Ethical AI training opportunity
Enjoy a 50% subsidy on this top-quality course and explore the ethical issues surrounding AI.
To register your interest, take 5 minutes to complete our Expression of Interest form this will help us ensure that you are a good fit for the training and will enjoy the best possible experience.
The Digital Health CRC welcomes EOI’s from people in Australia who are health sector workers or university students.
Students with The Digital Health CRC or who attend a partner university will be eligible to have all or part of the course fee subsidised if their EOI is successful.
Learn with Gradient Institute
The Digital Health CRC has partnered with the non-profit Gradient Institute, who bring a unique combination of cutting-edge research in ethical AI and hands-on knowledge of technical application, to deliver this technical training.
Deadline for EOI submissions: 01/11/20
Curriculum and instruction provided by Gradient Institute | Available to you with a 50% subsidy from the DHCRC
Data Scientist’s Introduction to Ethical AI
Date: Tuesday 17th November and Wednesday 18th November
Time: 9:00 am – 5:00 pm (AEDT) both days
Standard price: $1760
DHCRC special price: $880.00 inc. GST
Early bird rate: $800.00 if you register by 11th October
Having explored example systems that use machine learning to make automated decisions whilst accounting for ethical objectives, you will have gained an understanding of the technical pitfalls that prevent machine learning systems from behaving ethically, as well as how to identify and correct for them.
This course is for people who have experience building data-driven models; and, are comfortable building statistical models, reading equations and chatting about terms such as “parameter optimisation”, “overfitting” and “model validation”. Example roles include:
• Data Scientists
• Business Analysts
• Software Developer
• Machine learning engineer
Outline of Topics
- Automated decision making: We provide a review of the foundations of machine learning and model validation, with an emphasis on ensuring a strong conceptual understanding and the ethical implications of algorithmic decision making.
- Loss functions and robust modeling: We investigate the choice of loss function, including what considerations to omit and include, as the primary mechanism of control designers have over the ethical operation of the system. We examine the design choices and assumptions such as encoding values in loss functions, cost-sensitive classification, calibration, and decision making based on predicted probabilities and dataset shift.
- Causal versus predictive models: Failure to consider causality can lead to poor consequences despite good intentions. We clarify the distinction between causal and predictive models and how they can be used & interpreted: identifying when a causal model is required and understanding Simpson’s Paradox.
- Fair machine learning: We examine some of the common notions of algorithmic fairness that attempt to measure and correct for such disparate treatment or outcome in ML systems.
- Interpretability, transparency & accountability: We provide an introduction to some of the tools and techniques available for making models more interpretable and transparent and discuss how to communicate key information about model behaviour and ethical risks to those ultimately accountable for the system.
- An applied project that will give you the opportunity to put the concepts learned into practice. During the project, you will work in teams to analyse an algorithmic system, identify potential ethical issues, propose solutions, and present the results to the group at the end of the day.
The course is run in groups of up to fifteen students with two Gradient Institute instructors present throughout, to lead the course and answer any questions. The instructors are members of our team of machine learning practitioners all with more than a decade of experience designing and implementing consequential AI systems.
To ensure everyone’s safety, we are currently delivering our courses via remote access to respect social distancing measures. We look forward to being able to offer courses face-to-face once it is safe to do so.
We’ve formatted our online courses to replicate a classroom. We use video-conferencing for our presentation and linked break-out rooms for project work. You’ll be able to interact with our instructors and ask them questions through this and via an audio and text chat app, so that you can do the same things you would in face-to-face, such as ask questions and get to know your fellow students. You will access exercises, interactive models, and visualisations in a Jupyter notebook.
To participate in the course, you will need:
- A reliable computer with a webcam, microphone and headphone/speakers
- Reliable internet access
More information about systems requirements will be sent out closer to the course.
Expressions of interest close Sunday 1st November. Early-bird discount for EOI’s received on or before Sunday 11th October.