Module overview
This course focuses on the ethical integration and applications of Artificial Intelligence (AI) in business settings, with a specific emphasis on the challenges and opportunities related to algorithmic bias, inequalities, and diversity management as part of social sustainability. Taking a holistic approach to sustainability, students will explore the theoretical concepts and practical applications to understand how AI technologies can perpetuate or mitigate biases, create environmental issues and how this is all linked to economic sustainability. Based on critical evaluation of case studies regarding AI-induced inequalities, students will learn how to develop strategies for managing diversity and apply ethical frameworks to AI development and implementation.
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Apply critical thinking and problem-solving skills in various contexts, especially in evaluating and addressing AI-induced inequalities.
- Communicate complex ideas effectively in written and oral formats, particularly regarding ethical AI.
- Challenge conventional business thinking and leadership approaches for a fair, inclusive and progressive application of AI in business and organisational contexts.
- Collaborate with others to achieve common goals, demonstrating teamwork and leadership in managing diversity and AI projects.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The ethical integration and applications of Artificial Intelligence (AI) in business settings. The challenges and opportunities related to algorithmic bias, inequalities, and diversity management as part of social sustainability.
- The linkage between AI technologies and economic sustainability.
- Ethical principles of AI and developing regulatory frameworks.
- Theoretical concepts and practical applications to understand how AI technologies can perpetuate or mitigate biases and create environmental issues.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically Analyse AI Sustainability Applications from the perspectives of leadership, embedding all pillars of sustainability in business development and organisational transformation
- Evaluate alternative approaches and technologies to stainability and AI
- demonstrate a strong understanding of the ethical dilemmas for AI and how sustainability leadership can help to resolve them
Syllabus
The exact topics covered in this module will depend on the configuration of the team of tutors and their respective research areas within strategy and innovation management. The module may include, but is not limited to, the following topics:
AI and Sustainable Development Goals: How AI can contribute to achieving global sustainability targets.
Ethical Frameworks and Principles for AI: Examination of the core ethical principles guiding AI development and deployment.
AI for Environmental Sustainability: Applications of AI in monitoring and mitigating environmental impacts.
Bias and Fairness in AI: Strategies for identifying and mitigating bias in AI algorithms.
Transparency and Accountability: Ensuring AI systems are transparent and accountable to stakeholders.
Learning and Teaching
Teaching and learning methods
Teaching methods include:
Lectures, interactive case studies, simulation game, directed reading, and private/guided study.
Learning activities include:
• Introductory lectures
• A groupwork: presentation
• Case study/problem solving activities
• Private study: argumentative essay
• Use of video and online materials
Class activities, such as problem solving activities, discussions and use of case studies will provide opportunities for you to gain feedback from you tutor and/or peers about their level of understanding and knowledge prior to any formal summative assessment.
| Type | Hours |
|---|---|
| Lecture | 24 |
| Total study time | 24 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Final Report | 80% |
| Group Assignment | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Final Report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Final Report | 100% |