- DIS 2020: Strangers in the Room: Unpacking Perceptions of 'Smartness' and Related Ethical Concerns in the Home
- CHI 2020: 'I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook
- CHI 2020: Informing the Design of Privacy-Empowering Tools for the Connected Home
- ECIAIR 2019: Harnessing Interdisciplinarity to Promote the Ethical Design of AI Systems
- CHI 2018: X-Ray Refine
- PETRAS: The Little Book of the Internet of Things for the Home
- CHI 2019: Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools
- CHI 2018: `It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
- WebSci '18: Third party tracking in the mobile ecosystem
- CHI 2018: Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
- CHI 2018 (alt.chi): So, Tell Me What Users Want, What They Really, Really Want!
The increasingly widespread use of ‘smart’ devices has raised multifarious ethical concerns regarding their use in domestic spaces. Previous work examining such ethical dimensions has typically either involved empirical studies of concerns raised by specific devices and use contexts, or alternatively expounded on abstract concepts like autonomy, privacy or trust in relation to `smart homes’ in general.
This paper attempts to bridge these approaches by asking what features of smart devices users consider as rendering them `smart’ and how these relate to ethical concerns. Through a multimethod investigation including surveys with smart device users (n=120) and semi-structured interviews (n=15), we identify and describe eight types of smartness and explore how they engender a variety of ethical concerns including privacy, autonomy, and disruption of the social order. We argue that this middle ground, between concerns arising from particular devices and more abstract ethical concepts, can better anticipate potential ethical concerns regarding smart devices.
Beyond being the world’s largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing.
To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys.
Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of ‘distraction’.
Connected devices in the home represent a potentially grave new privacy threat due to their unfettered access to the most personal spaces in people’s lives. Prior work has shown that despite concerns about such devices, people often lack sufficient awareness, understanding, or means of taking effective action.
To explore the potential for new tools that support such needs directly we developed Aretha, a privacy assistant technology probe that combines a network disaggregator, personal tutor, and firewall, to empower end-users with both the knowledge and mechanisms to control disclosures from their homes. We deployed Aretha in three households over six weeks, with the aim of understanding how this combination of capabilities might enable users to gain awareness of data disclosures by their devices, form educated privacy preferences, and to block unwanted data flows.
The probe, with its novel affordances—and its limitations—prompted users to co-adapt, finding new control mechanisms and suggesting new approaches to address the challenge of regaining privacy in the connected home.
In this paper we describe our experience conducting an ‘ethical hackathon’ to promote the ethical design of AI systems. The model of the ethical hackathon has been developed by researchers in the Human Centred Computing theme as a novel twist on the conventional hackathon competition. Ethical hackathons are fun, educational events in which interdisciplinary teams compete on a design challenge that requires them to consider how responsibility mechanisms can be embedded into what they are building.
The ethical hackathon described in this paper was part of the UnBias project. In the paper we highlight the potential for these events to foster the ethical design and development of AI systems but also identify some practical challenges in running them. We conclude that a successful ethical hackathon needs to foster genuine interdisciplinarity and carefully manage participant expectations. We build on our own experiences by suggesting ways to optimise the ethical hackathon model.
Most smartphone apps collect and share information with various first and third parties; yet, such data collection practices remain largely unbeknownst to, and outside the control of, end-users.
In this paper, we seek to understand the potential for tools to help people refine their exposure to third parties, resulting from their app usage. We designed an interactive, focus-plus-context display called X-Ray Refine (Refine) that uses models of over 1 million Android apps to visualise a person’s exposure profile based on their durations of app use. To support exploration of mitigation strategies, Refine can simulate actions such as app usage reduction, removal, and substitution.
A lab study of Refine found participants achieved a high-level understanding of their exposure, and identified data collection behaviours that violated both their expectations and privacy preferences. Participants also devised bespoke strategies to achieve privacy goals, identifying the key barriers to achieving them.
In our first Little Book in the PETRAS series we explained the term Internet of Things (IoT) as follows:
“… the term [is used] to describe objects or things that can be interconnected via the Internet. This allows them to be readable, recognizable, locatable, addressable, and/or controllable by computers. The things themselves can be literally anything. Later in the book we use examples such as a kettle, a door lock, an electricity meter, a toy doll and a television but it’s important to remember that there is no limit on what is or is not an IoT thing. Anything that is connected to the Internet is arguably part of the IoT including us.”
In this book we focus on IoT products and services targeting the consumer market, in particular, those for use in our homes. These connected products are often referred to as ‘smart’ and our IoT-enabled homes are often called, ‘smart homes’. The promise of smart homes filled with connected products is frequently promoted as a way of making our lives easier and more convenient. For example, the Roomba robotic vacuum cleaner claims to allow you to “Forget about vacuuming for weeks at a time” and that it [the robot] is smart enough to know if your cat has tracked its litter through the house.
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 wellestablished 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.
Data-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 suggest there may be no ‘best’ approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.
This paper won a ‘Best Paper’ award at WebSci ‘18, the 10th ACM Conference on Web Science.
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.
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.
Equating users’ true needs and desires with behavioural measures of ’engagement’ is problematic. However, good metrics of ’true preferences’ are difficult to define, as cognitive biases make people’s preferences change with context and exhibit inconsistencies over time. Yet, HCI research often glosses over the philosophical and theoretical depth of what it means to infer what users really want.
In this paper, we present an alternative yet very real discussion of this issue, via a fictive dialogue between senior executives in a tech company aimed at helping people live the life they ‘really’ want to live. How will the designers settle on a metric for their product to optimise?