Introduction to Ethical AI

One-day course for people who work closely with models and technical teams

One of the most urgent problems facing Australian business today is developing and deploying artificial intelligence (AI) that is both ethical and trustworthy.

This course is designed for technical staff who use or interact closely with AI and machine learning (ML) systems and who needs to understand the conceptual, theoretical, technical, and organisational challenges in creating ethical AI systems as well as the approaches that begin addressing those challenges.

This instructor-led and guided introductory one-day course is designed for professionals who use or interact closely with models and the technical teams building them, and provides practical insights to AI uses in business in an interactive online class with other students. Participants are provided with training material at the end of the course.

Tuesday 18 May 20219:00 am – 5:00 pm AEST
STANDARD TICKET: $990 per person

Early Bird tickets – $900 per person: Book by 11:59pm on Monday 26 April 2021 to get 10% off the standard ticket price.
Group Bookings – $792 per person: Book two or more people to get 25% off the standard ticket price.
Registrations close at 11:59 pm on Monday 3 May 2021. Listed prices are inclusive of discounting and GST.
Who should take this course
  • Managers of technical teams and people responsible for the oversight of AI systems within an organisation
  • Domain experts, data custodians, and policy staff using or planning to use, machine learning or statistical modelling as part of their work
  • People with a technical background with exposure to machine learning or statistical modelling, and are comfortable interpreting data from a graph and discussing concepts such as averages across different groups and population averages

This course provides a conceptual understanding of what is needed to build AI systems to make automated decisions whilst accounting for ethical objectives. It includes presentations, discussions, a group project, and a hands-on interactive exercise.

What you will learn
Automated Decision Making
  • Core concepts underlying how ML systems operate and implications for ethics
  • The “Learn from data” ML paradigm
  • How to specify objectives/intent in an ML system
  • Understanding uncertainty in predictions from automated decision-making systems
  • Classification and regression
AI System Governance
  • How AI systems require precise, mathematical objectives
  • How objectives and intent are encoded in data-driven decision-making systems
  • The impact data-driven decision-making systems choices have on ethical outcomes
  • Encoding values in loss functions
  • The role of data in specifying objectives
  • Predicting and making decisions based on probabilities.
Algorithmic Bias and Fair Machine Learning
  • How ‘neutral’ algorithms nevertheless systematically disadvantage certain groups in society
  • How bias can arise in automated decision-making systems
  • Approaches to detecting and mitigating algorithmic bias
  • What ‘fair or unbiased’ can mean in AI; and what it ought to mean
  • Sources of bias in automated systems
  • Detecting and mitigating algorithmic bias
Interpretability, Transparency and Accountability
  • The three critical ingredients of an ethical AI system
  • Why it’s not always clear what it a ‘transparent’ model means
  • How improving interpretability drives more ethical results
  • Tools and techniques to make AI more transparent/interpretable
  • How tools can satisfy the underlying rationale of transparency
  • Motivation and audience for transparency and interpretability
  • Understanding what information an ML system is leveraging
  • The limitations of interpretability
Course format

This course is live, remotely delivered by two instructors. We replicate a classroom setting by using video-conferencing, live chat, and ‘break-out rooms’. At the start of each topic, there’s a presentation of key concepts followed by class discussion. Concepts are then explored in interactive exercises using Jupyter notebooks with one-on-one guidance by the tutors. The presentation material and notebook solutions are provided as reference material.

To participate in the course, you will need:

  • Reliable internet connection
  • Computer with a webcam, microphone, and audio
  • Current version of Firefox, Chrome/Chromium, Safari, or Edge browsers
  • Access to Microsoft Teams and from your network

Information about how to join the course and will be sent to you closer to the date of the course.

Questions? Email us, or ask us about tailoring this course for your organisation.

Terms and Conditions: By registering for this event, you accept the terms and conditions of Gradient Institute, an independent non-profit that researches, designs, and develops ethical AI systems. All published ticket prices are in Australian dollars, and the course will be taught in English.