Find us +44 (0)113 2837100
On successful completion of the module, students will be able to:
Demonstrate systematic knowledge and comprehensive understanding of the phenomena of ethics, transparency and accountability in computing-related situations
Demonstrate an ability to investigate, analyse, present and discuss complex ethical issues affecting professionals working in AI and data science
Develop awareness of the social impact of IT and the inter-relationship between social, ethical, professional and legal perspectives to issues that arise from the use of AI and data science
Reflect on the skills relevant to the role of a computer professional working in situations with legal and ethical implications
Collaborate effectively in a team and group discussions to present ethical solutions to problems that arise from the use of technology.
This module helps students develop an in-depth understanding of the ethical and social implications of computing. The course is designed to equip students with the knowledge, skills and tools needed to identify and address ethical challenges that arise in the design, development and use of computer systems.
The module covers topics such as responsible conduct of research, intellectual property, privacy, security and accessibility. Students will explore case studies and engage in group discussions to analyse real-world ethical dilemmas in AI and data science and propose sustainable solutions such as sustainable IT and digital environmental sustainability ethics. In addition to ethical issues, the course also emphasises the importance of accountability and transparency in building intelligent systems. Students will learn how to design and implement systems that are accountable to their users and stakeholders, and how to ensure transparency in decision-making processes.
In order to prepare students for the Project module, research ethics topics around ethical considerations in conducting research involving human subjects, animals and data will be part of the module curriculum.
Throughout the module, students will develop critical thinking skills, ethical reasoning and communication skills, which are essential for ethical leadership in the computing industry. Upon completion, students will be able to apply ethical principles, accountability and transparency concepts to complex computing scenarios and make sound ethical decisions.
The teaching and learning methods for this module are designed to be interactive, engaging, and collaborative. The module uses a variety of teaching methods within the workshop format to foster active learning and promote critical thinking. These methods include:
Lectures: Lectures are delivered by subject matter experts to provide an overview of the course content and introduce key concepts, theories and practices related to ethics, accountability and transparency in computer science. Lectures are supplemented by reading materials and resources, which are made available to students in advance of the class.
Case studies: The module employs case studies to facilitate analysis and discussion of real-world ethical challenges in computing. Case studies are selected from a range of contexts, including academic research, industry, government and non-profit organisations. Students work in small groups to analyse case studies and propose solutions to ethical dilemmas.
Group discussions: Group discussions are a key component of the course, providing an opportunity for students to share their perspectives and engage in dialogue about ethical issues related to computing. Discussions are facilitated by the module tutor and students are encouraged to participate actively and respectfully.
Tasks: The course includes a range of individual and group tasks, designed to assess students' understanding of course content and their ability to apply ethical principles, accountability and transparency concepts to computing scenarios. Tasks may include written essays, case analyses and presentations.
Peer feedback: Peer feedback is used to promote collaborative learning and provide constructive criticism to improve the quality of students' work. Students are encouraged to provide feedback to their peers and to reflect on the feedback they receive.
Guest speakers: Guest speakers, including experts from academia, industry and government, are invited to share their experiences and perspectives on ethical challenges in computer science. Guest speakers also provide an opportunity for students to network with professionals in the field.
The module is designed to be inclusive, promoting diversity and encouraging active participation from all students. The teaching and learning approach aims to develop students' critical thinking skills, ethical reasoning and communication skills, which are essential for ethical leadership in the computing industry.
Workshops
Hours: 40
Intended Group Size: 50
Guided independent study
Hours: 260
Further details relating to assessment
Group presentation: Students are presented with a real-world case study around the use of data science and AI to solve a political or/and social issue, for which they must investigate the legal and ethical implications and the phenomena of transparency and accountability in AI and data science related solutions. Students will need to work in groups and present their findings and communicate insights driven by data and their implications on society and the wider community.
Reflective report: A reflective account of the work process and individual contributions completed independently by each group member. Each student should reflect critically on the decisions taken by the group and on their own contributions, with reference to personal strengths and areas for development.
In this module, formative assessment will be used to support the skills that contribute to the assessment. It may take the form of group discussions, peer assessment, quizzes and/or concepts maps. Formative feedback will be an ongoing process within class sessions.
Students should refer to the Module Handbook for further details on the module learning, teaching and assessment strategies.
Module Coordinator - PRS_CODE=
Level - 7
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 7T2