (single application devices).

We present a qualitative study of 20 American Orthodox Jewish families' use of home automation for religious purposes. These lead users offer insight into real-life, long-term experience with home automation technologies. We discuss how automation was seen by participants to contribute to spiritual experience and how participants oriented to the use of automation as a religious custom. We also discuss the relationship of home automation to family life. We draw design implications for the broader population, including surrender of control as a design resource, home technologies that support long-term goals and lifestyle choices, and respite from technology.

Spontaneous multi-party interaction -- conversation among groups of three or more participants -- is part of daily life. While automated modeling of such interactions has received increased attention in ubiquitous computing research, there is little applied research on the organization of this highly dynamic and spontaneous sociable interaction within small groups. We report here on an applied conversation analytic study of small-group sociable talk, emphasizing structural and temporal aspects that can inform computational models. In particular, we examine the mechanics of multiple simultaneous conversational floors -- how participants initiate a new floor amidst an on-going floor, and how they subsequently show their affiliation with one floor over another. We also discuss the implications of these findings for the design of "smart" multi-party applications.

Pervasive personal communication technologies offer the potential for important social benefits for individual users, but also the potential for significant social difficulties and costs. In research on face-to-face social interaction, ambiguity is often identified as an important resource for resolving social difficulties. In this paper, we discuss two design cases of personal communication systems, one based on fieldwork of a commercial system and another based on an unrealized design concept. The cases illustrate how user behavior concerning a particular social difficulty, unexplained unresponsiveness, can be influenced by technological issues that result in interactional ambiguity. The cases also highlight the need to balance the utility of ambiguity against the utility of usability and communicative clarity.

This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users' emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users' engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC EMO- TIONAL PROSODY and CALLFRIEND speech corpora.

This paper presents a mobile audio space intended for use by gelled social groups. In face-to-face interactions in such social groups, conversational floors change frequently, e.g., two participants split off to form a new conversational floor, a participant moves from one conversational floor to another, etc. To date, audio spaces have provided little support for such dynamic regroupings of participants, either requiring that the participants explicitly specify with whom they wish to talk or simply presenting all participants as though they are in a single floor. By contrast, the audio space described here monitors participant behavior to identify conversational floors as they emerge. The system dynamically modifies the audio delivered to each participant to enhance the salience of the participants with whom they are currently conversing. We report a user study of the system, focusing on conversation analytic results.

This paper presents an exploratory study of college-age students using two-way, push-to-talk cellular radios. We describe the observed and reported use of cellular radio by the participants. We discuss how the half-duplex, lightweight cellular radio communication was associated with reduced interactional commitment, which meant the cellular radios could be used for a wide range of conversation styles. One such style, intermittent conversation, is characterized by response delays. Intermittent conversation is surprising in an audio medium, since it is typically associated with textual media such as instant messaging. We present design implications of our findings.

In addition to providing information to individual visitors, electronic guidebooks have the potential to facilitate social interaction between visitors and their companions. However, many systems impede visitor interaction. By contrast, our electronic guidebook, Sotto Voce, has social interaction as a primary design goal. The system enables visitors to share audio information - specifically, they can hear each other's guidebook activity using a technologically mediated audio eavesdropping mechanism. We conducted a study of visitors using Sotto Voce while touring a historic house. The results indicate that visitors are able to use the system effectively, both as a conversational resource and as an information appliance. More surprisingly, our results suggest that the technologically mediated audio often cohered the visitors' conversation and activity to a far greater degree than audio delivered through the open air

We present an overview+detail document interface that draws on perceptual principles to help users work with documents. Central to our approach is the use of improved document overviews. Our approach also includes novel highlighting in the full representation of documents, as well as techniques to help users smoothly transition from the overview to the full representation of the document. We present a specific implementation of our design for Web browsing. We also present a qualitative user study that indicates that our perceptual design principles are effective and that users prefer our interface to traditional "find" and highlighting techniques. Our user study additionally reveals interesting tasks and strategies supported in our framework that have implications for overview+detail document interfaces in general

This paper reports findings from a study of how a guidebook was used by pairs of visitors touring a historic house. We describe how the guidebook was incorporated into their visit in four ways: shared listening, independent use, following one another, and checking in on each other. We discuss how individual and groupware features were adopted in support of different visiting experiences, and illustrate how that adoption was influenced by social relationships, the nature of the current visit, and any museum visiting strategies that the couples had. Finally, we describe how the guidebook facilitated awareness between couples, and how awareness of non-guidebook users (strangers) influenced use.

Abstract We introduce a technique for creating novel, enhanced thumbnails of Web pages. These thumbnails combine the advantages of plain thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. As expected, there was a strong effect of question category. For some questions, text summaries outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text summaries. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) had more consistent performance than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

We introduce a technique for creating novel, textually-enhanced thumbnails of Web pages. These thumbnails combine the advantages of image thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search Web pages to find several different types of information. Participants took an average of 67, 86, and 95 seconds to find the answer with enhanced thumbnails, plain thumbnails, and text summaries, respectively. We found a strong effect of question category. For some questions, text outperformed plain thumbnails, while for other questions, plain thumbnails outperformed text. Enhanced thumbnails (which combine the features of text summaries and plain thumbnails) were more consistent than either text summaries or plain thumbnails, having for all categories the best performance or performance that was statistically indistinguishable from the best.

A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems.

Digital books can significantly enhance the reading experience, providing many functions not available in printed books. In this paper we study a particular augmentation of digital books that provides readers with customized recommendations. We systematically explore the application of spreading activation over text and citation data to generate useful recommendations. Our findings reveal that for the tasks performed in our corpus, spreading activation over text is more useful than citation data. Further, fusing text and citation data via spreading activation results in the most useful recommendations. The fused spreading activation techniques outperform traditional text-based retrieval methods. Finally, we introduce a preliminary user interface for the display of recommendations from these algorithms.

We introduce a system that helps users construct interactive visualizations with constant information density. This work is an extension of the DataSplash database visulaization environment. DataSplash is a direct manipulation system in which users can construct and navigate visualizations. Objects' appearances change as users zoom closer to or further away from the visualization. Users specify graphically the point at which these changes occur.Our experience with DataSplash indicates that users find it difficult to construct visualizations that display an appropriate amount of detail. In this paper, we introduce an extension to DataSplash based on the Principle of Constant Information Density. This extension gives users feedback about the density of visualizations as they create them. We also introduce an extension that suggests improvements to existing visualizations.We have performed an informal study of user navigation in applications with and without constant information density. We suggest that designers take density into account when designing applications to avoid biasing user navigation in unexpected ways.

Database visualization is an area of growing importance as database systems become larger and more accessible. DataSplash is an easy-to-use, integrated environment for navigating, creating, and querying visual representations of data. We will demonstrate the three main components which make up the DataSplash environment: a navigation system, a direct-manipulation interface for creating and modifying visualizations, and a direct-manipulation visual query system.

The cartographic Principle of Constant Information Density suggests that the amount of information in an interactive visualization should remain constant as the user pans and zooms.In previous work, we presented a system, VIDA (Visual Information Density Adjuster), which helps users manually construct applications in which overall display density remains constant.In the context of semantic zoom systems, this approach ensures uniformity in the z dimension, but does not extend naturally to ensuring uniformity in the x and y dimensions.In this paper, we present a new approach that automatically creates displays that are uniform in the x, y, and z dimensions.In the new system, users express constraints about visual representations that should appear in the display.The system applies these constraints to subdivisions of the display such that each subdivision meets a target density value.We have implemented our technique in the DataSplash/VIDA database visualization environment.We describe our algorithm, implementation, and the advantages and disadvantages of our approach.

The lineage of a datum records its processing history. Because such information can be used to trace the source of anomalies and errors in processed data sets, it is valuable to users for a variety of applications, including the investigation of anomalies and debugging. Traditional data lineage approaches rely on metadata. However, metadata does not scale well to fine-grained lineage, especially in large data sets. For example, it is not feasible to store all of the information that is necessary to trace from a specific floating-point value in a processed data set to a particular satellite image pixel in a source data set. In this paper, we propose a novel method to support fine-grained data lineage. Rather than relying on metadata, our approach lazily computes the lineage using a limited amount of information about the processing operators and the base data. We introduce the notions of weak inversion and verification. While our system does not perfectly invert the data, it uses weak inversion and verification to provide a number of guarantees about the lineage it generates. We propose a design for the implementation of weak inversion and verification in an object-relational database management system.

The paper reports on user experience with Tioga, a DBMS centric visualization tool developed at Berkeley. Based on this experience, we have designed Tioga-2 as a direct manipulation system that is more powerful and much easier to program. A detailed design of the revised system is presented, together with an extensive example of its application.

In this article we present an algorithm which automatically extracts geopositional coordinate index terms from text to support georeferenced document indexing and retrieval. Under this algorithm, words and phrases containing geographic place names or characteristics are extracted from a text document and used as input to database functions which use spatial reasoning to approximate statistically the geoposition being referenced in the text. We conclude with a discussion of preliminary results and future work. © 1994 John Wiley & Sons, Inc.

This paper presents an examination of two distinct but complementary extensions of previous work on hot spot contention in multistage interconnection networks. The first extension focuses on the use of larger queues at the memory modules than are traditionally studied. The second extension explores a simple feedback damping scheme, which we refer to as bleeding, which allows selected processors to ignore feedback information. The impact of memory queue size, feedback threshold value, and bleeding on system performance (specifically maximum bandwidth per processor) is evaluated by analyzing the results of extensive network simulations. These results indicate that combining these approaches can significantly improve the effective bandwidth of a multistage interconnection network. 1. Introduction In order to achieve the goal of teraflop computing speeds by the end of the century, the number of processing nodes in a multiprocessor will have to increase substantially. However, as the numb...