Publication
Machines and Trust: How to Mitigate AI Bias
https://www.toptal.com/developers/artificial-intelligence/mitigating-ai-bias
Michael is currently unavailable
Michael McKenna
Verified Expert in Engineering
Natural Language Processing (NLP) Developer
Melbourne, Victoria, Australia
Toptal member since July 16, 2019
Bio
Michael is a data scientist and machine learning engineer with a diverse background spanning retail, healthcare, and government sectors, working in both startup and large enterprise environments. He has an eclectic set of technical specialties, including causal inference for retail experimentation, instruction tuning for generative AI models, and auditing for algorithmic fairness.
Portfolio
Self Employed
Artificial Intelligence (AI), Azure, Generative Artificial Intelligence (GenAI)...
CVS Health
Artificial Intelligence (AI), Causal Inference
Widget Brain
Google OR-Tools, Jupyter, PyTorch, Python
Experience
- Natural Language Processing (NLP) - 8 years
- Machine Learning - 8 years
- Python - 8 years
- Pandas - 8 years
- Data Science - 8 years
- Generative Pre-trained Transformers (GPT) - 8 years
- PyTorch - 5 years
- PySpark - 3 years
Preferred Environment
Amazon Web Services (AWS), Git, Python, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Spark, PySpark, Computer Vision, Machine Learning, Causal Inference
The most amazing...
...project I've coded is a demand diagnosis model to understand reasons for COVID-19 vaccine hesitancy across the USA. It saved hundreds of lives.
Work Experience
CTO
2022 - 2024
Self Employed
- Developed and implemented a comprehensive AI/ML ethics risk management process, resulting in agency-wide adoption and integration into legal and external agency procedures, reducing risks associated with advanced data-driven initiatives.
- Spearheaded the review of 40+ data-driven and non-data-driven projects across the agency, leveraging data analysis and ethical frameworks to provide actionable recommendations that were highly valued by senior executives and the Ethics Committee.
- Created an innovative generative AI application to assist government agencies in identifying potential risks, rewards, and resilience factors, demonstrating practical application of cutting-edge AI technology in the public sector.
- Established a reputation as the agency's go-to expert on emergent AI/ML technologies, including generative AI, providing critical contributions to the agency's interim AI strategy, particularly in areas of cloud computing and experimentation.
- Collaborated with subject matter experts to develop data-driven, contextually relevant case studies for training senior executives and staff, resulting in approximately 90% positive feedback and improved understanding of data ethics across the org.
Technologies: Artificial Intelligence (AI), Azure, Generative Artificial Intelligence (GenAI), Auditing
Senior Data Scientist
2019 - 2021
CVS Health
- Served as a lead data scientist on various machine learning development, experimentation, and workforce innovation projects, providing an incremental annual value of XX million USD.
- Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
- Implemented a fully customizable suite of COVID-19 vaccine demand forecasting, causal inference, and demand diagnosis models. These models anticipated vaccine demand drops and highlighted potential areas for intervention.
- Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
- Acted as a key contributor to CVS's enterprise-wide algorithmic bias policy, which set out steps for monitoring and mitigating bias along protected class lines within AI systems.
Technologies: Artificial Intelligence (AI), Causal Inference
Data Scientist
2018 - 2019
Widget Brain
- Led retail projects including demographic-based demand forecasting for a large supermarket, roster optimization for a large Australian cosmetics chain, and theatre attendance forecasting for a large Australian cinema company.
- Delivered predictive maintenance models for a large shipping OEM, allowing a 66% reduction in sensors.
- Implemented deep learning extensions (such as LSTMs) to the existing demand forecasting product.
- Built production flows using NodeRed and deployed models using AWS serverless code tools.
Technologies: Google OR-Tools, Jupyter, PyTorch, Python
Research Officer
2016 - 2018
Australian National University
- Built NLP machine learning models to predict the likely severity of identity theft case reports. Research officer on Australia's first large-scale study on identity theft.
Technologies: Jupyter Notebook, PyTorch
Experience
Generalized Demand Forecasting Model
Together with a team of data scientists, implemented and used a data forecasting suite including over 20 different models
Supermarket Demand Driver Model
Lead developer of a machine learning model using census demographic data to predict the success of supermarket promotions, expansions, and luxury items in a given area.
Operation Warp Speed Demand Forecasting
• Implemented a fully customizable suite of COVID-19 vaccine demand forecasting and demand diagnosis models. These models anticipated vaccine demand drops and highlighted potential areas for intervention.
• Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
• Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
• Responded to consistent urgent requests from the White House, CDC, and Operation Warp Speed leadership on capacity planning, second-dose adherence, and daily vaccine utilization.
• Collaborated with lead designers to identify and address the impact of social determinants of health on low immunization rates, drawing on SHAP values and ethnographic data to design interventions.
Education
2016 - 2018
Graduate Diploma in Computing
Australian National University - Canberra, Australia
2013 - 2016
Bachelor's Degree in Law
Australian National Unviersity - Canberra, Australia
Certifications
JUNE 2024 - PRESENT
Certified Information Security Manager
ISACA
APRIL 2024 - PRESENT
Certified Information Systems Auditor
ISACA
OCTOBER 2022 - PRESENT
Certified Information Privacy Technologist
IAPP
JULY 2019 - PRESENT
Admitted Legal Practitioner
Supreme Court of Victoria
Skills
Libraries/APIs
PyTorch, Pandas, PySpark, TensorFlow, Facebook API, Keras, Scikit-learn
Tools
Git, Jupyter, Google OR-Tools
Languages
Python 3, SQL, Python
Frameworks
StrongLoop, Spark
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Azure
Paradigms
Agile Software Development
Storage
MySQL
Other
Convolutional Neural Networks (CNNs), Machine Learning, Artificial Intelligence (AI), Computer Vision, Natural Language Processing (NLP), Data Science, Generative Pre-trained Transformers (GPT), Causal Inference, Neural Networks, Deep Neural Networks (DNNs), LSTM Networks, GeoPandas, Generative Artificial Intelligence (GenAI), Auditing, Legal, Privacy, Cybersecurity Operations
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