Artificial intelligence (AI) has the potential to bring significant benefits to highly regulated industries such as healthcare or banking. Adoption, however, remains low. AI’s entry into complex socio-techno-legal systems raises issues of transparency, specifically for regulators. However, the perspective of supervisors, regulators who monitor compliance with applicable financial regulations, has rarely been studied. This paper focuses on understanding the needs of supervisors in anti-money laundering (AML) to better inform the design of AI justifications and explanations in highly regulated fields. Through scenario-based workshops with 13 supervisors and 6 banking professionals, we outline the auditing practices and socio-technical context of the supervisor. By combining the workshops’ insights with an analysis of compliance requirements, we identify the AML obligations that conflict with AI opacity. We then formulate seven needs that supervisors have for model justifiability. We discuss the role of explanations as reliable evidence on which to base justifications.

Webstrates are web substrates, a practical realization of shareable dynamic media under which distributability, shareability, and malleability are fundamental software principles. Webstrates blur the distinction between application and document in a way that enables users to share, repurpose, and refit software across a variety of domains, but its reliance on a central server constrains its use; it is at odds with personal and collective control of data; and limits applications to the web. We extend the fundamental principles to include interoperability and sovereignty over data and propose MyWebstrates, an implementation of Webstrates on top of a new, lower-level substrate for synchronization built around local-first software principles. MyWebstrates registers itself in the user’s browser and function as a piece of local software that can selectively synchronise data over sync servers or peer-to-peer connections. We show how MyWebstrates extends Webstrates to enable offline collaborative use, interoperate between Webstrates on non-web technologies such as Unity, and maintain personal and collective sovereignty over data. We demonstrate how this enables new types of applications of Webstrates and discuss limitations of this approach and new challenges that it reveals.

Explainability (XAI) has matured in recent years to provide more human-centered explanations of AI-based decision systems. While static explanations remain predominant, interactive XAI has gathered momentum to support the human cognitive process of explaining. However, the evidence regarding the benefits of interactive explanations is unclear. In this paper, we map existing findings by conducting a detailed scoping review of 48 empirical studies in which interactive explanations are evaluated with human users. We also create a classification of interactive techniques specific to XAI and group the resulting categories according to their role in the cognitive process of explanation: "selective", "mutable" or "dialogic". We identify the effects of interactivity on several user-based metrics. We find that interactive explanations improve perceived usefulness and performance of the human+AI team but take longer. We highlight conflicting results regarding cognitive load and overconfidence. Lastly, we describe underexplored areas including measuring curiosity or learning or perturbing outcomes.

Computational media describes a vision of software, which, in contrast to application-centric software, is (1) malleable, so users can modify existing functionality, (2) computable, so users can run custom code, (3) distributable, so users can open documents across different devices, and (4) shareable, so users can easily share and collaborate on documents. Over the last ten years, the Webstrates and Codestrates projects aimed at realizing this vision of computational media. Webstrates is a server application that synchronizes the DOM of websites. Codestrates builds on top of Webstrates and adds an authoring environment, which blurs the use and development of applications. Grounded in a chronology of the development of Webstrates and Codestrates, we present eight tensions that we needed to balance during their development. We use these tensions as an analytical lens in three case studies and a game challenge in which participants created games using Codestrates. We discuss the results of the game challenge based on these tensions and present key takeaways for six of them. Finally, we present six lessons learned from our endeavor to realize the vision of computational media, demonstrating the balancing act of weighing the vision against the pragmatics of implementing a working system.

The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to complex AI systems. Although it is usually considered an essentially technical field, effort has been made recently to better understand users' human explanation methods and cognitive constraints. Despite these advances, the community lacks a general vision of what and how cognitive biases affect explainability systems. To address this gap, we present a heuristic map which matches human cognitive biases with explainability techniques from the XAI literature, structured around XAI-aided decision-making. We identify four main ways cognitive biases affect or are affected by XAI systems: 1) cognitive biases affect how XAI methods are designed, 2) they can distort how XAI techniques are evaluated in user studies, 3) some cognitive biases can be successfully mitigated by XAI techniques, and, on the contrary, 4) some cognitive biases can be exacerbated by XAI techniques. We construct this heuristic map through the systematic review of 37 papers-drawn from a corpus of 285-that reveal cognitive biases in XAI systems, including the explainability method and the user and task types in which they arise. We use the findings from our review to structure directions for future XAI systems to better align with people's cognitive processes.

Affinity diagramming is widely applied to analyze qualitative data such as interview transcripts. It involves multiple analytic processes and is often performed collaboratively. Drawing on interviews with three practitioners and upon our own experience, we show how practitioners combine multiple analytic processes and adopt different artifacts to help them analyze their data. Current tools, however, fail to adequately support mixing analytic processes, devices, and collaboration styles. We present a vision and prototype ADQDA, a cross-device, collaborative affinity diagramming tool for qualitative data analysis, implemented using distributed web technologies. We show how this approach enables analysts to appropriate available pertinent digital devices as they fluidly migrate between analytic phases or adopt different methods and representations, all while preserving consistent analysis artifacts. We validate this approach through a set of application scenarios that explore how it enables new ways of analyzing qualitative data that better align with identified analytic practices.

Data workers are people who perform data analysis activities as a part of their daily work but do not formally identify as data scientists. They come from various domains and often need to explore diverse sets of hypotheses and theories, a variety of data sources, algorithms, methods, tools, and visual designs. Taken together, we call these alternatives. To better understand and characterize the role of alternatives in their analyses, we conducted semi-structured interviews with 12 data workers with different types of expertise. We conducted four types of analyses to understand 1) why data workers explore alternatives; 2) the different notions of alternatives and how they fit into the sensemaking process; 3) the high-level processes around alternatives; and 4) their strategies to generate, explore, and manage those alternatives. We find that participants' diverse levels of domain and computational expertise, experience with different tools, and collaboration within their broader context play an important role in how they explore these alternatives. These findings call out the need for more attention towards a deeper understanding of alternatives and the need for better tools to facilitate the exploration, interpretation, and management of alternatives. Drawing upon these analyses and findings, we present a framework based on participants' 1) degree of attention, 2) abstraction level, and 3) analytic processes. We show how this framework can help understand how data workers consider such alternatives in their analyses and how tool designers might create tools to better support them.

Uncertainty plays an important and complex role in data analysis, where the goal is to find pertinent patterns, build robust models, and support decision making. While these endeavours are often associated with professional data scientists, many domain experts engage in such activities with varying skill levels. To understand how these domain experts (or "data workers") analyse uncertain data we conducted a qualitative user study with 12 participants from a variety of domains. In this paper, we describe their various coping strategies to understand, minmise, exploit or even ignore this uncertainty. The choice of the coping strategy is influenced by accepted domain practices, but appears to depend on the types and sources of uncertainty and whether participants have access to support tools. Based on these findings, we propose a new process model of how data workers analyse various types of uncertain data and conclude with design considerations for uncertainty-aware data analytics.

We introduce Codestrates, a literate computing approach to developing interactive software. Codestrates blurs the distinction between the use and development of applications. It builds on the literate computing approach, commonly found in interactive notebooks such as Jupyter notebook. Literate computing weaves together prose and live computation in the same document. However, literate computing in interactive notebooks are limited to computation and it is challenging to extend their user interface, reprogram their functionality, or develop stand-alone applications. Codestrates builds literate computing capabilities on top of Webstrates and demonstrates how it can be used for (i) collaborative interactive notebooks, (ii) extending its functionality from within itself, and (iii) developing reprogrammable applications.

System schematics, such as those used for electrical or hydraulic systems, can be large and complex. Fisheye techniques can help navigate such large documents by maintaining the context around a focus region, but the distortion introduced by traditional fisheye techniques can impair the readability of the diagram. We present SchemeLens, a vector-based, topology-aware fisheye technique which aims to maintain the readability of the diagram. Vector-based scaling reduces distortion to components, but distorts layout. We present several strategies to reduce this distortion by using the structure of the topology, including orthogonality and alignment, and a model of user intention to foster smooth and predictable navigation. We evaluate this approach through two user studies: Results show that (1) SchemeLens is 16-27% faster than both round and rectangular flat-top fisheye lenses at finding and identifying a targ et alng one or several paths in a network diagram; (2) augmenting SchemeLens with a model of user intentions aids in learning the network topology.

We revisit Alan Kay's early vision of dynamic media that blurs the distinction between documents and applications. We introduce shareable dynamic media that are malleable by users, who may appropriate them in idiosyncratic ways; shareable among users, who collaborate on multiple aspects of the media; and distributable across diverse devices and platforms. We present Webstrates, an environment for exploring shareable dynamic media. Webstrates augment web technology with real-time sharing. They turn web pages into substrates, i.e. software entities that act as applications or documents depending upon use. We illustrate Webstrates with two implemented case studies: users collaboratively author an article with functionally and visually different editors that they can personalize and extend at run-time; and they orchestrate its presentation and audience participation with multiple devices. We demonstrate the simplicity and generative power of Webstrates with three additional prototypes and evaluate it from a systems perspective.

We present the WADE Integrated Development Environment (IDE), which simplifies interface and functionality modification of existing third-party software without access to source code. WADE clones the Graphical User Interface (GUI) of a host program through dynamic-link library (DLL) injection, enabling modifications to (1) the GUI in a WYSIWYG fashion and (2) software functionality. We compare WADE with an alternative state-of-the-art runtime toolkit overloading approach in a user-study, whose results demonstrate that WADE significantly simplifies the task of GUI-based add-on development.

We present WatchIt, a prototype device that extends interaction beyond the watch surface to the wristband, and two interaction techniques for command selection and execution. Because the small screen of wristwatch computers suffers from visual occlusion and the fat finger problem, we investigated the use of the wristband as an available interaction resource. Not only does WatchIt use a cheap, energy efficient and invisible technology, but it involves simple, basic gestures that allow good performance after little training, as suggested by the results of a pilot study. We propose a novel gesture technique and an adaptation of an existing menu technique suitable for wristband interaction. In a user study, we investigated their usage in eyes-free contexts, finding that they perform well. Finally, we present techniques where the bracelet is used in addition to the screen to provide precise continuous control over list scrolling. We also report on a preliminary survey of traditional and digital jewelry that points to the high frequency of watches and bracelets in both genders and gives a sense of the tasks people feel like performing on such devices.

We propose Augmented Letters, a new technique aimed at augmenting gesture-based techniques such as Marking Menus [9] by giving them natural, mnemonic associations. Augmented Letters gestures consist of the initial of command names, sketched by hand in the Unistroke style, and affixed with a straight tail. We designed a tentative touch device interaction technique that supports fast interactions with large sets of commands, is easily discoverable, improves user's recall at no speed cost, and supports fluid transition from novice to expert mode. An experiment suggests that Augmented Letters outperform Marking Menu in terms of user recall.

This paper presents a novel middleware for developing flexible interactive multi-surface applications. Using a scenario-based approach, we identify the requirements for this type of applications. We then introduce Substance, a data-oriented framework that decouples functionality from data, and Shared Substance, a middleware implemented in Substance that provides powerful sharing abstractions. We describe our implementation of two applications with Shared Substance and discuss the insights gained from these experiments. Our finding is that the combination of a data-oriented programming model with middleware support for sharing data and functionality provides a flexible, robust solution with low viscosity at both design-time and run-time.

This article introduces runtime toolkit overloading, a novel approach to help third-party developers modify the interaction and behavior of existing software applications without access to their underlying source code. We describe the abstractions provided by this approach as well as the mechanisms for implementing them in existing environments. We describe Scotty, a prototype implementation for Mac OS X Cocoa that enables developers to modify existing applications at runtime, and we demonstrate a collection of interaction and functional transformations on existing off-the-shelf applications. We show how Scotty helps a developer make sense of unfamiliar software, even without access to its source code. We further discuss what features of future environments would facilitate this kind of runtime software development.

Information awareness applications offer the exciting potential to help people to better manage the data they encounter on a routine basis, but customizing these applications is a difficult task. Most applications allow users to perform basic customizations or programmers to create advanced ones. We present an intermediate customization space and Cocoa Buzz, an application that demonstrates one way to bridge these two extremes. Cocoa Buzz runs on an extra display on the user's desktop or on a large shared display and cycles through different information sources customized by the user. We further demonstrate some of the customizations that have been made using this approach. We show some preliminary evidence to suggest that this approach may be useful at providing users with the ability to perform customizations across this spectrum.

How can designers of ubiquitous computing technologies ensure that they understand the non-functional needs, values, and expectations of end-users? In this paper, we use a qualitative method from public policy to elicit reflective feedback from end-users about technologies that they may not yet have used nor fully comprehend. Our study uncovers information about end-user perceptions of RFID, including a range of "folk theories" held by the public about this technology, and their associations of it with certain social groups and values. We argue that these perceptions can limit technological adoption, and conclude with a discussion of challenges for the design and deployment of ubiquitous computing systems.

Peripheral awareness systems offer significant promise to help people to manage the abundance of information available to them without placing significant additional demands on attention. Rich personalization is essential to enabling these systems to provide relevant information to a particular person's individual needs in their own particular situation. I focus on rich personalization and its implications for the interfaces that can enable it.

In this paper, we describe JHAVI~ (Java-hosted Algorithm Visualization Environment), a client-server environment for delivering algorithm visualizations over the Web.The first section of the paper briefly summarizes prior research by a variety of investigators into the pedagogical effectiveness of algorithm visualization (AV).The design goals of JHAVI~ are then placed in the context of this research.After a discussion of some technical details of the JHAVI~ architecture, we present two examples of algorithms depicted in JHAVI~.The results of students' exploring these algorithms with JHAVI~ are analyzed.We close with a discussion of the general conclusions reached from our current work and future directions in which it may lead.