We argue that one major weakness in current remote work technologies is the lack of support for relation work and articulation work, caused by limited embodiment. The concept of relation work denotes the fundamental activities of creating socio-technical connections between people and artefacts during collaborative activities, enabling actors in a global collaborative setting to engage each other in activities such as articulation work. We know that articulation work cannot be handled in the same way in hybrid remote environments. The fundamental difference is that strategies of awareness and coordination mechanisms are embedded in the physical surroundings, and use of artefacts cannot simply be applied to the hybrid setting, but instead requires translation.

Actors in hybrid settings must create and connect the foundational network of globally distributed people and artefacts in a multitude of ways.

In REWORK, we focus on enriching digital technologies for hybrid work. We will investigate ways to strengthen relation work and articulation work through explorations of embodiment and presence. To imagine futures and technologies that can be otherwise, we look to artistic interventions, getting at the core of engagement and reflection on the future of remote and hybrid work by imagining and making alternatives through aesthetic speculations and prototyping of novel multimodal interactions (using the audio, haptic, visual, and even olfactory modalities). We will explore the limits of embodiment in remote settings by uncovering the challenges and limitations of existing technical solutions, following a similar approach as some of our previous research.

Scientific value
REWORK will develop speculative techniques and ideas that can help rethink the practices and infrastructures of remote work and its future. REWORK focuses on more than just the efficiency of task completion in hybrid work. Rather, we seek to foreground and productively support the invisible relation and articulation work that is necessary to ensure overall wellbeing and productivity.

Specifically, REWORK will contribute:

1. Speculative techniques for thinking about the future of remote work;
2. Multimodal prototypes to inspire a rethink of remote work;
3. Design Fictions anchoring future visions in practice;
4. Socio-technical framework for the future of hybrid remote work practices;
5. Toolkits for industry.

The research conducted as part of REWORK will produce substantial scientific contributions disseminated through scientific publications in top international journals and conferences relevant to the topic. The scientific contributions will constitute both substantive insights and methodological innovations. These will be targeting venues such as the Journal of Human-Computer Interaction, ACM TOCHI, Journal of Computer Supported Cooperative Work, the ACM CHI conference, NordiCHI, UIST, DIS, Ubicomp, ICMI, CSCW, and others of a similar level.

The project will also engage directly and closely with industries of different kinds, from startups that are actively envisioning new technology to support different types of hybrid work (Cadpeople, Synergy XR, and Studio Koh) to organizations that are trying to find new solutions to accommodate changes in work practices (Arla, Bankdata, Keyloop, BEC).

Part of the intent of engagement with the artistic collaboratory is to create bridges between artistic explorations and practical needs articulated by relevant industry actors. REWORK will enable the creation of hybrid fora to enable such bridging. The artistic collaboratory will enable the project to engage with the general public through an art exhibit at Catch, public talks, and workshops. It is our goal to exhibit some of the artistic output at a venue, such as Ars Electronica, that crosses artistic and scientific audiences.

Societal value
The results of REWORK have the potential to change everybody’s work life broadly. We all know that “returning to work after COVID-19” will not be the same – and the combined situation of hybrid work will be a challenge. Through the research conducted in REWORK, individuals that must navigate the demands of hybrid work and the organizations that must develop policies and practices to support such work will benefit from the improved sense of embodiment and awareness, leading to more effective collaboration.

REWORK will take broadening participation and public engagement seriously, by offering online and in-person workshops/events through a close collaboration with the arts organization Catch (catch.dk). The workshops will be oriented towards particular stakeholder groups – artists interested in exploring the future of hybrid work, industry organizations interested in reconfiguring their existing practices – and open public events.

Capacity building
There are several ways in which REWORK contributes to capacity building. Firstly, by collaborating with the Alexandra Institute, we will create a multimodal toolbox/demonstrator facility that can be used in education, and in industry.

REWORK will work closely with both industry partners (through the Alexandra Institute) and cultural (e.g. catch.dk)/public institutions for collaboration and knowledge dissemination, in the general spirit of DIREC.

We will include the findings from REWORK in our research-based teaching at all three universities. Furthermore, we plan to host a PhD course, or a summer school, on the topic in Year 2 or Year 3. Participants will be recruited nationally and internationally.

Lastly, in terms of public engagement, HCI and collaborative technologies are disciplines that can be attractive to the public at large, so there will be at least one REWORK Open Day where we will invite interested participants, and the DIREC industrial collaborators.

January 1, 2022 – December 31, 2024 – 3 years.

The main research problems are:

1. To identify safety and security requirements (including threats, attacker models and counter measures) for IoT systems, as well as the inherent design limitations in the IoT problem domain (e.g., limited computing resources and a limited energy budget).
2. To organize the knowledge in a comprehensive model. We propose to extend attack‐defense trees with strategic game features and quantitative aspects (time, cost, energy, probability).
3. To transform this new model into existing “computer models” (automata and games) that are amenable to automatic analysis algorithms. We consider stochastic priced timed games as an underlying framework for such models due to their generality and existing tool support.
4. To develop/extend the algorithms needed to perform analysis and synthesis of optimal response strategies, which form the basis of quantitative risk assessment and decision‐making.
5. To translate the findings into instruments and recommendations for the partner companies, addressing both technical and organizational needs.
6. To design, evaluate, and assess the user interface of the IoT security tools, which serve as important backbones supporting to design and certify IoT security training programs for stakeholder organizations.

Throughout the project, we focus on the challenges and needs of the partner companies. The concrete results and outcomes of the project will also be evaluated in the contexts of these companies. The project will combine the expertise of five partners of DIREC (AAU, AU, Alexandra, CBS and DTU) and four Work Streams from DIREC (WS7: Verification, WS6: CPS and IoT systems, WS8: Cybersecurity and WS5: HCI, CSCW and InfoVis) in a synergistic and collaborative way.

Business value
While it is difficult to make a precise estimate of the number of IoT devices, most estimates are in the range 7‐15 billion connected devices and expected to increase dramatically over the next 5‐10 years. The impact of a successful attack on IoT systems can range from nuisance, e.g., when baby monitors or thermostats are hacked, over potentially expensive DDoS attacks, e.g., when the Mirai malware turned many IoT devices into a DDoS botnet, to life‐threatening, e.g., when pacemakers are not secure. Gartner predicted that the worldwide spending on IoT security will increase from roughly USD 900M to USD 3.1B in 2021 out of a total IoT market up to USD 745B.

The SIOT project will concretely contribute to the agility of the Danish IoT industry. By applying the risk analysis and secure design technologies developed in the project, these companies get a fast path to certification of secure IoT devices. Hence, this project will give Danish companies a head‐start for the near future where the US and UK markets will demand security certification for IoT devices. Also, EU is already working on security regulation for IoT devices. Furthermore, it is well known that the earlier in the development process a security vulnerability or programming error is found, the cheaper it is to fix it. This is even more important for IoT products that may not be updatable “over‐the‐air” and thus require a product recall or physical update process. The methods and technologies developed in this project will help companies find and fix security vulnerabilities already from the design phase and exploration phase, thus reducing long‐term cost of maintenance.

Societal value
It is an academic duty to contribute to safer and more secure IoT systems, since they are permeating the society. Security issues quickly become safety incidents, for instance since IoT systems are monitoring against dangerous physical conditions. In addition, compromised IoT devices can be detrimental for our privacy, since they are measuring all aspects of human life. DTU and Alexandra Institute will disseminate the knowledge and expertise through the network built in the joint CIDI project (Cybersecure IoT in Danish Industry, ending in 2021), in particular a network of Danish IoT companies interested in security, with a clear understanding of companies’ needs for security concerns.

We will strengthen the cybersecurity level of Danish companies in relation to Industry 4.0 and Internet of Things (IoT) security, which are key technological pillars of digital transformation. We will do this by means of research and lectures on several aspects of IoT security, with emphasis on security‐by‐design, risk analysis, and remote attestation techniques as a counter measure.

Capacity building
The education of PhD students itself already contributes to “capacity building”. We will organize a PhD Summer school towards the end of the project, to disseminate the results, across the PhD students from DIREC and students abroad.

We will also prepare learning materials to be integrated in existing course offerings (e.g., existing university courses, and the PhD and Master training networks of DIREC) to ensure that the findings of the project are injected into the current capacity building processes.

Through this education, we will also attract more students for the Danish labor market. The lack of skilled people is even larger in the security area than in other parts of computer science and engineering.

February 1, 2022 – January 31, 2025 – 3 years.

Total budget DKK 25,10 million / DIREC investment DKK 6,74 million

The EXPLAIN-ME initiative seeks to optimize the utility of feedback provided by healthcare explainable AI (XAI). We will approach this problem both in static healthcare applications, where clinical decisions are based on data already collected, and in dynamic applications, where data is collected on the fly to continually improve confidence in the clinical decision. Via an interdisciplinary effort between XAI, medical simulation, participatory design and HCI, we aim to optimize the explanations provided by the XAI to be of maximal utility for clinicians, supporting technology utility and acceptance in the clinic.

Case 1: Renal tumor classification
Classification of a renal tumor as malign or benign is an example of a decision that needs to be taken under time pressure. If malign, the patient should be operated immediately to prevent cancer from spreading to the rest of the body, and thus a false positive diagnosis may lead to the unnecessary destruction of a kidney and other complications. While AI methods can be shown statistically to be more precise than an expert physician, there is a need for extending it with explanation for a decision– and only the physicians know what “a good explanation” is. This motivates a collaborative design and development process to find the best balance between what is technically possible and what is clinically needed.

Case 2: Ultrasound Screening
Even before birth, patients suffer from erroneous decisions made by healthcare workers. In Denmark, 95% of all pregnant women participate in the national ultrasound screening program aimed at detecting severe maternal-fetal disease. Correct diagnosis is directly linked to the skills of the clinicians, and only about half of all serious conditions are detected before birth. AI feedback, therefore, comes with the potential to standardize care across clinicians and hospitals. At DTU, KU and CAMES, ultrasound imaging will be the main case for development, as data access and management, as well as manual annotations, are already in place. We seek to give the clinician feedback during scanning, such as whether the current image is a standard ultrasound plane (see figure); whether it has sufficient quality; whether the image can be used to predict clinical outcomes, or how to move the probe to improve image quality.

Case 3: Robotic Surgery

AAU and NordSim will collaborate on the assessment and development of robotic surgeons’ skills, associated with an existing clinical PhD project. Robotic surgery allows surgeons to do their work with more precision and control than traditional surgical tools, thereby reducing errors and increasing efficiency. AI-based decision support is expected to have a further positive effect on outcomes. The usability of AI decision support is critical, and this project will study temporal aspects of the human-AI collaboration, such as how to present AI suggestions in a timely manner without interrupting the clinician; how to hand over tasks between a member of the medical team and an AI system; and how to handle disagreement between the medical expert and the AI system.

In current healthcare AI research and development, there is often a gap between the needs of clinicians and the developed solutions. This comes with a lost opportunity for added value: We miss out on potential clinical value for creating standardized, high quality care across demographic groups. Just as importantly, we miss out on added business value: If the first, research-based step in the development food chain is unsuccessful, then there will also be fewer spin-offs and start-ups, less knowledge dissemination to industry, and overall less innovation in healthcare AI.

The EXPLAIN-ME initiative will address this problem:

• We will improve clinical interpretability of healthcare AI by developing XAI methods and workflows that allow us to optimize XAI feedback for clinical utility, measured both on clinical performance and clinical outcomes.
• We will improve clinical technology acceptance by introducing these XAI models in clinical training via simulation-laboratories.
• We will improve business value by creating a prototype for collaborative, simulation-based deployment of healthcare AI. This comes with great potential for speeding up industrial development of healthcare AI: Simulation-based testing of algorithms can begin while algorithms still make mistakes, because there is no risk of harming patients. This, in particular, can speed up the timeline from idea to clinical implementation, as the simulation-based testing is realistic while not requiring the usual ethical approvals.

This comes with great potential value: While AI has transformed many aspects of society, its impact on the healthcare sector is so far limited. Diagnostic AI is a key topic in healthcare research, but only marginally deployed in clinical care. This is partly explained by the low interpretability of state-of-the-art AI, which negatively affects both patient safety and clinicians’ technology acceptance. This is also explained by the typical workflow in healthcare AI research and development, which is often structured as parallel tracks where AI researchers independently develop technical solutions to a predefined clinical problem, while only occasionally interacting with the clinical end-users. This often results in a gap between the clinicians’ needs and the developed solution. The EXPLAIN-ME initiative aims to close this gap by developing AI solutions that are designed to interact with clinicians in every step of the design-, training-, and implementation process.

October 1, 2021 – April 30, 2025 – 3,5 years.