Artificial Intelligence and Machine Learning in Public Sector
2-day Training | 19 & 20 October 2022 | 10 AM – 2 PM AEST | Online
Training Overview
A Tool-Kit to Uplift Capability, Systems, and Governance for Impactful, Sustainable Outcomes
Artificial intelligence and machine learning have proven to be powerful cross-functional tools in the public sector. From improving operational efficiencies, providing valuable insights, and uplifting service delivery and customer experience – AI, ML, and Data Modelling are quickly becoming a core competency in government agencies. On the flip side. It has been estimated that 85% of AI projects will fail and deliver erroneous outcomes through 2022, largely due to biases in the data, algorithms, or people managing them. This statistic won’t be a surprise to most data scientists in the public sector. Algorithms are no silver bullet and their success depends wholly on the quality of your data sets.
Through an innovative mix of lecture-style presentations, interactive group discussion, and expert feedback, the ‘Artificial Intelligence and Machine Learning in Public Sector’ 2-day training course will focus on three core elements of scaling and future-proofing AI capability: Systems & Processes, Ethics & Governance, and Collaboration.
Learning Outcomes
Meet Your Facilitator
Dr. Mahendra Samarawickrama,
Director, The Centre for Sustainable AI
Dr. Mahendra (GAICD, MBA, SMIEEE, ACS(CP)) is the ACS ICT Professional of the Year 2022; and an accomplished data science leader who is actively involved in unlocking the power of technology to drive better humanitarian, social, and sustainability outcomes. He drives this in his roles as author, academic, inventor, and mentor. He holds a Ph.D. in computer science with double Masters in Business Administration and Project Management.
He is an industry collaborator who actively leads technology innovation-and-transformation initiatives and partnerships toward humanity, social justice, and sustainability. In this role, he is an Advisory Council Member in Harvard Business Review (HBR), an Expert in AI ethics and governance at Global AI Ethics Institute, an industry Mentor in the UNSW business school, a senior member of IEEE (SMIEEE), an honorary visiting scholar at the University of Technology Sydney (UTS), an Advisor for Data Science and Ai Association of Australia (DSAi), and a graduate member of the Australian Institute of Company Directors (GAICD).
He developed the Australian Red Cross AI governance and strategy framework crucial to the business’ successful deployment of Data Science and AI capabilities to mobilise the power of humanity. He built the Volunteer Data Science and Analytics team from the ground up, supporting the Australian Red Cross’s strategic goals. He helped the business for personalised engagement of customers for disaster resilience in the demanding times of pandemics, natural disasters, and global conflicts. He was also a co-author of the IFRC data playbook and contributed to the data science and emerging technology chapter for AI governance, ethics, and literacy.
Key Sessions
Module 1: Strategy and Technology
Developing a Cohesive, Outcome Driven Roadmap for AI-Powered Transformation
- Collaborating with business leaders and function leaders to create a sustainable AI and ML strategy
- Future-proofing your AI and ML strategy and technology
- Why do AI and ML products fail and strategies to overcome them
Uplifting your AI and ML Platform for Optimal Social and Business Outcomes
- Uplifting your technology stack for better AI and ML-powered outcomes on a budget
- What are the most common causes of reduced accuracy of AI and why do they occur
- Identifying your risk apatite based on Ethics, Bias, Privacy, and Impact
Q & A with Trainer
Module 2: AI Governance & Ethics
Applying the KITE model and Wind Turbine model in your Agency to Drive Better Business and Social Outcomes
- Discussing the benefits and the shortcomings of both models
- Identifying the best uses for these models in your own organisations
- Understanding how these models can mitigate the risks of biases
Driving better Social and Business Outcomes by Overcoming Biases when Deploying AI
- Defining the benchmark for Ethical AI practices and identifying the key gaps in your practice
- Strategies and controls to overcome algorithmic biases
- How to create a future-proofed, holistic governance and ethics framework
Q & A with Trainer and Wrap Up
Module 3: AI Projects and Frameworks
Applying Natural Language Processing and Image Processing to Obtain Valuable Insights
- Discussing processing techniques to ingest and inspect data
- Applying these techniques to sentiment and social media analysis
- Applying NLP and Image Processes to streamline processes
Embedding AI and Ensuring that it is a Core Competency in your Business
- A Checklist: The question to ask to ensure your AI and ML products are aligned with business outcomes
- Bridging the communication gap and engaging with subject matter experts within business to drive better outcomes
- How to scale and embed your systems and processes
Q & A with Trainer
Module 4: Sustainable AI and AI for Sustainability
Setting up AI and ML Frameworks for Sustainability
- Understanding the significance of sustainability in AI and embedding it in your products
- Future proofing and sustainability
- Case Study: Mitigating the risk of climate change to first nations people
Creating a Proof of Concept
- Applying the learning from the course to create a Proof-of-Concept AI model
Summary and Closing Notes from the Trainer
