This two-day course aims to develop the technical skills necessary for building systems that use machine learning to make automated decisions whilst accounting for ethical objectives. The course includes presentations, discussions, a group project, and hands-on interactive exercise modules which will allow the new concepts from the course to be consolidated.

Outcomes

By the end of the course, participants will have explored simple example systems that use machine learning to make automated decisions whilst accounting for ethical objectives. Participants will understand and explore some of the technical pitfalls that prevent machine learning systems from behaving ethically, and how to identify and correct for them.

Note: While many of the concepts discussed in this course are applicable to a wide range of AI systems, the content primarily focuses on models built using structured and labeled training data.

Prerequisites

This course is for people who have experience building data-driven models, interpreting graphs, and chatting about terms such as “parameter optimisation”, “overfitting” and “model validation”. Exercises and activities are based on interactive models and visualisation.

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.

Visit our website for the full course description and more information about our organisation.

Some feedback from our recent attendees

'The biggest appreciation I have is the emphasis of what the responsibility of a data scientist is. i.e. not to choose a set of metrics and the approach to get the best model, but to explain all the risks/choices to decision makers so they can make the most informed decisions. This means being competent technically but also able to consider all the major ethical risks in advance of project creation.'

'Seriously – well done, as mentioned the calibre was high, it felt rather authentic and each of the presenters were passionate in their field. I truly loathe online sessions where it’s text book module approach and really don’t care who’s listening/how engaged. Your team truly did a great job and should be commended for being so engaging in the sessions.'

Instructors

Two Gradient Institute members, from our team of researchers and course developers, are present throughout the course to lead and answer any questions. Find out more about our team on our people page.

Course format

This is an online course with instructors joining live available for the duration of the course.

Remote meetings can be challenging, so we’ve done our best to replicate a live classroom with video-conferencing, live chat, and break-out rooms. At the beginning of each topic, there will be a short presentation and discussion introducing key concepts. To explore the concepts in greater depth, participants work through interactive exercises in Jupyter notebooks, supported by one-on-one guidance from the tutors. The notebook solutions and the presentation material will be provided after the course as a reference.

To participate in the course, you will need:

a computer with a webcam, microphone and audio
a reliable internet connection
a current version of Firefox, Chrome/Chromium, Safari or Edge
the ability to access Microsoft Teams and gradientinstitute.org from your network

More information about systems requirements will be sent out closer to the course.

Send an email to training@gradientinstitute.org if you have any questions or to discuss a corporate course for your organisation.

Tickets

Register no later than 11:59 PM Monday 3/05/2021 to save 10% of the ticket price.

Save 25% when you register yourself and one or more colleagues.

Registrations close 11:59 PM Monday 10/05/2021. Listed prices are inclusive of discounting and GST.

Contact the host of Data Scientist Introduction to Ethical AI - May 2021