We have instituted strong safeguards that greatly reduce the likelihood that Fable is misused for tasks related to cybersecurity (among others). In fact, our safeguards are so strong that many users have complained that they are overly broad.

In supervised and semi-supervised machine learning (ML) pipelines, labeled data is a vital component of training and validating models [46].

Executives believe users will increasingly interact with a single AI assistant rather than a collection of separate applications.

Each person on the team gets their own slice of the brain, scoped by login. When you query, you only see what you're allowed to see — never another person's notes, never another team's data. We fuzz-tested this across every way you can read the brain (search, list, lookup, multi-source reads) and got zero leaks.

He wants to end the days of launching apps, pointing, clicking, and typing.

Match isolation strength to the user's capacity for oversight. A developer who can read bash and a knowledge worker who can't are not running the same threat model.

The more approvals a user sees, the less attention they pay to each, becoming over time much less diligent in their supervision.

Opus 4.8 defaults to high effort, which we judge to be the best overall balance of quality and user experience.

It claims 8 million global users and 100 trillion tokens processed per month

Engagements like this will run across mid-sized companies across industries, each shaped by the people closest to the work.

The tool also provided reflective value. Participants reported that it helped articulate what matters to them and why. Beyond research settings, individuals can use the framework to audit which dimensions drive their own sense of ownership, select AI tools that respect those priorities (e.g., suggestion-only assistance for high-Control creators), and mediate collaboration by visualizing divergent ownership profiles when teammates disagree about contribution and credit.

In multitasking, tasks compete for limited sensory, motor, and central (cognitive) capacities

Visual objects that are unique in their visual primitives attract user's attention.

Interaction is a concept that is fundamental in HCI and specific to this field [357]. Intuitively, it refers to the reciprocal influence between people and an interactive system that takes place through the user interface.

Users continuously adapt their social behavior to compensate for the lack of social cues in computer-mediated communication

Users' performance in providing input to a computer is limited by a speed–accuracy trade-off

A key technical construct in HCI is the user interface. It refers to the parts of an interactive system that the user comes into contact with or that in other ways shape the user's perception of the system.

In HCI, evaluation refers to the application of some systematic methodology to attribute human-related values to an artifact, prototype, system, or process.

A special part of a computing system is the user interface. It is the part that the user can see and utilize to control the computer.

Programmability lends computers their power as tools.

We built AI into our editor's foundation instead of bolting it on top.

All of these requests can add up to minutes that users spend waiting for Codex to complete complex tasks.

This richly layered collage poster features art, science, history, design, and global culture surrounding the phrase “Create Everything at Once,” blending planets, anatomy sketches, maps, architecture, symbols, crystals, and mixed media imagery into a vibrant creative mosaic.

Greater precision and control

This poster-style image introduces “ChatGPT Images 2.0” with a bold editorial layout, blocks of explanatory text, and geometric shapes in red, black, blue, and yellow.

The company reportedly has about 18 million verified users thus far, but many of them are people in developing nations who signed up because of the promise of Worldcoin, a cryptocurrency that has seemingly fallen out of World’s plans.

Sam Altman is banking on people being willing to surrender scans of their eyes in order to authenticate themselves

You can open the Threads Sidebar from the icon in the bottom left, or via the keybinding option-cmd-j on macOS and ctrl-option-j on Linux and Windows.

Ask ten different programmers how they use AI, and you can get ten different answers.

In early April, we shared that more than 3 million developers were using Codex every week. Just two weeks later, that number has grown to more than 4 million.

In our internal evals and testing, medium effort achieved slightly lower intelligence with significantly less latency for the majority of tasks.

We reverted this change on April 7 after users told us they'd prefer to default to higher intelligence and opt into lower effort for simple tasks.

over 100,000 customers now run Claude on Amazon Bedrock

over 100,000 customers now run Claude on Amazon Bedrock

placing constraints upon them not only helps users and services build trust in them, but it also helps people more easily conceptualise what they do.

WordPress wasn't the best-designed CMS — Drupal was. It wasn't the most performant or reliable either. But that didn't stop WordPress from becoming the de-facto standard for building websites and the largest ecosystem — all because it was the easiest to use.

Agent harnesses are much more like WordPress than they are like Apache, simply because people want to have their own agents — just like everyone wanted their own website in the early 2000s.

For Max-plan users hitting rate limits instead of dollars: your 5-hour window ends sooner by roughly the same ratio on English-heavy work. A session that ran the full window on 4.6 probably doesn't on 4.7.

Claude keeps its responses focused and concise so as to avoid potentially overwhelming the user with overly-long responses

the person typically wants Claude to make a reasonable attempt now, not to be interviewed first

多年积累的对话、定制 Agent、项目记忆、MCP 配置、Skill 库——一次风控就可能全部失联。

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Reasoning-oriented models like OpenAI's o1 and GPT-5 show measurable gains over standard models—not only in logic and mathematics but also with interpreting user intent.

Reasoning-oriented models like OpenAI's o1 and GPT-5 show measurable gains over standard models—not only in logic and mathematics but also with interpreting user intent.

Ollama stores downloaded models using hashed filenames in its own format. If you've been pulling models through Ollama for months, you can't just point llama.cpp or LM Studio at those files without extra work.

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ChatGPT has 900 million weekly users, which means employees already know how to work with it. For enterprises, that reduces rollout friction and accelerates the point where every employee can delegate tedious tasks.

ChatGPT has 900 million weekly users, which means employees already know how to work with it. For enterprises, that reduces rollout friction and accelerates the point where every employee can delegate tedious tasks and take on more ambitious projects.

一个本该由「应用商店」承载的分发体系，现在却被内容平台接管了。

Website: add animated workflow demos

Switching between windows on your desktop can be clunky and slow. Now, you can bring up Gemini from anywhere on your Mac with a quick shortcut (Option + Space)

Behaviors also vary strongly with levels of reasoning and users' inferred socio-economic status

recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%)

Our results highlight some of the hidden risks to users that can emerge when companies begin to subtly incentivize advertisements in chatbots.

We find that a majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations, including recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%).

Memory enables sticky, personalized agentic experiences and creates strong lock-in.

six third-party providers are ready to go. Pick one with 'hermes memory setup'

The share of U.S. adults who used Claude in the past week rose from 3.0% in early March to 4.3% in early April 2026

Theories of rationality have increased our understanding of how users fail to be optimal.

MDP is a formalism that originates from studies of sequential decision-making in artificial intelligence and operations research. Instead of the choice between n actions, MDP deals with environments where rewards are delayed (or distal). This requires an ability to plan actions as part of sequences instead of one-shot choices.

Information scent refers to a user's intuition that a cue in the interface represents the information needed. It is an estimation of relevance based on a proximal cue.

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested.

Information foraging refers to information-seeking activities such as navigating, exploring, comparing, searching, or manipulating information contents in an information space.

A payoff refers to the benefits that are left after the costs have been subtracted.

To state that a user's choice is rational means that it is selected with the expectation that it yields the highest utility out of the available options.

Rational analysis is a theory of rational behavior proposed by Anderson and Schooler [21]. It examines the distribution of rewards in the environment to explain how users adapt their behavior. According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced.

They share a focus on the emergence of interactive behavior; in other words, they predict how users choose to behave in certain given circumstances.

Utility refers to the agent's consideration of positive and negative rewards when deciding how to act.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

The term satisficing is used to describe how users tend to behave when facing a complex decision-making problem. It refers to settling on a satisfactory but not optimal solution in the normative sense.

The author wants to augment the formula to explain the meaning of the terms on either side of the arrow—first

they often benefit from being augmented with descriptive elements, such as labels describing the meaning of an expression or colors linking an identifier to its description in the text.

In this walkthrough, the author is trying to add labels to the formula V(s_t) ← R_t to describe the meaning of its terms in an article they are writing.

Our design was motivated by two major goals for notation authoring. These goals followed from recent studies of notation augmentation [30, 71] and conversations with scientists who had experience writing notation in instructional materials and research communications (4 professors, 2 graduate students, R1–6).

We define the key projections as markup (in this case, LaTeX), an annotatable render, and a structure hierarchy view. Augmentations are made easy to invoke, and projections are kept synchronized and co-present so that authors can shift between representations as is expedient to them.

the challenge of using these tools is that annotations are unmoored from the structure of the formula and must be redone whenever the formula changes. Authors must perform precision positioning and sizing operations that could be inferred from the coordinates of the augmented expressions.

these markup languages can require cumbersome and error-prone editing, arising from the intermixing of annotation markup with the underlying formula. Participants in a study by Wu et al. [71] identified difficulty with debugging nested braces and locating markup to edit.

FreeForm, a projectional editor wherein authors can augment formulas—with color, labels, spacing, and more—across multiple synchronized representations. Augmentations are created graphically using direct selections and compact menus. Those augmentations propagate to LaTeX markup, which can itself be edited and easily exported.

Authors of typeset formulas augment those formulas to make them easier to understand.

Ply offers this LLM-supported program decomposition supported by visualization and parameterization UIs, permitting users to use interactions beyond chat to compose their programs incrementally.

designing complex behavior can be a difficult programming task, and program representations in end-user programming tools may not be well-suited for heavy programs.

users can develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals.

It encourages program decomposition into "layer" abstractions, It automatically creates visualizations of event payloads at layer boundaries to help users understand layer behavior without having to read the underlying generated code, and It constructs ad hoc parametrization interfaces that allow users to configure important dimensions of the behavior of each layer without having to re-author it.

Ply maintains the simplicity of a straightforward connection between a trigger and action but provides a structure within which users can enlist an LLM to specify the behavior of each trigger and action.

However, such LLM-authored code, especially when implementing nontrivial logic, can be difficult to specify, understand or debug. Users need appropriate tools and handles to understand and make changes to the computation that is being performed in such code.

Trigger-action programming offers an elegant interface to construct simple programs that result in customized behavior for software or devices.

Trigger-action programming has been a success in end-user programming. Traditionally, the simplicity of links between triggers and actions limits the expressivity of such systems. LLM-based code generation promises to enable users to specify more complex behavior in natural language. However, users need appropriate ways to understand and control this added expressive power.

by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants. In particular, we identified an additional phase that is prominent among the participants, intention to learn, but did not appear in prior models. Then, we identified three new factors that significantly influence their technology acceptance but which are, again, not represented in the existing models: self-efficacy, conversion readiness, and peer support.

Our preliminary results indicate that there is an additional phase, the intention to learn, and three relating factors, self-efficacy, conversion readiness, and peer support, that significantly influence the acceptance of mobile technologies among the participants, but are not represented in the existing models. With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies.

Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

We identified three distinct factors that influence older adults' technology acceptance behaviors, particularly the intention to learn phase, that are not represented in prior models: self-efficacy, conversion readiness, and peer support.

人对错误的容忍度很低，一个错误推送比少记几件事更容易让用户觉得产品不好。

以 Enter 键为锚点，捕捉用户每一次表达意图的瞬间。

Cai et al. [117] interviewed 21 pathologists who used a deep neural network to aid in thediagnosis of prostate cancer. The interviews showed that pathologists needed to learn moreabout the network’s strengths and limitations to use it effectively. They also wanted to knowthe design objective of the network and the kind of data on which it was trained.

Roy et al. [715] explored what happens when users doing a task with a simulated crane needto choose between further using automation and manually continuing the task. The authorsshowed that the decision of whether to use automation is affected by the users’ perceptionof its accuracy as well as how easy it is to do the task themselves.

The performance of the system must be reliable and controllable. Its behavior should be safe, and the way it is designed and used should be ethical [768]. Users need to trust the system's decisions and ability. It should be made clear to the user what it can and cannot do.

when setting up a new Macbook it presents FileVault as an optional checkbox and I can certainly tell you that there are many people (including my younger self and my family member with the Intel Mac) who do not know what it is and choose to disable it instead.

Teams at companies like Notion, Ramp, Braintrust, and Wasmer are already using Codex to accelerate their engineering workflows.

We propose that cognitive engagement may be a useful construct in conceptualizing human engagement with AI and can help to distinguish between passive engagement, when individuals simply follow AI recommendations, and deeper forms of engagement, when they critically examine these recommendations and compare them with their own knowledge and judgement.

An outcome of deeper cognitive engagement would be an ability to reject information that is inconsistent with individuals' own knowledge and beliefs, and to adjust their own knowledge to incorporate new information.

Given continuous concerns regarding the reliability and trustworthiness of AI, human critical engagement may be a necessary component of successful human-AI interaction, particularly in domains with a high cost of errors, such as health and medicine.

In many areas of human enterprise, individuals increasingly rely on Artificial Intelligence (AI) to inform their decisions and choices.

How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning?

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process.

We also identified the factors that are critical to older adults but did not appear in the existing models. Finally, we applied the existing vocabulary to our model to comply with the conventional terms in the field.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Employing the grounded theory method [33], we allowed recurring themes and concepts in relation to technology acceptance behaviors to arise from the data itself.

We inductively analyzed the first-round interview data using thematic analysis based on a grounded theory approach [33]. Grounded theory methods build theory iteratively from the data, using rigorous coding practices. Initial open codes are primarily descriptive. These may be combined into more sophisticated related sets of descriptors, in which each set is referred to as an axial code. Subsequently, axial codes are combined into more theoretically powerful code complexes, called selective codes. Our approach included a process of open coding, axial coding, and selective coding.

With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies.

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over.

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Although many researchers have sought to understand and predict technology acceptance behavior, there has been relatively less effort to build a theoretical model for older adults, with one exception (STAM).

Extending the original TAM and consolidating the constructs of several other existing models, Venkatesh et al. proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) [37].

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

Our goal is to modulate the saliency of words in the original text so that users can easily bypass certain words during skimming while maintaining an uninterrupted reading flow.

Be resilient to AI errors by enabling the reader to (a) notice, (b) have enough context to judge, and (c) easily recover from, automated decisions they disagree with.

Support skimming without interrupting flow. The system should improve skimming of text while minimizing the impact on the user's natural reading flow. In particular, as much as possible, it should avoid presenting users with salient text that is unparsable as a coherent thought, i.e., the system should present a complete sentence rather than a phrase or sentence fragment.

Support reading at multiple levels of detail. The system should help users navigate the full complexity of a text, shifting focus seamlessly between different levels of semantic coverage, or granularity, from the big picture to the fine details.

Integrate seamlessly into existing reading experiences. The system should complement and not interfere with the existing digital reading workflow that people are used to. It should provide all the functionalities in the same view, minimizing the overhead of mode and context switching.

Remain faithful to the original text. The system should not automatically reword or add new words or phrases to the original text. It should preserve the original text, while rendering it in a way that aids reading, skimming, or information retrieval.

We aspired to design a text rendering interface that alleviates some of the cognitive demands of reading, skimming, or performing information retrieval on natural language documents—particularly those with long, complicated sentences—without compromising the integrity of the original content.

Established theories of human cognition describe how exposure to variation and consistency within prescribed structures can help people more robustly form mental models of a phenomenon, e.g., how an LLM behaves. Specifically, in line with Variation Theory [35], the features we instantiate identify patterns of consistency (Figure 1d, "Exact Matches"), variation (Figure 1c, "Unique Words"), or both (Figures 1a, 1b, "Positional Diction Clustering (PDC)"—a novel algorithm we introduce in this paper). In line with Analogical Learning Theory [13], PDC highlights analogous text across LLM responses, i.e., positionally consistent and similar in diction, such that users can see emergent relationships.

users may want to select the best option from among many, compose their own response through bricolage, consider many ideas during ideation, audit a model by looking at the variety of possible responses, or compare the functionality of different models or prompts.

participants seemed to prefer engaging with the text directly without having to articulate a lens with which to look at the corpus, since their analysis goal may be initially under-defined.

we want to decorate text to show pre-computed relationships, such as string matches or analogous sentences, across responses. In this way, we help users shift cognitive bandwidth away from identifying overlapping or \

In our formative study, we found that automated analysis rarely captured what the participants were looking for when inspecting LLM responses.

We want to support a wide range of tasks that involve sensemaking. For example, we want to support the detection of similarities and differences between individual responses as well as groups of responses, and support the detection of

We aim to make 10s to 100s of LLM responses cognitively comfortable to peruse, as this was the scale we found to be most relavent in our formative study.

dialogue, as a form of interaction, is not limited to speech and language even though this is often our first interpretation of the term "dialogue."... the concepts of dialogue are applicable across modalities.

An FSM is a model of discrete computation applicable to dialogues. In computer science, an FSM is a special case of a Turing machine that reads but does not write on the tape.

Formal models of computation are suitable for describing discrete, moded dialogues. A mode refers to the variation in the interpretation of a user's input according to an internal state. In a modeless dialogue, all inputs are possible in all states and their interpretation is always the same.

One thing that is missing is an account of how beliefs about the computer are formed and updated and how they drive action specification. The current understanding is that users form internal models that predict how their actions produce perceived outputs, and they learn to minimize prediction errors.

both the computer and the human participate in establishing a shared context. The computer does not simply receive a message; it also communicates the effects of that message.

Robustness refers to the communication partners' ability to achieve shared understanding even in light of misunderstandings and other unanticipated troubles.

Communication repair refers to the "work of restoring shared understanding" when conversational partners misunderstand each other.

Mixed-initiative interaction is the idea of organizing interaction in dialogue where both the computer and the human can take initiative.

Dialogue can be understood as computation, goal-directed action, communication, or embodied action. Each perspective provides specific methods for the analysis and design of dialogue.

Dialogue interaction includes speech-based and graphical interactions.

The core elements of dialogue are communication turns, the communication context, and turn interpretation.

Dialogue is about the organization of communication as a series of turns between communication partners.

The key idea in the dialogue view of interaction is the organization of communication as a series of turns. Dialogue evolves through communication turns between two or more partners. In one turn, an appropriate communication act is made by one partner based on the communication context. The act aims to get the other partner to do or understand something. This understanding then forms the context within which the other partner takes their turn.

Interaction may be viewed as a dialogue, that is, a conversation that occurs between two partners in a context for some purpose.

TAM posits that the intention to adopt a particular technology is driven by two kinds of perceptions: (1) how easy it is to use a system and (2) how useful it will be to use it [180]. Furthermore, the perceived ease of use affects the perceived usefulness: If technology is hard to use, it is less useful.

it is perfectly possible to have a program which is structured, modular, readable, flexible, self-documenting, maintainable, which performs its specified function, and which is a source of constant frustration and irritation to its users.

Text entry can also be seen as a task where different subtasks are shared between the human and the computer (Chapter 20).

One example is autocorrect, which automatically corrects typing errors while the user is typing. Another example is the use of word predictions, which allow the user to select a word from a set of word suggestions instead of typing out the word in full.

For example, text entry methods such as eye typing are designed to allow nonspeaking users with motor disabilities to enter text using their eye movements only.

Text entry is also a good example of tool use (Chapter 19). A text entry method is a tool that allows the user to communicate with someone or something, typically other people or a service, using asynchronous text messages and longer documents.

In teleological determination, goals or purposes determine interaction in some way.

What happens in interaction is mutually determined by the human and the computer. In other words, what happens in interaction cannot be attributed solely to the human or the computer—the two must be considered together.

"Average movement time can be predicted as linear regression to the index of difficulty."

"The difficulty of selecting a target is proportional to its distance and inversely proportional to its width (index of difficulty)."

"If the user tries to increase speed, accuracy will be compromised, and vice versa: An increase in accuracy reduces speed."

For example, they can talk about information, difficulty, working memory, and so on.

A proposition is a claim about the world.

Interaction also occurs in different contexts, including work, leisure, and in-between contexts such as commuting.

Interaction is a dynamic phenomenon that unfolds over time as users and computers influence each other.

It has been used to describe individuals, groups, and communities using computers.

Interaction is a core notion in HCI and refers to the mutual influence between people and computers.

Pressing a button takes about a hundred milliseconds; adopting an information system in a large organization can easily take months.

We have used it to discuss various applications, from a user typing on a smartphone to a team of information workers communicating via email.

Such points about the origins of data and the processes of their collection are a key factor in civic text visualization. Indeed, a shift to emphasizing paradata can help draw attention to the representativeness of data.

On the other side of this spectrum, at the detail level, articulating nuanced information present in raw text data can enable civic leaders to peruse and sublimate critical insights.

In contrast, we could consider designing explicitly for multiple users. Doing so requires more than designing for different levels of expertise (see the following subsection for more on expertise) or designing for collaborative use, though both those things may be valuable in their own right. Rather, this dimension encourages accounting for the different types of relationalities that users may have with a system [cf. BB17].

Civic text visualizations similarly designed to foreground interpretation could help make clearer who is making these interpretive decisions, thereby highlighting the lack of neutrality and objectivity in data [DK20].

work on visualization evaluation [SP06; IZCC08; LBI*12] has emphasized the importance of close attention to the various contexts in which a visualization will be applied.

It is informative to contrast this analytic emphasis with other evolving discourses in information visualization. The prior work reviewed above illustrates a few alternative orientations, including rhetoric [HD11], feminism [DK16; DK20], ethics [Cor19], and others [DFCC13; VW08].

For instance, CommunityPulse provides a scaffolding for multifaceted public input analysis using visualizations [JHSM21], and MultiConVis enables multilevel exploration and analysis of threaded conversations [HC16b].

For example, CommunityPulse [JHSM21] uses common, simple visualizations and iconography, such as bar charts and emojis, to provide overviews of people's emotions towards civic agendas and ideas. Similarly, ConsiderIt [KMF*12b] uses bar charts to visualize people's stance towards ballot measures.

For instance, visual analytic systems such as MultiConVis [HC16b] use multiple connected views to enable analysts to filter and explore text data at multiple levels.

Tools such as ConsiderIt [KMF*12b] or Opinion Space [FBRG10] are designed specifically for the public. In contrast, tools such as CommunityPulse [JHSM21] or CommunityClick [JKW*21] are focused more on supporting community leaders and decision makers.

For example, MultiConVis [HC16b] makes prescriptive statements not only as to the sentimental valence of individual conversations but also as to the topics that each conversation is about. Similarly, ConsiderIt [KMF*12b] asks participants to place individual statements as either supporting or opposing a given ballot proposition.

Consider how systems such as MutiConVis [HC16b] and CommunityClick [JKW*21] provide visual representations to help the viewer understand the structure and content of conversations.

tools such as ConsiderIt [KMF*12b] and CommunityPulse [JHSM21] prominently feature specific comments from members of the public (i.e., the data).

Improving the public input process has become an important goal in the field of digital civics [MNC*19; VCL*16; OW15]. To that end, researchers and practitioners have developed a variety of systems for, e.g., sharing public opinions [FBRG10], building consensus [KMF*12a; ZNB15], summarizing public input [19], or identifying people's priorities, reflections, and hidden insights [JHSM21].

Previous work has introduced several online engagement platforms to enable the public to asynchronously provide their comments, ideas, and feedback around civic issues [19; 20b; MJN*18]. These engagement tools have used micro-tasks [MJN*18], visualizations [19], and forum-like discussions [20b] to engage disconnected and disenfranchised populations [MNC*19]. Others have proposed technologies to promote in-person engagement of reticent participants during town halls [JKW*21] and public meetings [LLS] using clicker-like devices.

Despite their central importance in the civic engagement process, members of the general public are not necessarily involved in the analysis process. Hence, they are often left out of the loop when designing civic text visualizations—their requirements, aptitudes, knowledge, etc. are not given central consideration. Integrating participatory approaches in civic text visualization could pave the way not only for more inclusive analysis but also for leveraging the general public's knowledge to gather richer insights.

social dynamics, such as shyness and tendency to avoid confrontation with dominant personalities can also hinder opinion sharing in town halls by favoring privileged individuals who are comfortable or trained to take part in contentious public discussions [27, 127].

town halls inadvertently cater to a small number of privileged individuals, and silent participants often become disengaged despite physically attending the meetings [61]. Due to the lack of inclusivity, the outcome of such meetings often tends to feel unjust and opaque for the general public [39, 54].

designing communitysourcing technologies to include marginalized opinions and amplify participation alone may not be enough to solve inequality of sharing opinions in the civic domain [26, 126]. Despite the success of previous works [25, 53, 90], technology is rarely integrated with existing manual practices and follow-ups of engagements between government officials and community members are seldom propagated to the community.

Marginalization can be broadly defined as the exclusion of a population from mainstream social, economic, cultural, or political life [58], which still stands as a barrier to inclusive participation in the civic domain [48, 94]. Researchers in HCI and CSCW have explored various communitysourcing approaches to include marginalized populations in community activities, proceedings, and designs [48, 53, 81, 93, 132].

To increase broader civic participation, researchers in HCI have proposed both online [4, 5, 7, 81, 93] and face-to-face [21, 80, 91, 125] technological interventions that use the communitysourcing approach.

Prior investigations by Bryan [29] and Gastil [56] showed a steady decline in civic participation in town halls due to the growing disconnect between local government and community members and the decline in social capital [43, 111, 113]. Despite the introduction of online methods to increase public engagement in the last decade [4, 5, 7, 37, 81, 93], government officials continue to prefer face-to-face meetings to engage the community in the decision-making process [32, 52, 94].

To reengage disconnected, reticent, or disenfranchised community members, researchers in HCI and digital civics have offered novel strategies and technological interventions to increase engagement [60, 62, 94, 107, 130].

Bryan [29] and Gastil [56] investigated the state of town halls and demonstrated a steady decline in civic participation due to the growing disconnect between local government and the community.

Traditional community consultation methods, such as town halls, public forums, and workshops are the modus operandi for public engagement [52, 94]. For fair and impartial civic decision-making, the inclusivity of community members' feedback is paramount [60, 94, 126]. However, traditional methods rarely provide opportunities for inclusive public participation [30, 87, 95].

Murphy used such systems to promote democracy and community partnerships [103]. Similarly, Boulianne et al. deployed clicker devices in contentious public discussions about climate change to gauge public opinions [25]. Bergstrom et al. used a single button device where the attendees anonymously voted (agree/disagree) on issues during the meeting. They showed that back-channel voting helped underrepresented users get more involved in the meeting [22].

As evidenced by numerous studies on statistical cognition (Kline, 2004; Beyth-Marom et al, 2008), even trained scientists have a hard time interpreting p-values, which frequently leads to misleading or incorrect conclusions.

few researchers can resist the temptation to conclude that there is no effect, a common fallacy called "accepting the null" which had frequently led to misleading or wrong scientific conclusions (Dienes, 2014, p.1).

Again, p is the probability of seeing results as extreme (or more extreme) as those actually observed if the null hypothesis were true. So p is computed under the assumption that the null hypothesis is true. Yet it is common for researchers, teachers and even textbooks to think of p as the probability of the null hypothesis being true (or equivalently, of the results being due to chance), an error called the "fallacy of the transposed conditional" (Haller and Krauss, 2002; Cohen, 1994, p.999).

Many researchers fail to appreciate that p-values are unreliable and vary widely across replications.

Providing non-misleading interpretations of figures with confidence intervals requires judgment, and no mechanical decision procedure can carry out this job better than a thoughtful investigator.

Estimation seems much more likely to promote clear statistical thinking.

This assessment raises two issues. First, it is arbitrary. If 10 of the 15 CIs included the predicted values, would the results also support the theory, or instead refute it? If one instead used 99% CIs, would positive results for 12 of the 15 predictions be enough to support the theory? This arbitrariness arises because CIs offer no principled method for generating an inference regarding the theory.

two out of three necessary conditions for testing theory are missing.