Mohammad Naiseh - Projects

PRESERVE: Using big data to support criminal investigations

01 Sep 24 - 31 August 27

PRESERVE aims to improve the daily investigative and preventive work of Law Enforcement Authorities (LEAs), enhance proactive threat detection and response, and provide a regulatory-compliant framework for the responsible use of AI in crime detection and prevention. The €6 million project, funded by the European Commission’s Horizon Europe programme, involves partners from 8 countries across Europe – including researchers, law enforcement agencies from Greece, Spain, Romania and Czechia, and industrial partners with expertise in technology and software development. The €6 million project, funded by the European Commission’s Horizon Europe programme, involves partners from 8 countries across Europe – including researchers, law enforcement agencies from Greece, Spain, Romania and Czechia, and industrial partners with expertise in technology and software development.

Project Lead: Prof. Hamid Bouchachia (PI)

Co-I - Dr. Mohammad Naiseh, Dr. Hammadi Nait Charif

Project Partners: Center for Security Studies (Greece), Università Degli Studi Bari (Italy) and GRADIANT (Spain)

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Communicating Liability in Autonomous Vehicles

01 Sep 22 - 31 August 23

In autonomous vehicles (AVs), factors affecting performance, such as loss of control, operational limits and unforeseen circumstances mean that liability is, and will remain, a point of contention among users, manufacturers, insurers and policymakers. Establishing better driver education and human-machine interaction is essential in aligning perceptions of risk and liability to avoid threats to safety, resources, and/or reputation. This project examines how liability is perceived and communicated between AVs’ drivers, and 3rd-party insurers, and aims to develop integrated and commonly agreed-on mental models that help each stakeholder in their risks and responsibilities of vehicle control.

Project Lead: Dr. Mohammad Naiseh (PI)

Co-I - Dr. Rychard Hyde, Dr. Paurav Shukla, Dr. Justyna Lisinska

Project Partners: Johns Hopkins University, Ultra Leap, Connected Places Catapult, Center for Connected and Autonomous Vehicles

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REFORMIST: Mirrored decision support fRamEwork FOR Multidisciplinary Teams in Oesophageal cancer

01 Apr 23 - 31 May 24

The Upper Gastrointestinal (UGI) multidisciplinary Multidisciplinary team (MDT) makes critical treatment decisions in every oesophageal cancer patient’s journey (e.g., neoadjuvant therapies, expedited surgery or palliation). This has a profound impact on survival and quality of life. Recent studies have highlighted that rising caseloads and time pressures can lead to inconsistent and suboptimal decisions. Machine learning (ML) offers the potential to standardise and deliver data-driven decisions. We will mirror the decision-making process by incorporating human-selected critical variables and work with practitioners to assess regulatory and practical challenges (as human-AI partnerships) to ensure that the solution is trustworthy for routine clinical use.

Project Lead: Dr. Ganesh Vigneswaran (PI)

Co-I - Dr. Mohammad Naiseh, Prof. Gopal Ramchurn, Dr. Zoe Walters, Prof. Tim Underwood

Project Partners: University Hospital Southampton and Unversity College London

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XHS: eXplainable Human-swarm Systems

01 Arpil 22 - 31 March 23

In swarm robotics, agents interact through local roles to solve complex tasks beyond an individual’s ability. Even though swarms are capable of carrying out some operations without the need for human intervention, many safety-critical applications still call for human operators to control and monitor the swarm. There are novel challenges to effective Human-Swarm Interaction (HSI) that are only beginning to be addressed. Explainability is one factor that can facilitate effective and trustworthy HSI and improve the overall performance of the Human-Swarm team. Explainability was studied across various human-AI domains, such as Human-Robot Interaction and Human-Centered ML. However, it is still ambiguous whether explanations studied in Human-AI literature would be beneficial in Human-Swarm research and development. Furthermore, the literature lacks foundational research on the prerequisites for explainability requirements in swarm robotics, i.e., what kind of questions an explainable swarm is expected to answer, and what types of explanations a swarm is expected to generate. In this project, we explore these fundamental challenges in human-swarm environments.

Project Lead: Dr. Mohammad Divband Soorati (PI)

Co-I - Dr. Mohammad Naiseh, Dr. Gopal Ramchurn, Dr. Kaite Parnell

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Inclusive autonomous vehicles: the role of human risks perception and trust narratives

01 Feb 2021 - 31 Jan 2022

With AI for autonomous vehicles evolving from limited responsibility to fully autonomous driving, this project posits that each level of AI control handover triggers different levels of risk perceptions and trust concerns as it involves varying levels of consumer commitment and relinquishing of control.Grounded in the theories of social psychology and behavioural economics, this project will uncover how concerns with AI can be alleviated at each level. In so doing, we will investigate the mechanisms that can address consumers’ concerns and increase their overall trust and well-being. The project will help in managing evolution for inclusive human-AI interactions.

Project Lead: Prof. Paurav Shukla (PI)

Researchers - Dr. Mohammad Naiseh, Dr. Jediah Clark, Dr. Liz Dowthwaite

Project Partners: Catapult Connected Places

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Intersectional Approaches To Design And Deployment Of Trustworthy Autonomous Systems

01 Apr 22 - 31 Mar 23

This project explores how intersectional approaches can inform the design and deployment of TAS towards the creation of an inclusive, fair and just world. Intersectionality is a growing area of research and practice that can address issues around equity in TAS due to the unique way in which it understands and engages with both individual and structural aspects of inequality, discrimination and injustice within systems of oppression (racism, sexism, classism). Our research aims to investigate how intersectional frameworks are produced, drawn upon and can be applied within the design and deployment of TAS. We focus on the health and maritime sectors to address a range of individual, technical, systemic, place-based, professional, cultural and institutional issues from an intersectional perspective.

Project Lead: Dr. Caitlin Bentley (PI) -

Co-I - Dr. Mohammad Naiseh, Dr. Laura Sbaffi, Dr. Efbraxia Zamani

Project Partners: NHS, University of Westminister, Maritime and Coastguard Agency

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