Module Directory

This course explores the ethical dimensions, societal implications, and practical considerations of implementing Artificial Intelligence (AI) in the organisations and analytics context. This module focuses on key themes such as fairness, transparency, privacy, and accountability, while examining fundamental concepts like algorithmic fairness, transparency, and ethical decision-making. Engaging with debates around AI autonomy, bias mitigation, and regulatory frameworks, students will develop a comprehensive understanding of the responsible AI landscape.

The aims of this module are:

• To introduce students to foundational principles, modern tools, and methodologies in Responsible AI, emphasising ethical considerations, fairness, and transparency.


• To provide students with a theoretical understanding and hands-on proficiency in deploying Responsible AI technologies within organisational settings.


• To enable students to confidently apply Responsible AI techniques for decision support in various organisational contexts, across diverse industries.

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

1. Demonstrate a critical understanding of relevant theories, concepts, and methdologies in Responsible Artifical Intelligence (AI), enabling them to analyse challenges relevant to AI in organisations and devise ethically sound solutions.


2. Demonstrate a comprehensive understanding of the roles and impact of different types of AI systems in supporting decision-making processes within businesses while considering ethical implications.


3. Identify, explain, and evaluate the potential of Responsible AI in supporting complex decision-making processes within real-world business environments, with a focus on ethical considerations and societal impact.

Transferable Skills

1. Critically analyse a wide range of issues related to AI systems and the responsible use of intelligence in these systems, considering societal impacts and ethical frameworks.


2. Scrutinize and critically analyse the challenges associated with the responsible implementation of AI, identifying potential ethical dilemmas and proposing strategies for responsible deployment.


3. Evaluate and demonstrate a critical understanding of the role played by Responsible AI in businesses, emphasising ethical considerations and its influence on organisational decision-making processes. 

In this module students will be introduced to:

Syllabus information

• Understanding the role of AI in modern business decision-making
  
  
  
  • Exploring the foundations of AI
  
  
  • Exploring growing applications of AI in decision making
  
  
  
  

• AI; Transparency and explainability
  
  
  
  • Importance of transparent AI systems
  
  
  • Exploring the methods for explaining complex AI models to stakeholders
  
  
  
  

• Privacy, Data Protection and Governance in AI
  
  
  
  • Understanding the debates around the ethical use of data in AI applications
  
  
  • Exploring best practices for ensuring privacy in AI-driven initiatives
  
  
  
  

• AI in Society - Debates and Implications
  
  
  
  • Engaging in debates on autonomy, bias mitigation, and AI regulation
  
  
  • Exploring the societal impact of AI
  
  
  
  

This module will be delivered via:

1. One 1-hour seminar per week
2. One 1-hour lecture per week

The following learning and teaching methods will inform the pedagogic structure of the course: lectures, case studies, class exercises, group work, and signposting to other resources and support.

Students will be encouraged and required to refer to a wide range of resources covering textbooks and academic peer reviewed journal articles, to build an understanding of theoretical concepts and refer to online platforms to follow current trends and practices concerning business analytics and decision-making tools.

The lectures will be developed around key concepts as mentioned in the indicative module content and will use a range of examples and cases from practice to demonstrate the application of theoretical concepts.