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Bridge project

REWORK – The future of hybrid work

DIREC project

REWORK

- The Future of Hybrid Work

Summary

The COVID-19 pandemic, and the consequent lockdown, demonstrated the potential benefits and possibilities of remote work practices, as well as the remarkable deficiencies such practices bring. ‘Zoom fatigue’, resulting from high cognitive loads and intense amounts of eye contact, is just the tip of the iceberg.

Remote and hybrid work will certainly be part of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and design a different kind of future workplace? Together with companies, this project seeks a vision of the future that integrates hybrid work experiences.

Project period: 2022-2025
Budget: DKK 20,21 million

There are a multitude of reasons to embrace remote and hybrid work. Climate concerns are increasing, borders are difficult to cross, work/life balance may be easier to attain, power distributions in society could potentially be redressed, to name a few. This means that the demand for Computer Supported Cooperative Work (CSCW) systems that support hybrid work will increase significantly. At the same time, we consistently observe and collectively experience that current digital technologies struggle to mediate the intricacies of collaborative work of many kinds. Even when everything works, from network connectivity to people being present and willing to engage, there are aspects of embodied co-presence that are almost impossible to achieve digitally.

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.

Impact

The project has the potential to change everybody’s work life broadly. Both employees and management will benefit from an improved sense of embodiment and awareness, leading to more effective collaboration.

News / coverage

Participants

Project Manager

Eve Hoggan

Associate Professor

Aarhus University
Department of Computer Science

E: eve.hoggan@cs.au.dk

Susanne Bødker

Professor

Aarhus University
Department of Computer Science

Irina Shklovski

Professor

University of Copenhagen
Department of Computer Science

Pernille Bjørn

Professor

University of Copenhagen
Department of Computer Science

Louise Barkhuus

Professor

IT University of Copenhagen
Department of Computer Science

Naja Holten Møller

Assistant Professor

University of Copenhagen
Department of Computer Science

Nina Boulus-Rødje

Associate Professor

Roskilde University
Department of People and Technology

Allan Hansen

Head of Digital Experience and Solutions Lab

The Alexandra Institute

Mads Darø Kristensen

Principal Application Architect

The Alexandra Institute

Melanie Duckert Schmidt

PhD student

IT University of Copenhagen
Department of Computer Science

Juliane Busboom

PhD Student

Roskilde University
Department of People and Technology

Qianqian Mu

PhD Student

Aarhus University
Department of Computer Science

Kellie Dunn

PhD Student

University of Copenhagen
Department of Computer Science

Sarbajit Deb

Executive Vice President

LTI

Claus Trillingsgaard

VP Global Delivery and Agile Transformation

Keyloop

Barbara Scherfig

Programme coordinator

Kulturværftet

Rikke Lindekilde

Partner

LEAD

Michael Edwards

CEO

eventSPACE

Peter Bering

Head of Digitalization

Bankdata

Maz Spork

Partner

Unlikly

Simon Lajboschitz

Co-founder & CEO

Khora

Helena Grøn Kähler

People, Strategy and Performance Manager

Arla Foods

Stine Deja

Artist

Jakob la Cour

Artist

Line Finderup Jensen

Artist

Lea Porsager

Artist

Partners

Categories
Bridge project

Secure Internet of Things – Risk Analysis in Design and Operation (SIoT)

DIREC project

Secure Internet of things (SIOT)

- Risk Analysis in Design and Operation

Summary

IoT devices are blending into the infrastructure of both society and our personal lives. However, many of these devices run in uncontrolled, potentially hostile environments, which makes them vulnerable to security attacks.

Moreover, with the increasing number of safety critical IoT devices, such as medical and industrial IoT devices, IoT security is a public safety issue.

In collaboration with industrial partners, the project aims to develop a modeling method for systematically describing relevant aspects of IoT systems/services with a focus on security, interaction, performance, and cost. The project will also introduce a new concept of attack-defense games and algorithms to compute optimal strategies and trade-offs between performance, cost, and security. Additionally, tools will be developed for quantitative risk assessment and “what-if” scenario analysis to enhance the design and operation of secure IoT systems. Usability studies and design for usability of the tools within organizations will be conducted, along with the development of training material to enforce security policies for employees.

 

Project period: 2022-2025
Budget: DKK 25,10 million

When developing novel IoT services or products today, it is essential to consider the potential security implications of the system and to take those into account before deployment. Due to the criticality and widespread deployment of many IoT systems, the need for security in these systems has even been recognised at the government and legislative level, e.g., in the US and the UK, resulting in proposed legislation to enforce at least a minimum of security consideration in deployed IoT products.

However, developing secure IoT systems is notoriously difficult, not least due to the characteristics of many such systems: they often operate in unknown and frequently in privacy‐sensitive environments, engage in communication using a wide variety of protocols and technologies, and must perform essential tasks such as monitoring and controlling (physical) entities. In addition, IoT systems must often perform within real‐ time bounds on limited computing platforms and at times even with a limited energy budget. Moreover, with the increasing number of safety‐critical IoT devices (such as medical devices and industrial IoT devices), IoT security has become a public safety issue. To develop a secure IoT system, one should take into account all of the factors and characteristics mentioned above, and balance them against functionality and performance requirements. Such a risk analysis must be performed not only at the design stage, but also throughout the lifetime of the product. Besides technical aspects, the analysis should also take into account the human and organizational aspects. This type of analysis will form an essential activity for standardization and certification purposes.

In this project, we will develop a modelling formalism with automated tool support, for performing such risk assessments and allowing for extensive “what‐if” scenario analysis. The starting point will be the well‐ known and widely used formalism of attack‐defense trees extended to include various quantities, e.g., cost or energy consumption, as well as game features, for modelling collaboration and competition between systems and between a system and its environment.

In summary, the project will deliever:

  • a modeling method for a systematic description of the relevant IoT system/service aspects
  • a special focus on their security, interaction, performance, and cost aspects
  • a systematic approach, through a new concept of attack‐defense‐games
  • algorithms to compute optimal strategies and trade‐offs between performance, cost and security
  • a tool to carry out quantitative risk assessment of secure IoT systems
  • a tool to carry out “what‐if” scenario analysis, to harden a secure IoT system’s design and/or operation
  • usability studies and design for usability of the tools within organizations around IoT services
  • design of training material to enforce security policies for employees within these organizations.

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.

Impact

The project will give Danish companies a head-start for the near future when both the EU, the US, and the UK markets will demand security certification for IoT devices.

By applying risk analysis and secure design technologies developed in the project, Danish companies get a fast path to certification of secure IoT devices.

News / coverage

Participants

Project Manager

Jaco van de Pol

Professor

Aarhus University
Department of Computer Science

E: jaco@cs.au.dk

Torkil Clemmensen

Professor

Copenhagen Business School
Department of Digitalization

Qiqi Jiang

Associate Professor

Copenhagen Business School
Department of Digitalization

Kim Guldstrand Larsen

Professor

Aalborg University
Department of Computer Science

René Rydhof Hansen

Associate Professor

Aalborg University
Department of Computer Science

Flemming Nielson

Professor

Technical University of Denmark
DTU Compute

Alberto Lluch Lafuente

Associate Professor

Technical University of Denmark
DTU Compute

Nicola Dragoni

Professor

Technical University of Denmark
DTU Compute

Sean Kauffman

Assistant Professor (Tenure Track)

Aalborg University

Mikael Bisgaard Dahlsen-Jensen

PhD Student

Aarhus University
Department of Computer Science

Alyzia-Maria Konsta

PhD Student

Technical University of Denmark
DTU Compute

Gert Læssøe Mikkelsen

Head of Security Lab

The Alexandra Institute

Laura Lynggaard Nielsen

Senior Anthropologist

The Alexandra Institute

Zaruhi Aslanyan

Security Architect

The Alexandra Institute

Marcia ShiTing Wang

PhD Student

Copenhagen Business School
Department of Digitalization

Anders Qvistgaard Sørensen

R&D Manager

Micro Technic

Jørgen Hartig

CEO & Strategic Advisor

SecuriOT

Claus Riber

Senior Manager
Software Cybersecurity

Beumer Group

Morten Granum

Software Director

Beumer Group

Kristian Baasch Thomsen

Lead Digital Compliance Specialist

Grundfos

Karsten Ries

CEO

Develco Products

Daniel Lux

Chief Technology Officer

Seluxit

Samant Khajuria

Chief Specialist Cybersecurity

Terma

Mads Pii

Chief Technical Officer

Logos Payment Solutions

Tobias Worm Bøgedal

PhD student

Aalborg University

Partners

Categories
Bridge project

HERD: Human-AI Collaboration: Engaging and Controlling Swarms of Robots and Drones

DIREC project

HERD: Human-AI Collaboration

- Engaging and Controlling Swarms of Robots and Drones

Summary

Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. How can we leverage the potential benefits of having multiple robots working in parallel to reduce time to completion? If robots are given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance.  

Together with industrial partners, this project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems.

Project period: 2021-2025
Budget: DKK 17,08 million

Robots and drones take on an increasingly broad set of tasks, such as AgroIntelli’s autonomous farming robot and the drone-based emergency response systems from Robotto. Currently, however, such robots are limited in their capacity to cooperate with one another and with humans. In the case of AgroIntelli, for instance, only one robot can currently be deployed on a field at any time and is unable to respond effectively to the presence of a human-driven tractor or even another farming robot working in the same field. In the future, AgroIntelli wants to leverage the potential benefits of having multiple robots working in parallel on the same field to reduce time to completion. A straightforward way to achieve this is to partition the field into several distinct areas corresponding to the number of robots available and then assign each robot its own area. However, such an approach is inflexible and requires detailed a priori planning. If, instead, the robots were given the task collectively as a swarm, they could potentially coordinate their operation on the fly and adapt based on local conditions to achieve optimal or near-optimal task performance.

Similarly, Robotto’s system architecture currently requires one control unit to manage each deployed drone. In large area search scenarios and operations with complex terrain, the coverage provided by a single drone is insufficient. Multiple drones can provide real-time data on a larger surface area and from multiple perspectives – thereby aiding emergency response teams in their time-critical operations. In the current system, however, additional drones each requires a dedicated operator and control unit. Coordination between operators introduces an overhead and it can become a struggle to maintain a shared understanding of the rapidly evolving situation. There is thus a need to develop control algorithms for drone-to-drone coordination and interfaces that enable high-level management of the swarm from a single control console. The complexity requires advanced interactions to keep the data actionable, simple, and yet support the critical demands of the operation. This challenge is relevant to search & rescue (SAR) as well as other service offerings in the roadmap, including firefighting, inspections, and first responder missions.

For both of our industrial partners, AgroIntelli and Robotto, and for similar companies that are pushing robotics technology toward real-world application, there is a clear unmet need for approaches that enable human operators to effectively engage and control systems composed of multiple autonomous robots. This raises a whole new set of challenges compared to the current paradigm where there is a one-to-one mapping between operator and robot. The operator must be able to interact with the system at the swarm level as a single entity to set mission priorities and constraints, and at the same time, be able to intervene and take control of a single robot or a subset of robots. An emergency responder may, for instance, want to take control over a drone to follow a civilian or a group of personnel close to a search area, while a farmer may wish to reassign one or more of her farming robots to another field.

HERD will build an understanding of the challenges in multi-robot collaboration, and design and evaluate technological solutions that enable end-users to engage and control autonomous multi-robot systems. The project will build on use cases in agriculture and search & rescue supported by the industrial partners’ domain knowledge and robotic hardware. Through the research problems and aims outlined below, we seek to enable the next generation of human-swarm collaboration.

Pre-operation and on-the-fly mission planning for robot swarms: An increase in the number of robots under the user’s control has the potential to lead to faster task completion and/or a higher quality. However, the increase in unit count significantly increases the complexity of both end-user-to-robot communication and coordination between robots. As such, it is critical to support the user in efficient and effective task allocation between robots. We will answer the following research questions: (i) What are the functionalities required for humans to effectively define mission priorities and constraints at the swarm level? (ii) How can robotic systems autonomously divide tasks based on location, context, and capability, and under the constraints defined by the end-user? (iii) How does the use of autonomous multi-robot technologies change existing organizational routines, and which new ones are required?

Situational awareness under uncertainty in multi-robot tasks: Users of AI-driven (multi-)robot systems often wish to simulate robot behaviour across multiple options to determine the best possible approach to the task at hand. Given the context-dependent and algorithm-driven nature of these robots, simulation accuracy can only be achieved up to a limited degree. This inherent uncertainty negatively impacts the user’s ability to make an informed decision on the best approach to task completion. We will support situational awareness in the control of multi-robot systems by studying: (i) How to determine and visualise levels of uncertainty in robot navigation scenarios to optimise user understanding and control? (ii) What are the implications of the digital representation of the operational environment for organizational sensemaking? (iii) How can live, predictive visualisations of multi-robot trajectories and task performance support the steering and directing of robot swarms from afar?

User intervention and control of swarm subsets: Given the potentially (rapidly) changing contexts in which the robots operate, human operators will have to regularly adapt from a predetermined plan for a subset of robots. This raises novel research questions both in terms of robot control, in which the swarm might depend on a sufficient number of nearby robots to maintain communication, and in terms of user interaction, in which accurate robot selection and information overload can quickly raise issues. We will therefore answer the following research questions:

(i) When a user takes low-level control of a single robot or subset of a robot swarm, how should that be done, and how should the rest of the system respond?

(ii) How can the user interfaces help the user to understand the potential impact when they wish to intervene or deviate from the mission plans?

Validation of solutions in real-world applications: Based on the real-world applications of adaptive herbicide spraying by farming robots and search & rescue as provided by our industrial partners, we will validate the solutions developed in the project. While both industrial partners deal with robotic systems, their difference in both application area and technical solution (in-the-air vs. on land) allows us to assess the generalisability and efficiency of our solutions in real-world applications. We will answer the following research questions:

(i) What common solutions should be validated in both scenarios and which domain-specific solutions are relevant in the respective types of scenarios?

(ii) What business and organisational adaptation and innovation are necessary for swarm robotics technology to be successfully adopted in the public sector and in the private sector.

Advances in AI, computer science, and mechatronics mean that robots can be applied to an increasingly broader set of domains. To build the world class computer science research and innovation centres, as per the long-term goal of DIREC, this project focuses on building the competencies necessary to address the complex relationship between humans, artificial intelligence, and autonomous robots.

Scientific value
The project’s scientific value is the development of new methods and techniques to facilitate effective interaction between humans and complex AI systems and the empirical validation in two distinct use cases. The use cases provide opportunities to engage with swarm interactions across varying demands, including domains where careful a priori planning is possible (agricultural context) and chaotic and fast-paced domains (search & rescue with drones). HERD will thus lead to significant contributions in the areas of autonomous multi-robot coordination and human-robot interaction. We expect to publish at least ten rank A research articles and to demonstrate the potential of the developed technologies in concrete real-world applications. This project also gears up the partners to participate in project proposals to the EU Framework Programme on specific topics in agricultural robotics, nature conservation, emergency response, security, and so on, and in general topics related to developing key enabling technologies.

Capacity building
HERD will build and strengthen the research capacity in Denmark directly through the education of three PhDs, and through the collaboration between researchers, domain experts, and end-users that will lead to industrial R&D growth. Denmark has been a thought leader in robotics, innovating how humans collaborate with robots in manufacturing and architecture, e.g. Universal Robots, MiR, Odico, among others. Through HERD, we support not only the named partners in developing and improving their products and services, but the novel collaboration between the academic partners, who have not previously worked together, helps to ensure that the Danish institutes of higher education build the competencies and the workforce that are needed to ensure continued growth in the sectors of robotics and artificial intelligence. HERD will thus contribute to building the capacity required to facilitate effective interaction between end-users and complex AI systems.

Business value
HERD will create business value through the development of technologies that enable end-users to effectively engage and control systems composed of multiple robots. These technologies will significantly increase the value of the industrial partners’ products, since current tasks can be done faster and at a lower cost, and entirely new tasks that require multiple coordinated robots can be addressed. The value increase will, in turn, increase sales and exports. Furthermore, multi-robot systems have numerous potential application domains in addition to those addressed in this project, such as infrastructure inspection, construction, environmental monitoring, and logistics. The inclusion of DTI as partner will directly help explore these opportunities through a broader range of anticipated tech transfer, future market and project possibilities.

Societal value
HERD will create significant societal value and directly contribute to SDGs 1 (no poverty), 2 (zero hunger), 13 (climate action), and 15 (life on land). Increased use of agricultural robots can, for instance, lead to less soil compaction and enable the adoption of precision agriculture techniques, such as mechanical weeding that eliminates the need for pesticides. Similarly, increased use of drones in search & rescue can reduce the time needed to save people in critical situations.

Impact

The project will develop technologies that enable end-users to effectively engage and control systems composed of multiple robots.

Systems composed of multiple robots will significantly increase the value of industrial products, since current tasks can be done faster and at a lower cost, and entirely new tasks that require multiple coordinated robots can be addressed. 

News / coverage

Participants

Project Manager

Anders Lyhne Christensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: andc@mmmi.sdu.dk

Ulrik Pagh Schultz

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Timothy Robert Merritt

Associate Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Ionna Constantiou

Professor

Copenhagen Business School
Department of Digitalization

Kenneth Richard Geipel

Chief Executive Officer

Robotto

Christine Thagaard

Marketing Manager

Robotto

Lars Dalgaard

Head of Section

Danish Technological Institute
Robot Technology

Gareth Edwards

R&D Team Manager

AGROINTELLI A/S

Hans Carstensen

CPO

AGROINTELLI A/S

Maria-Theresa Oanh Hoang

PhD Student

Aalborg University
Department of Computer Science

Alexandra Hettich

PhD Student

Copenhagen Business School
Department of Digitalization

Kasper Grøntved

PhD Student

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Partners

Categories
Bridge project

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

DIREC project

Explain me

- Learning to Collaborate via Explainable AI in Medical Education

Summary

In the Western world, approximately one in ten medical diagnoses is estimated to be incorrect, which results in the patients not getting the right treatment. The explanation may be lack of experience and training on the part of the medical staff.

Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.

Project period: 2021-2025
Budget: DKK 28,44 million

AI is widely deployed in assistive medical technologies, such as image-based diagnosis, to solve highly specific tasks with feasible model optimization. However, AI is rarely designed as a collaborator for the healthcare professionals, but rather as a mechanical substitute for part of a diagnostic workflow. From the AI researcher’s point of view, the goal of development is to beat state-of-the-art on narrow performance parameters, which the AI may solve with superhuman accuracy.

However, for more general problems such as full diagnosis, treatment execution, or explaining the background for a diagnosis, the AI is still not to be trusted. Hence, clinicians do not always perceive AI solutions as helpful in solving their clinical tasks, as they only solve part of the problem sufficiently well. The EXPLAIN-ME initiative seeks to create AIs that help solve the overall general tasks in collaboration with the human health care professional.

To do so, we need not only to provide interpretability in the form of explainable AI models — we need to provide models whose explanations are easy to understand and utilize during the clinician’s workflow. Put simply, we need to provide good explanations.

Unmet technical needs
It is not hard to agree that good explanations are better than bad explanations. In this project, however, we aim to establish methods and collect data that allow us to train and validate the quality of clinical AI explanations in terms of how understandable and useful they are.

AI support should neither distract nor hinder ongoing tasks, giving fluctuating need for AI support, e.g. throughout a surgical procedure. As such, the relevance and utility of AI explanations are highly context- and task-dependent. Through collaboration with Zealand University Hospital we will develop explainable AI (XAI) feedback for human-AI collaboration in static clinical procedures, where data is collected and analyzed independently — e.g. when diagnosing cancer from scans collected beforehand in a different unit.

In collaboration with CAMES and NordSim, we will implement human-AI collaboration in simulation centers used to train clinicians in dynamic clinical procedures, where data is collected on the fly — e.g. for ultrasound scanning of pregnant women, or robotic surgery. We will monitor the clinicians’ behavior and performance as a function of feedback provided by the AI. As there are no actual patients involved in medical simulation, we are also free to provide clinicians with potentially bad explanations, and we may use the clinicians’ responses to freely train and evaluate the AI’s ability to explain.

Unmet clinical needs
In the Western World, medical errors are only exceeded by cancer and heart diseases in the number of fatalities caused. About one in ten diagnoses is estimated to be wrong, resulting in inadequate and even harmful care. Errors occur during clinical practice for several reasons, but most importantly, because clinicians often work alone with minimal expert supervision and support. The EXPLAIN-ME initiative aims to create AI decision support systems that take the role of an experienced mentor providing advice and feedback.

This 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.

Impact

The project will develop explainable AI that can help medical staff make qualified decisions by taking the role as a mentor.

News / coverage

Participants

Project Manager

Aasa Feragen

Professor

Technical University of Denmark
DTU Compute

E: afhar@dtu.dk

Anders Nymark Christensen

Associate Professor

Technical University of Denmark
DTU Compute

Mads Nielsen

Professor

University of Copenhagen
Department of Computer Science

Mikael B. Skov

Professor

Aalborg University
Department of Computer Science

Niels van Berkel

Associate Professor

Aalborg University
Department of Computer Science

Henning Christiansen

Professor

Roskilde University
Department of People and Technology

Jesper Simonsen

Professor

Roskilde University
Department of People and Technology

Henrik Bulskov Styltsvig

Associate Professor

Roskilde University
Department of People and Technology

Martin Tolsgaard

Associate Professor

CAMES Rigshopitalet

Morten Bo Svendsen

Chief Engineer

CAMES Rigshospitalet

Sten Rasmussen

Professor, Head

Department of Clinical Medicine
Aalborg University

Mikkel Lønborg Friis

Director

NordSim
Aalborg University

Nessn Htum Azawi

Associate Professor,
Head of Research Unit & Renal Cancer team

Department of Urology
Zealand University Hospital

Manxi Lin

PhD Student

University of Southern Denmark
DTU Compute

Naja Kathrine Kollerup

PhD Student

Aalborg University
Department of Computer Science

Jakob Ambsdorf

PhD Student

University of Copenhagen
Department of Computer Science

Daniel van Dijk Jacobsen

PhD Student

Roskilde University
Department of People and Technology

Partners