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Since GDPR went into effect in 2018, many other data protection and privacy regulations have been released. With the new regulation, there has been an associated increase in industry professionals focused on data protection and privacy. Building on related work showing the potential benefits of knowledge management in organisational compliance and privacy engineering, this paper presents the findings of an exploratory qualitative study with data protection officers and other privacy professionals. We found issues with knowledge management to be the underlying challenge of our participants' feedback. Our participants noted four categories of feedback: (1) a perceived disconnect between regulation and practice, (2) a general lack of clear job description, (3) the need for data protection and privacy to be involved at every level of an organisation, (4) knowledge management tools exist but are not used effectively. This paper questions what knowledge management or automation solutions may prove to be effective in establishing better computer-supported work environments., 27 pages, 1 figure, 1 table, DRAFT and Unpublished

The Computers Are Social Actors paradigm suggests people exhibit social/anthropomorphic biases in their treatment of technology. Such insights have encouraged the design of interfaces that interact with users in more social (chatty or even friend-like) ways. However, in typical `dark pattern' fashion, social-emotional responses to systems as (seemingly sentient) agents can be harnessed to manipulate user behaviour. An increasingly common example is app notifications that assume person-like tones to persuade or pressure users into compliance. Regardless of being manipulative, difficulties meeting contextual social expectations can make automated social acting seem rude, invasive, tactless, and even disrespectful - constituting 'social' anti-patterns. This paper explores ways to improve how automated systems treat people in interactions. We mixed four qualitative methods to elicit user experiences and preferences regarding how automated systems "talk" to/at them. We identify an emerging 'social' class of dark and anti-patterns, and propose guidelines for helping (social) interfaces treat users in more respectful, tactful, and autonomy-supportive ways., Comment: Submitted to the 2023 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW)

Tracking is a highly privacy-invasive data collection practice that has been ubiquitous in mobile apps for many years due to its role in supporting advertising-based revenue models. In response, Apple introduced two significant changes with iOS 14: App Tracking Transparency (ATT), a mandatory opt-in system for enabling tracking on iOS, and Privacy Nutrition Labels, which disclose what kinds of data each app processes. So far, the impact of these changes on individual privacy and control has not been well understood. This paper addresses this gap by analysing two versions of 1,759 iOS apps from the UK App Store: one version from before iOS 14 and one that has been updated to comply with the new rules. We find that Apple's new policies, as promised, prevent the collection of the Identifier for Advertisers (IDFA), an identifier for cross-app tracking. Smaller data brokers that engage in invasive data practices will now face higher challenges in tracking users - a positive development for privacy. However, the number of tracking libraries has roughly stayed the same in the studied apps. Many apps still collect device information that can be used to track users at a group level (cohort tracking) or identify individuals probabilistically (fingerprinting). We find real-world evidence of apps computing and agreeing on a fingerprinting-derived identifier through the use of server-side code, thereby violating Apple's policies. We find that Apple itself engages in some forms of tracking and exempts invasive data practices like first-party tracking and credit scoring. We also find that the new Privacy Nutrition Labels are sometimes inaccurate and misleading. Overall, our findings suggest that, while tracking individual users is more difficult now, the changes reinforce existing market power of gatekeeper companies with access to large troves of first-party data and motivate a countermovement., Comment: The paper has been accepted for publication by the ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2022

In this article, we conduct data mining to discover the countries, universities and companies, produced or collaborated the most research on Covid-19 since the pandemic started. We present some interesting findings, but despite analysing all available records on COVID-19 from the Web of Science Core Collection, we failed to reach any significant conclusions on how the world responded to the COVID-19 pandemic. Therefore, we increased our analysis to include all available data records on pandemics and epidemics from 1900 to 2020. We discover some interesting results on countries, universities and companies, that produced collaborated most the most in research on pandemic and epidemics. Then we compared the results with the analysing on COVID-19 data records. This has created some interesting findings that are explained and graphically visualised in the article.

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.

Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry., Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table 1

In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing., 8 pages, 0 figures, The General Data Protection Regulation: An Opportunity for the CHI Community? (CHI-GDPR 2018), Workshop at ACM CHI'18, 22 April 2018, Montreal, Canada

Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions---like taxation, justice, and child protection---are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning---absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications., 14 pages, 0 figures, ACM Conference on Human Factors in Computing Systems (CHI'18), April 21--26, Montreal, Canada

A characteristic trend of digital health has been the dramatic increase in patient-generated data being presented to clinicians, which follows from the increased ubiquity of self-tracking practices by individuals, driven, in turn, by the proliferation of self-tracking tools and technologies. Such tools not only make self-tracking easier but also potentially more reliable by automating data collection, curation, and storage. While self-tracking practices themselves have been studied extensively in human–computer interaction literature, little work has yet looked at whether these patient-generated data might be able to support clinical processes, such as providing evidence for diagnoses, treatment monitoring, or postprocedure recovery, and how we can define information quality with respect to self-tracked data. In this article, we present the results of a literature review of empirical studies of self-tracking tools, in which we identify how clinicians perceive quality of information from such tools. In the studies, clinicians perceive several characteristics of information quality relating to accuracy and reliability, completeness, context, patient motivation, and representation. We discuss the issues these present in admitting self-tracked data as evidence for clinical decisions.

Human interaction is increasingly mediated through technological systems, resulting in the emergence of a new class of socio-technical systems, often called Social Machines. However, many systems are designed and managed in a centralised way, limiting the participants’ autonomy and ability to shape the systems they are part of.In this paper we are concerned with creating a graphical formalism that allows novice users to simply draw the patterns of interaction that they desire, and have computational infrastructure assemble around the diagram. Our work includes a series of participatory design workshops, that help to understand the levels and types of abstraction that the general public are comfortable with when designing socio-technical systems. These design studies lead to a novel formalism that allows us to compose rich interaction protocols into functioning, executable architecture. We demonstrate this by translating one of the designs produced by workshop participants into an a running agent institution using the Lightweight Social Calculus (LSC).

Mobile digital behaviour changes interventions (mDBCIs) are becoming increasingly useful and necessary within healthcare and wellbeing. Health interventions need to close the gap between intention to behave and the behaviour itself. If apps fail to engage users, the behavioural intervention material is never seen. This paper investigates the measurements of engagement using a health based quiz app. Quiz questions were created using the NHS website and fell into the following six categories: “healthy eating”, “losing weight”, “sleep”, “fitness”, “food” and “smoking”. Gamification features such as count down timers were used to encourage user participation. Notifications, with individual goals, were sent out to nudge the users to play the quiz. Engagement was measured in two ways. Firstly, a count of completed quiz questions to illustrate app engagement. Secondly, a participants learning was evaluated using a learning curve. This measured whether a participant understood and retained the health facts, illustrated by an improvement in their answers over time. A comparison between two participants using the first measurement (count of completed quiz questions) would have shown an identical rate of engagement, both answering 44 questions. However, the second measure (engagement with app content) showed a different rate of engagement. One participant improved producing the expected learning curve whilst the other consistently answered questions wrong, showing a lack of engagement with the intervention material. Participants were allowed to select their preferred category of question and in this case chose different areas. This was to encourage them to choose topics of interest to further improve their commitment to the study and provide a more personal experience. Future research would include more participants within each question category for further analysis and direct comparison. However, this research illustrates that these two types of engagement, app and intervention material, separately paint different pictures. To improve the design and effectiveness of mDBCIs engagement analysis, studies need to utilise more than one method of measurement

The recent emergence of online citizen science is illustrative of an efficient and effective means to harness the crowd in order to achieve a range of scientific discoveries. Fundamentally, citizen science projects draw upon crowds of non-expert volunteers to complete short Tasks, which can vary in domain and complexity. However, unlike most human-computational systems, participants in these systems, the `citizen scientists' are volunteers, whereby no incentives, financial or otherwise, are offered. Furthermore, encouraged by citizen science platforms such as Zooniverse, online communities have emerged, providing them with an environment to discuss, share ideas, and solve problems. In fact, it is the result of these forums that has enabled a number of scientific discoveries to be made. In this paper we explore the phenomenon of collective intelligence via the relationship between the activities of online citizen science communities and the discovery of scientific knowledge. We perform a cross-project analysis of ten Zooniverse citizen science projects and analyse the behaviour of users with regards to their Task completion activity and participation in discussion and discover collective behaviour amongst highly active users. Whilst our findings have implications for future citizen science design, we also consider the wider implications for understanding collective intelligence research in general.

The use of personal data has incredible potential to benefit both society and individuals through increased understanding of behaviour, communication and support for emerging forms of socialisation and connectedness. However, there are risks associated with disclosing personal information, and present systems show a systematic asymmetry between the subjects of the data and those who control and manage the way that data is propagated and used. This leads to a tension between a desire to engage with online society and enjoy its benefits on one hand, and a distrust of those with whom the data is shared on the other. In this chapter, we explore a set of obfuscation techniques which may help to redress the balance of power when sharing personal data, and return agency and choice to users of online services.

Most existing PIM tools either suffer from having data isolated by being siloed in an application or only interact with specific tools offered through specific services. The consequences are that what people can do with their own data becomes constrained by what services application developers afford. We propose a web-standards based architecture called WebBox to support easy maintenance and repurposing of one's own data for private, social or public publishing, collaboration and reuse.

http://dayta.me is a highly personal information recommender that augments a person’s online calendar with useful information pertaining to their upcoming activities. To perform this recommendation, it draws upon a large collection of distributed, linked-data and Web 2.0 data sources it queries live, and provides a clear, simple user interface that regular citizens can easily use to situate useful, but formerly inaccessible, information within the context of their daily activities.

One of the foremost challenges towards realizing a “Read-write Web of Data” [3] is making it possible for everyday computer users to easily find, manipulate, create, and publish data back to the Web so that it can be made available for others to use. However, many aspects of Linked Data make authoring and manipulation difficult for “normal” (ie non-coder) end-users. First, data can be high-dimensional, having arbitrary many properties per “instance”, and interlinked to arbitrary many other instances in a many different ways. Second, collections of Linked Data tend to be vastly more heterogeneous than in typical structured databases, where instances are kept in uniform collections (e.g., database tables). Third, while highly flexible, the problem of having all structures reduced as a graph is verbosity: even simple structures can appear complex. Finally, many of the concepts involved in linked data authoring - for example, terms used to define ontologies are highly abstract and foreign to regular citizen-users.To counter this complexity we have devised a drag-and-drop direct manipulation interface that makes authoring Linked Data easy, fun, and accessible to a wide audience. Groupie allows users to author data simply by dragging blobs representing entities into other entities to compose relationships, establishing one relational link at a time. Since the underlying representation is RDF, Groupie facilitates the inclusion of references to entities and properties defined elsewhere on the Web through integration with popular Linked Data indexing services. Finally, to make it easy for new users to build upon others’ work, Groupie provides a communal space where all data sets created by users can be shared, cloned and modified, allowing individual users to help each other model complex domains thereby leveraging collective intelligence.

The US and UK governments have recently made much of the data created by their various departments available as data sets (often as csv files) available on the web. Known as ”open data” while these are valuable assets, much of this data remains useless because it is effectively inaccessible for citizens to access for the following reasons: (1) it is often a tedious, many step process for citizens simply to find data relevant to a query. Once the data candidate is located, it often must be downloaded and opened in a separate application simply to see if the data that may satisfy the query is contained in it. (2) It is difficult to join related data sets to create richer integrated information (3) it is particularly difficult to query either a single data set, and even harder to query across related data sets. (4) To date, one has had to be well versed in semantic web protocols like SPARQL, RDF and URI formation to integrate and query such sources as reusable linked data. Our goal has been to develop tools that will let regular, non-programmer web citizens make use of this Web of Data. To this end, we present GEORDi, a set of integrated tools and services that lets citizen users identify, explore, query and represent these open data sources over the web via Linked Data mechanisms. In this paper we describe the GEORDi process of authoring new and translating existing open data in a linkable format, GEORDi’s lens mechanism for rendering rich, plain language descriptions and views of resources, and the GEORDI link-sliding paradigm for data exploration. With these tools we demonstrate that it is possible to make the Web of open (and linked) data accessible for ordinary web citizen users.

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools.

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this chapter, we describe a personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their time-varying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate's open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model.

In this position paper we explore current work in AtomsMasher, an end-user reactive programming environment for the Web, highlight ongoing work in user interface design, privacy, and sharing, and look towards a future of extending end-user programming from simple tasks to complete experiences.

The rise of "Web 2.0" has seen an explosion of web sites for the social sharing of personal information. To enable users to make valuable use of the rich yet fragmented sea of public, social, and personal information, data mashups emerged to provide a means for combining and filtering such information into coherent feeds and visualizations. In this paper we present AtomsMasher (AM), a new framework which extends data mashups into the realm of context-aware reactive behaviors. Reactive scripts in AM can be made to trigger automatically in response to changes in its world model derived from multiple web-based data feeds. By exposing a simple state-model abstraction and query language abstractions of data derived from heterogeneous web feeds through a simulation-based interactive script debugging environment, AM greatly simplifies the process of creating such automation in a way that is flexible, predictable, scalable and within the reach of everyday Web programmers.

In this position paper, we argue that improved mechanisms for knowledge acquisition and access on the semantic web (SW) will be necessary before it will be adopted widely by end-users. In particular, we propose an investigation surrounding improved languages for knowledge exchange, better UI mechanisms for interaction, and potential help from user modeling to enable accurate, efficient, SW knowledge modeling for everyone.

Interacting with digital PIM tools like calendars, to-do lists, address books, bookmarks and so on, is a highly manual, often repetitive and frequently tedious process. Despite increases in memory and processor power over the past two decades of personal computing, not much has changed in the way we engage with such applications. We must still manually decompose frequently performed tasks into multiple smaller, data specific processes if we want to be able to recall or reuse the information in some meaningful way. "Meeting with Yves at 5 in Stata about blah" breaks down into rigid, fixed semantics in separate applications: data to be recorded in calendar fields, address book fields and, as for the blah, something that does not necessarily exist as a PIM application data structure. We argue that a reason Personal Information Management tools may be so manual, and so effectively fragmented, is that they are not personal enough. If our information systems were more personal, that is, if they knew in a manner similar to the way a personal assistant would know us and support us, then our tools would be more helpful: an assistive PIM tool would gather together the necessary material in support of our meeting with Yves. We, therefore, have been investigating the possible paths towards PIM tools as tools that work for us, rather than tools that seemingly make us work for them. To that end, in the following sections we consider how we may develop a framework for PIM tools as "perfect digital assistants" (PDA). Our impetus has been to explore how, by considering the affordances of a Real World personal assistant, we can conceptualize a design framework, and from there a development program for a digital simulacrum of such an assistant that is not for some far off future, but for the much nearer term.

This paper introduces AtomsMasher, an environment for creating reactive scripts that can draw upon widely heterogeneous information to automate common information-intensive tasks. AtomsMasher is enabled by the wealth of user-contributed personal, social and contextual information that has arisen from Web2.0 social networking content sharing and micro-blogging sites. Starting with existing web mashup tools and end-user automation, we describe new challenges in achieving reactive behaviours: deriving a consistent representation that can be used to predictably drive discrete action from a multitude of noisy, incomplete and inconsistent data sources. Our solution employs a mix of automatic and user-assisted approaches to build a common internal representation in RDF, which is used to provide a simplified programming model that lets Web2.0 programmers succinctly specify behaviours in terms of high level relationships between entities and their current contextual state. We highlight the advantages and limitations of this architecture, and conclude with ongoing work towards making the system more predictable and understandable, and accessible to non-programmers.

This paper is a case study of an artifact design and evaluation process; it is a reflection on how right thinking about design methods may at times result in sub-optimal results. Our goal has been to assess our decision making process throughout the design and evaluation stages for a software prototype in order to consider where design methodology may need to be tuned to be more sensitive to the domain of practice, in this case software evaluation in personal information management. In particular, we reflect on design methods around (1) scale of prototype, (2) prototyping and design process, (3) study design, and (4) study population.

We introduce research on information scraps – short, self-contained personal notes that fall outside of traditional filing schemes. We report on a preliminary study of information scraps’ nature and outline plans for the next phase of our user study. Based on ongoing study results, we describe our designs and prototypes for information scrap capture and access tools.

Information cannot be found if it is not entered. Research shows that existing rich graphical application approaches interfere with user input in many ways, forcing complex interactions to enter simple information, requiring complex cognition to decide where the data should be stored, and limiting the kind of information that can be entered to what can fit into specific applications' data models. Freeform text entry suffers from none of these limitations but produces data that is hard to retrieve or visualize. We describe the design and implementation of a system that aims to bridge these two modalities, supporting lightweight text entry, and weightless context capture, that produces enough structure to support rich interactive presentation and retrieval of the arbitrary information entered.

Cite as:Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao and Nigel Shadbolt (2018) 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions. ACM Conference on Human Factors in Computing Systems (CHI'18), April 21–26, Montreal, Canada. doi: 10.1145/3173574.3173951Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

Mainstream PIM tools capture only a portion of the information that people need to manage. Many information scraps seem to exist that don't make their way into these tools, instead being relegated to sticky notes, text files, and other makeshift storage, or simply being lost. In an effort to understand the role of these information scraps, the underlying needs they reflect, and the way PIM tools must be modified to support those needs, we created List-it, a micronote tool for quick and simple capture of information scraps. In this article, we analyze the notes and interaction logs of 420 volunteer users of List-it over a two-year period of study (August 2008-August 2010). We contextualize our analysis with results of two surveys and an e-mail interview we conducted in October 2009. We find that people are drawn to List-it by the ease and speed of note capture and by the ability to record scraps with arbitrary content that blends or completely escapes the types and roles imposed by our rigid PIM tools. Notes are taken to serve a variety of needs -- reminding, reference, journaling/activity logging, brainstorming, and to indefinitely archive information of sentimental or personal value. Finally, while people differ considerably in the ways they keep information, our findings suggest such differences can be described as a combination of four distinct strategies, enriching the Filer/Piler distinction identified for classic document management.

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this paper, we describe a next-generation personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their time-varying states and activities. Combined with other information sources on the web, including the user’s online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate’s open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model.