£1.2m EPSRC Award for “Fixing The Future: The Right to Repair and Equal-IoT”

Consider this future scenario:

Home insurance firms now mandate installation of smart smoke alarms. One popular low-cost alarm has developed a speaker hardware fault and recently stopped providing software security updates after just 2 years. Adam, who can financially afford to, simply buys another alarm and throws the broken one away. Ben however, cannot afford a new alarm and faces unexpected financial consequences. The lack of security means his home network has been compromised and hackers intercepted his banking log-in credentials. Worse still, the faulty alarm did not warn him of a small housefire in the night, causing damage he cannot afford to repair. The insurance company is refusing to pay the claim, as unknown to Ben, they recently removed the device from their list of accredited alarms. 

October 2022 is the start of Fixing The Future: The Right to Repair and Equal-IoTa £1.2m EPSRC project that examines how to avoid such future inequalities due to the poor long-term cybersecurity, exploitative use of data and lacking environmental sustainability that defines the current IoT. Presently, when IoT devices are physically damaged, malfunction or cease to be supported, they no longer operate reliably. Devices also have planned obsolescence, featuring inadequate planning for responsible management throughout their lifespan. This impacts on those who lack the socio-economic means to repair or maintain their IoT devices, leaving them excluded from digital life for example, with broken phone screens or cameras.

This project is an ambitious endeavour that aims to develop a digitally inclusive and more sustainable Equal-IoT toolkit by working across disciplines such as law and ethics, Human Computer Interaction, Design and technology.

Law and Ethics: To what extent do current legal and ethical frameworks act as barriers to equity in the digital economy, and how should they be improved in the future to support our vision of Equal-IoT?  We will examine how cybersecurity laws on ‘security by design’ shape design e.g. the proposed UK Product Security & Telecoms Infrastructure Bill; how consumer protection laws can protect users from loss of service and data when IoT devices no longer work e.g. the US FTC Investigation of Revolv smart hubs which disconnected/bricked customer devices due to a buy-out by Nest.

Design: How can grassroots community repair groups help inform next-generation human-centred design principles to support the emergence of Equal-IoT?  We will use The Repair Shop 2049 as a prototyping platform to explore the legal and HCI related insights in close partnership with The Making Rooms (TMR), a community led fabrication lab working as a grassroots repair network in Blackburn. This will combine the expertise of local makers, citizens,   civic leaders and technologists to understand how best to design and implement human-centred Equal-IoT infrastructures and explore the lived experience of socio-economic deprivation and digital exclusion in Blackburn.

Human Computer (Data) Interaction (HDI): How can HCI help operationalise repairability and enable creation of Equal-IoT? We will create prototype future user experiences and technical design architectures that showcase best practice on how Equal-IoT can be built to be more repairable and address inequalities posed by current IoT design. Our series of blueprints, patterns and frameworks will align needs of citizens technical requirements and reflect both constraints and opportunities manufacturers face.

Image created and provided by Michael Stead

This is an exciting collaboration between the Universities of Nottingham, Edinburgh, Lancaster and Napier, along with industry partners –Which?, NCC Group, Canadian Government, BBC R&D, The Making Rooms.

For more information about this project, contact Horizon Transitional Assistant Professor Neelima Sailaja






Helena updates us on her latest research




As a socio-technical researcher I work on projects that combine elements of computer science and the social sciences. I focus on bringing in the human experience and perspective into our understanding of technology and enjoy highlighting how these human factors can positively shape technological development. Joining Horizon as a Transitional Assistant Professor allows me space to develop my research portfolio and I am excited to be bringing this approach into a new research collaboration, called “Artificial Intelligence Decision Support for Kidney Transplantation (AID-KT)”

AID-KT is funded by NIHR and is led by a team at the University of Oxford. The project co-leaders are Simon Knight in the Centre for Evidence in Transplantation and Tingting Zhu from the Oxford Computational Health Informatics Lab. The project seeks to improve outcomes in kidney transplantation by developing an AI decision support tool.

The kidney is the most transplanted organ – accounting for just over 65% of organ transplants. At any one time, there are around 5,000 patients on the waiting list for a kidney transplant in the UK. However, donor organs are often turned down due to fears of poor outcomes for patients. Currently there is an absence of support tools to help clinicians determine and discuss with their patients how likely the transplant of a specific donor kidney will be for them, plus how this might compare to not having the transplant and waiting for another kidney to become available. Being able to predict the graft survival of the kidney after transplant could greatly increase the transplant success rate, leading to better outcomes for, and making better use of the available organ pool and healthcare resources.

The aim of this project is to address this absence by developing and testing a clinical decision support tool for kidney transplant. It will be driven by machine learning techniques and will help answer this crucial clinical question for potential kidney donor recipients:

Will my outcome be better if I accept this transplant offer, or wait for the next offer in the future?

Much of the project focuses on the creation and validation of machine learning models that can accurately predict graft and patient survival following transplant and patient outcomes if a transplant offer is declined. Alongside this, we are conducting work to make these models explainable and transparent. Little existing research has investigated how to present these kinds of clinical predictions to patients and clinicians in ways they find accessible. Therefore, we will be involving clinicians and patients in our research – through qualitative interviews and other methods – to assess which data are useful to them and how data should be presented to support decision making and informed consent. As part of this we recognise that patients will differ in terms of the level of detail they want to have and the extent to which they prefer to lead their own decision making or defer to clinical judgement. As such, the clinical decision tool needs to be adaptable to the preferences of different individuals. In addition, it also needs to make its salient features visible and interpretable to clinician users so that they can understand how the model is using underlying data and explain the predictions made to patients clearly. By bringing in these human perspectives to the development of the tool we can optimise its usefulness and effectiveness in clinical settings. By extension we can also, hopefully, improve acceptance rates for kidney transplants as well as post-transplant outcomes.

Written by Helena Webb