In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI's dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. Initial operational gains hid ``intuition rust'': the gradual dulling of expert judgment. These asymptomatic effects evolved into chronic harms, such as skill atrophy and identity commoditization. Building on these findings, we offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections. The framework operationalizes sociotechnical immunity through dual-purpose mechanisms that serve institutional quality goals while building worker power to detect, contain, and recover from skill erosion, and preserve human identity. Evaluated across healthcare and software engineering, our work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with the preservation of human expertise.

Making AI explainable requires more than algorithmic transparency: it demands understanding who needs explanations and why. In our sixth CHI workshop on Human-Centered XAI (HCXAI), we shift focus to agentic AI systems. LLM-based agents foundationally challenge existing explainability paradigms. Unlike traditional AI that produces single outputs, agents plan multi-step strategies, invoke tools with real-world consequences, and coordinate with other systems; yet current XAI approaches fail to address these complexities. Users need to understand not just what an agent might do, but the cascade of actions it could trigger, the risks involved, and why responses take time. Even our expanded HCXAI frameworks struggle with these new demands. Through our workshop series, we have built a strong community making important conceptual, methodological, and technical impact. This year, we re-examine what human-centered explainable AI means in the agentic era, bringing together researchers and practitioners to shape explainability for both users and developers of these systems.

Explainable AI (XAI) is more than just "opening" the black box who opens it matters just as much, if not more, as the ways of opening it. Human-centered XAI (HCXAI) advocates that algorithmic transparency alone is not sufficient for making AI explainable. In our fifth CHI workshop on Human-Centered XAI (HCXAI), we shift our focus to new, emerging frontiers of explainability: (1) participatory approaches toward explainability in civic AI applications; (2) addressing hallucinations in LLMs using explainability benchmarks; (3) connecting HCXAI research with Responsible AI practices, algorithmic auditing, and public policy; and (4) improving representation of XAI issues from the Global South. We have built a strong community of HCXAI researchers through our workshop series whose work has made important conceptual, methodological, and technical impact on the field. In this installment, we will push the frontiers of work in HCXAI with an emphasis on operationalizing perspectives sociotechnically.

Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in which actions are chosen because of their likelihood of obtaining future rewards. However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen. We propose a technique Experiential Explanations to generate counterfactual explanations by training influence predictors along with the RL policy. Influence predictors are models that learn how different sources of reward affect the agent in different states, thus restoring information about how the policy reflects the environment. Two human evaluation studies revealed that participants presented with Experiential Explanations were better able to correctly guess what an agent would do than those presented with other standard types of explanation. Participants also found that Experiential Explanations are more understandable, satisfying, complete, useful, and accurate. Qualitative analysis provides information on the factors of Experiential Explanations that are most useful and the desired characteristics that participants seek from the explanations.

Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.

Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make the user experience seamless, hiding the seams risks disempowering users to mitigate fallouts from AI mistakes. Instead of hiding these AI imperfections, can we leverage them to help the user? While Explainable AI (XAI) has predominantly tackled algorithmic opaqueness, we propose that seamful design can foster AI explainability by revealing and leveraging sociotechnical and infrastructural mismatches. We introduce the concept of Seamful XAI by (1) conceptually transferring "seams" to the AI context and (2) developing a design process that helps stakeholders anticipate and design with seams. We explore this process with 43 AI practitioners and real end-users, using a scenario-based co-design activity informed by real-world use cases. We found that the Seamful XAI design process helped users foresee AI harms, identify underlying reasons (seams), locate them in the AI's lifecycle, learn how to leverage seamful information to improve XAI and user agency. We share empirical insights, implications, and reflections on how this process can help practitioners anticipate and craft seams in AI, how seamfulness can improve explainability, empower end-users, and facilitate Responsible AI.

Human-centered XAI (HCXAI) advocates that algorithmic transparency alone is not sufficient for making AI explainable. Explainability of AI is more than just “opening” the black box — who opens it matters just as much, if not more, as the ways of opening it. In the era of Large Language Models (LLMs), is “opening the black box” still a realistic goal for XAI? In this fourth CHI workshop on Human-centered XAI (HCXAI), we build on the maturation through the previous three installments to craft the coming-of-age story of HCXAI in the era of Large Language Models (LLMs). We aim towards actionable interventions that recognize both affordances and pitfalls of XAI. The goal of the fourth installment is to question how XAI assumptions fare in the era of LLMs and examine how human-centered perspectives can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we emphasize “operationalizing.” We seek actionable analysis frameworks, concrete design guidelines, transferable evaluation methods, and principles for accountability.

To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful effects is important. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls (EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users. EPs are different from dark patterns, which are intentionally deceptive practices. We articulate the concept of EPs by demarcating it from dark patterns and highlighting the challenges arising from uncertainties around pitfalls. We situate and operationalize the concept using a case study that showcases how, despite best intentions, unsuspecting negative effects, such as unwarranted trust in numerical explanations, can emerge. We propose proactive and preventative strategies to address EPs at three interconnected levels: research, design, and organizational. We discuss design and societal implications around reframing AI adoption, recalibrating stakeholder empowerment, and resisting the "move fast and break things" mindset.

When the initial vision of Explainable (XAI) was articulated, the most popular framing was to open the (proverbial) "black-box" of AI so that we could understand the inner workings. With the advent of Large Language Models (LLMs), the very ability to open the black-box is increasingly limited. Especially when it comes to non-technical end-users. In this paper, we challenge the assumption of "opening" the black-box in the LLM era and argue for a shift in our XAI expectations. Highlighting the epistemic blind spots of an algorithm-centered XAI view, we argue that a human-centered perspective can be a path forward. We operationalize the argument by synthesizing XAI research along three dimensions: explainability outside the black-box, explainability around the edges of the black box, and explainability that leverages infrastructural seams. We conclude with takeaways that reflexively inform XAI as a domain.

Ongoing calls from academic and civil society groups and regulatory demands for the central role of affected communities in development, evaluation, and deployment of artificial intelligence systems have created the conditions for an incipient “participatory turn” in AI. This turn encompasses a wide number of approaches — from legal requirements for consultation with civil society groups and community input in impact assessments, to methods for inclusive data labeling and co-design. However, more work remains in adapting the methods of participation to the scale of commercial AI. In this paper, we highlight the tensions between the localized engagement of community-based participatory methods, and the globalized operation of commercial AI systems. Namely, the scales of commercial AI and participatory methods tend to differ along the fault lines of (1) centralized to distributed development; (2) calculable to self-identified publics; and (3) instrumental to intrinsic perceptions of the value of public input. However, a close look at these differences in scale demonstrates that these tensions are not irresolvable but contingent. We note that beyond its reference to the size of any given system, scale serves as a measure of the infrastructural investments needed to extend a system across contexts. To scale for a more participatory AI, we argue that these same tensions become opportunities for intervention by offering case studies that illustrate how infrastructural investments have supported participation in AI design and governance. Just as scaling commercial AI has required significant investments, we argue that scaling participation accordingly will require the creation of infrastructure dedicated to the practical dimension of achieving the participatory tradition’s commitment to shifting power.

In this opinion, Upol Ehsan and Mark Riedl argue why a singular monolithic definition of explainable AI (XAI) is neither feasible nor desirable at this stage of XAI's development.

Generative Artificial Intelligence (AI) encounters limitations in efficiency and fairness within the realm of Procedural Content Generation (PCG) when human creators solely drive and bear responsibility for the generative process. Alternative setups, such as Mixed-Initiative Co-Creative (MI-CC) systems, exhibited their promise. Still, the potential of an active mixed initiative, where AI takes a role beyond following, is understudied. This work investigates the influence of the adaptive ability of an active and learning AI agent on creators' expectancy of creative responsibilities in an MI-CC setting. We built and studied a system that employs reinforcement learning (RL) methods to learn the creative responsibility preferences of a human user during online interactions. Situated in story co-creation, we develop a Multi-armed-bandit agent that learns from the human creator, updates its collaborative decision-making belief, and switches between its capabilities during an MI-CC experience. With 39 participants joining a human subject study, Our developed system's learning capabilities are well recognized compared to the non-learning ablation, corresponding to a significant increase in overall satisfaction with the MI-CC experience. These findings indicate a robust association between effective MI-CC collaborative interactions, particularly the implementation of proactive AI initiatives, and deepened understanding among all participants.

Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap-divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can reflexively provide actionable insights to improve explainability. Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap. We apply the framework to a third case in a new domain, showcasing its affordances. Finally, we discuss conceptual implications of the framework, share practical considerations in its operationalization, and offer guidance on transferring it to new contexts. By making conceptual and practical contributions to understanding the sociotechnical gap in XAI, the framework expands the XAI design space.

Explainability is an essential pillar of Responsible AI that calls for equitable and ethical Human-AI interaction. Explanations are essential to hold AI systems and their producers accountable, and can serve as a means to ensure humans' right to understand and contest AI decisions. Human-centered XAI (HCXAI) argues that there is more to making AI explainable than algorithmic transparency. Explainability of AI is more than just "opening" the black box — who opens it matters just as much, if not more, as the ways of opening it. In this third CHI workshop on Human-centered XAI (HCXAI), we build on the maturation through the first two installments to craft the coming-of-age story of HCXAI, which embodies a deeper discourse around operationalizing human-centered perspectives in XAI. We aim towards actionable interventions that recognize both affordances and potential pitfalls of XAI. The goal of the third installment is to go beyond the black box and examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we emphasize "operationalizing." Within our research agenda for XAI, we seek actionable analysis frameworks, concrete design guidelines, transferable evaluation methods, and principles for accountability.

Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural generative systems has emphasized one particular means of interacting with AI systems: the user provides a specification, usually in the form of prompts, and the AI system generates the content. However, there are other configurations of human and AI coordination, such as co-creativity (CC) in which both human and AI systems can contribute to content creation, and mixed-initiative (MI) in which both human and AI systems can initiate content changes. In this paper, we define a hypothetical human-AI configuration design space consisting of different means for humans and AI systems to communicate creative intent to each other. We conduct a human participant study with 185 participants to understand how users want to interact with differently configured MI-CC systems. We find out that MI-CC systems with more extensive coverage of the design space are rated higher or on par on a variety of creative and goal-completion metrics, demonstrating that wider coverage of the design space can improve user experience and achievement when using the system; Preference varies greatly between expertise groups, suggesting the development of adaptive, personalized MI-CC systems; Participants identified new design space dimensions including scrutability -- the ability to poke and prod at models -- and explainability.

Explainability of AI systems is crucial to hold them accountable because they are increasingly becoming consequential in our lives by powering high-stakes decisions in domains like healthcare and law. When it comes to Explainable AI (XAI), understanding who interacts with the black-box of AI is just as important as “opening” it, if not more. Yet the discourse of XAI has been predominantly centered around the black-box, suffering from deficiencies in meeting user needs and exacerbating issues of algorithmic opacity. To address these issues, researchers have called for human-centered approaches to XAI. In this second CHI workshop on Human-centered XAI (HCXAI), we build on the success of the first installment from CHI 2021 to expand the conversation around XAI. We chart the domain and shape the HCXAI discourse with reflective discussions from diverse stakeholders. The goal of the second installment is to go beyond the black box and examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we put an emphasis on “operationalizing”, aiming to produce actionable frameworks, transferable evaluation methods, concrete design guidelines, and articulate a coordinated research agenda for XAI.

When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed does not imply its FATE-related issues cease to exist. In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences. We operationalize this concept and its implications through the 2020 events surrounding the algorithmic grading of the General Certificate of Education (GCE) Advanced (A) Level exams, an internationally recognized UK-based high school diploma exam administered in over 160 countries. While the algorithmic standardization was ultimately removed due to global protests, we show how the removal failed to undo the algorithmic imprint on the sociotechnical infrastructures that shape students', teachers', and parents' lives. These events provide a rare chance to analyze the state of the world both with and without algorithmic mediation. We situate our case study in Bangladesh to illustrate how algorithms made in the Global North disproportionately impact stakeholders in the Global South. Chronicling more than a year-long community engagement consisting of 47 interviews, we present the first coherent timeline of "what" happened in Bangladesh, contextualizing "why" and "how" they happened through the lenses of the algorithmic imprint and situated algorithmic fairness. Analyzing these events, we highlight how the contours of the algorithmic imprints can be inferred at the infrastructural, social, and individual levels. We share conceptual and practical implications around how imprint-awareness can (a) broaden the boundaries of how we think about algorithmic impact, (b) inform how we design algorithms, and (c) guide us in AI governance. The imprint-aware design mindset can make the algorithmic development process more human-centered and sociotechnically-informed.

Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textit{layers of explanation} (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textit{sensitivity} (emotional responsiveness) of features and the level of \textit{stakes} (decision's consequence) in a domain to determine whether different types of explanations are \textit{appropriate} in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.

The realm of Artificial Intelligence (AI)’s impact on our lives is far reaching – with AI systems proliferating high-stakes domains such as healthcare, finance, mobility, law, etc., these systems must be able to explain their decision to diverse end-users comprehensibly. Yet the discourse of Explainable AI (XAI) has been predominantly focused on algorithm-centered approaches, suffering from gaps in meeting user needs and exacerbating issues of algorithmic opacity. To address these issues, researchers have called for human-centered approaches to XAI. There is a need to chart the domain and shape the discourse of XAI with reflective discussions from diverse stakeholders. The goal of this workshop is to examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we put an emphasis on “operationalizing”, aiming to produce actionable frameworks, transferable evaluation methods, concrete design guidelines, and articulate a coordinated research agenda for XAI.

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.