Lecturer | Public Speaker | Business Automation Specialist | Technology Researcher
Artificial Intelligence Terminators
The technical, operational, and social implication of using artificial intelligence to augment/automate human decision-making exceeds the challenges posed by rule-based software due to artificial intelligence’s features and intended functions. This presentation will provide insights into best practices for developing, implementing and monitoring Artificial Intelligence systems. This presentation will also focus on what could go wrong with AI projects.
Technical PDU: 1
- Discuss the various types of Artificial Intelligence techniques and their levels of intelligence
- Discuss the strengths and challenges of the inherent features of Artificial Intelligence
- Discuss approaches to mitigate challenges of implementing Artificial Intelligence projects
About Ivy Munoko
Ivy Munoko, ABD, ACCA, CISA, is an Assistant Professor at University of Florida. Her research and teaching focus is on the use of Artificial Intelligence for Auditing and Forensics. She has over seven years of combined experience in IT, Finance, and Auditing. She has had several corporate roles, including, an IT Project Manager, Systems Auditor and Automation Specialist, directly related to operational and financial systems development, risk and control assessments, managing process automation and improvements, leading all phases of diverse technology projects.
She co-authored the publication “The Ethical Implications of Using Artificial Intelligence in Auditing” in the Financial Times Top 50 Journal: Journal of Business Ethics. She has presented this research to various professional bodies, including US and International Regulators who are exploring the ethical implications of using Artificial Intelligence for Auditing. She has a passion for technology research, education and delivering concrete results. Within the Forensics field, her current research is examining how Artificial Intelligence techniques such as Natural Language Processing and Machine Learning can be used to detect fraud red flags.