This eCourse consists of three modules. Module 1 focuses on the key ethical issues surrounding the use of AI algorithms, such as bias/discrimination and data privacy and security, and the regulatory and industry response in that regard. The field of AI has developed largely disconnected from ethical concerns. As AI has advanced and its applications have proliferated, the need for ethics has become palpable. Time and again, AI has resulted in unnecessary harms that could have been prevented if the technology had been designed and deployed with ethics in mind.

Module 2 looks in detail at the issue of biases in AI algorithms and when such biases amount to discrimination. It also examines the potential solutions to avoiding these biases.

Module 3 looks at the importance of ensuring that data is kept private and secure from an ethical viewpoint. Data privacy and security is an ethical issue because the lack of it leads to wrongs, harms, and risks for individuals, institutions, and society at large. These issues can occur as a result of a hack, a leak, or a data transfer, among other ways.

On completion of this course, you will be able to:
- Identify the key areas of concern in relation to AI ethics, namely bias and discrimination and data privacy and security
- Recognise how regulators are responding to these concerns in terms of both technology-independent regulations and AI-specific regulations
- Identify the benefits and limitations of AI ethics codes
- Identify the main sources of bias in AI and recognise when bias can become discrimination
- Identify some possible solutions to the problem of bias in AI
- Recognise through a case study how AI biases have created issues in the world of banking
- Identify the reasons why data privacy and security is a concern from an ethical perspective
- Identify some solutions for ensuring that personal data is kept private and secure
- Recognise through a case study how data breaches can have very serious consequences

Module 1 - AI Ethics - Key Issues
Topic 1: Areas of Concern
Topic 2: Regulatory Response
Topic 3: AI Ethics Codes

Module 2 - AI Ethics - Bias & Discrimination
Topic 1: Bias & Discrimination in AI Algorithms
Topic 2: Solutions to AI Bias Problems
Topic 3: Case Study

Module 3 - AI Ethics - Data Privacy & Security
Topic 1: Data Privacy & Security in an Ethical Context
Topic 2: Ensuring Data Privacy & Security
Topic 3: Case Study