As the use of Artificial Intelligence continues to rise, so do the questions surrounding the ethicality of the technology. According to a recent study by IBM, executives ranking AI ethics as important jumped from less than 50% in 2018 to nearly 75% in 2021.
This course empowers learners to approach and apply ethical AI, teaching them to design and build models with fairness and limited bias. Students will gain ethical AI literacy skills, enabling more meaningful discussions across AI disciplines and applying ethical principles to their organisations.
This Ethics of Artificial Intelligence course will be delivered to you in partnership with Udacity, meaning you'll have access to both Udacity's learning and career services as well as RMIT Online's course enablement support through our Learner Success team.
This Ethical AI course is ideal for:
- Data specialists and AI practitioners who want to deepen their understanding of the ethical implications and challenges associated with AI development and deployment.
- Aspiring AI policy, and risk professionals interested in the societal and policy aspects of AI, such as AI ethics consultants, policymakers, and strategists.
By the end of this course, you'll be able to:
- Design an AI model applying ethical considerations
- Quantify potentially harmful impacts of AI within the model and apply bias mitigation techniques
- Communicate ethical considerations of AI for a business use case
During this course, you will have the opportunity to demonstrate your newly acquired Ethical AI skills through a final project. Your final project will be to use AI ethics for personalised budget prediction.
After completing this course, you’ll walk away with your own AI application and an RMIT credential which can be validated, recognised, and shared on social media platforms.
For Ethical AI, students should have experience working with and/or knowledge of the following topics:
- Identify and articulate the popular use cases of AI systems in society, such as an autonomous vehicles, smart voice assistants, and robots
- Create a machine learning model, such as a linear or logistic regression model, naive Bayes classifier, or neural network using the scikit-learn framework.
- Perform basic data parsing and visualisation activities, including using pandas data frames and visualisations libraries such as matplotlib.
- Create efficient scripts using Python 3.7.6 or higher versions using variables, functions and common data types.
- Articulate fundamental concepts on AI lifecycle phases (e.g., training and deployment), the inputs to AI systems (e.g. image, text, tubular), and the outputs/results of a typical AI system (e.g, predictions, interferences).
