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Robotic and Autonomous Systems Safety (RASS)


This course aims to:

In this module, we will consider the challenges posed to safety engineering techniques and praxis by Robotics and Autonomous Systems in three broad areas: technical, engineering and social. Among the technical challenges, we will explore the nature of decision-making technologies and will consider the implications for data management, model learning, verification and deployment and understanding of the interaction between AS and the 'outside world', including humans. Engineering challenges include the elicitation and validation of safety requirements, identifying and analysing new classes of hazard and understanding how failures propagate in systems with an autonomous component, implications for incident report and investigation etc.. Social challenges include the role and expectations of the human in interactions with RAS, ethical concerns, acceptance and communication of risk and challenges for the law, governance and regulatory regimes in a number of domains. Implications for the safety case, particularly with reference to machine understanding and decision-making, will be considered throughout the module.

The module will be taught in a blended fashion, using a combination of pre-recorded lectures and live exercises sessions in which students will be taught in small groups. After the taught part of the module, students will select a topic and conduct a short critical literature review (formative). They will use this as a basis for a short talk, in a small group session, on which they will receive feedback both from other members of the group and from the course tutor. There will also be an open assessment (summative), undertaken over 7 weeks following the taught part of the module.

By the end of this module, students will be able to:

  • Identify and describe the disruptors - technical, engineering and social - to existing system safety engineering practices generated by RASS.
  • Describe and evaluate the implications for and changes required in safety assessment and assurance practices to accommodate RASS and emerging technologies such as ML.
  • Communicate consistently and clearly concepts and issues relating to RASS engineering and safety.
  • Identify the societal and regulatory impact of RASS and implications for risk acceptance in a range of safety-critical domains.
  • Develop a safety case for RASS.

Who is the course for?

This course is suitable for:

  • Practitioners across all domains including aerospace, military, railway, automotive, civil nuclear, civil maritime, medical devices and healthcare;
  • Developers of equipment safety cases during design for software, hardware, procedures, systems and/or platforms;
  • Developers of safety cases for operational safety and disposal;
  • Reviewers of safety cases within an organisation or as an independent activity;
  • Developers and reviewers of changes to existing safety-critical / safety-related equipment and operations;
  • Project managers where development of a safety case is a significant element of projects they manage;
  • Regulators of safety critical domains.


A basic understanding of system safety terminology and lifecycle via prior learning or industrial experience. It is useful for you to have taken our Foundations of System Safety Engineering course.

No prior knowledge of Robotics and Autonomous Systems (RAS) is required for this module: we will provide an introduction to the technologies to enable your understanding of the safety aspects covered in the module.

If you are unsure about your previous experience, please email the MSc SCSE and Short Courses Team at so that we can assess your suitability for this course.

How is the course taught?

During your course, you will have full access to the benefits of the York approach, with experienced and knowledgeable lecturing staff on hand throughout the week, as well as the opportunity to gain insights from your industry peers.
During the 2024/25 academic year courses will be taught in a blended format. There will be three days of face-to-face teaching in York, taking place on Tuesday to Thursday of the teaching week. In addition students will be provided with self-study materials totaling some 10 - 15 hours of study time. These will be split between work to be completed before the in-York sessions and materials to be studied afterwards. 

During the teaching week there will be a combination of lecture materials and case studies to explore. The case studies give you the chance to work through an example to reinforce your learning from the lectures.
Each course ends with an optional assessed exercise that is undertaken away from the University over 5 weeks following the taught element of the module. Each assessment takes approximately 65 hours in addition to the scheduled teaching time, of which we estimate students spend 30 hours undertaking private study plus 35 hours writing up the assessment. All assessed exercises are open (so you won't take an exam in supervised conditions), and comprise a report, case study, or documented piece of software.
If you choose to take the assessment and you pass, your results can count towards the completion of one of our postgraduate awards: 

You should apply for your Masters, Diploma or Certificate award after taking no more than 40 credits of modules. All components of your chosen postgraduate award, including modules taken as SCSE short courses, must be taken within a five year time period.

When will this course be taught?

The key dates for this course in the 2024/25 academic year are as follows:

Registration closes: Friday 04/04/2025

Preparatory materials released: Thursday 17/04/2025

Face-to-face teaching: Tuesday 29/04/2025 - Thursday 01/05/2025

Recommended reading


  • Russell, S.J. and Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.
  • Marcus, G. and Davis, E., 2019. Rebooting AI: Building artificial intelligence we can trust. Pantheon.  
  • Goodfellow, I., et. al., 2016. Deep learning (Vol. 1). Cambridge: MIT press.
  • Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.
  • Topol, Eric. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK, 2019.


  • Liu, Yun, et al. "How to read articles that use machine learning: users’ guides to the medical literature." Jama 322.18 (2019): 1806-1816.
  • Chen, Po-Hsuan Cameron, Yun Liu, and Lily Peng. "How to develop machine learning models for healthcare." Nature materials 18.5 (2019): 410.


Booking conditions

Booking Conditions

  • Acceptance onto a short course is at the agreement of the course leader. They will want to assure themselves that you have the relevant level of background knowledge. You may therefore be asked to provide a CV detailing your knowledge / experience in particular areas.
  • Course fees quoted include all relevant course materials, tuition and examinations, lunch and refreshments.
  • A completed booking form with Purchase Order or payment is required no later than one month before the course starts, or immediately for bookings made within one month of the beginning of the course.
  • Fees are payable to The University of York. 
  • Payment can also be made by credit or debit card. 


We regret that a fee must be charged when confirmed bookings are cancelled or transferred to future dates. In the event of a cancellation, you may nominate a substitute
(acceptance of this substitution is subject to academic and availability conditions). If a suitable substitute cannot be found the following scale of charges will apply:

  • 56 days or more before the programme starts ‐ full refund
  • 55 days or less ‐ 50% refund
  • 28 days or less ‐ 25% refund
  • 14 days or less ‐ no refund

Transfers to a postgraduate award

Students who attend any short course(s), and subsequently choose to undertake a full MScCertificate or Diploma, will be entitled to some credit of the fees already paid, which can be used towards the cost of the full award. The credit a student is entitled to is calculated as follows:

Module Credit % of the fees already paid
1st module 85%
2nd module 75%
3rd module 70%
4th module 70%

You should apply for your chosen postgraduate award after taking no more than 40 credits of modules. All components, including modules taken as SCSE short courses, must be taken within a five year time period.

We reserve the right to amend published information.

Book your place

Before you make your booking, please ensure that you have read our Booking Conditions.

Enrolling on your course

Please complete the CPD Booking Form and return it to Once this form has been processed, you will be able to use e:Vision to access your student record. Please include your name in the 'subject' of your email.

Paying for your course

If your employer will be paying for your training and you would like us to raise an invoice, please complete the CPD Payment Form and return it to Please include your name in the 'subject' of your email.

If you wish to pay by credit or debit card, click the 'pay online' button, below.

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Contact us

MSc SCSE and Short Courses Team