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Course Outline
Day 1: Foundations of Artificial Intelligence and Document Analysis Capabilities
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Module 1: Introduction to AI for Public Service Professionals
- Clarifying core concepts of AI, machine learning (ML), and natural language processing (NLP) using accessible terminology.
- Positioning AI as a collaborative instrument: Shifting focus from potential disruption to operational support.
- Review of relevant case studies demonstrating successful AI deployment in regulated sectors such as legal and financial services.
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Module 2: Essential NLP Functions for Document Processing
- Document Classification: Utilizing AI to automatically categorize document types, including charters, financial reports, and environmental permits.
- Entity Extraction: Leveraging AI to identify and retrieve specific data points such as executive names, capital values, effective dates, and taxpayer identification numbers.
- Sentiment Analysis and Risk Assessment: Detecting potential liabilities or adverse indicators within contractual clauses and documentation.
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Module 3: Practical Applications of Machine Learning
- Mechanisms of supervised learning through the use of historical document datasets.
- The critical role of data quality in ensuring accurate model performance.
- Overview of the machine learning project lifecycle, ranging from data acquisition to model validation.
Day 2: Operational Applications, Technology Landscape, and Strategic Planning
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Module 4: Workshop - Aligning Workflows with AI Solutions
- Interactive exercise to identify high-volume manual tasks within the licensing workflow.
- Collaborative strategy session on applying NLP and ML technologies to address identified inefficiencies.
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Module 5: Overview of AI Technologies and Tools for Government
- Evaluation of solution tiers, ranging from off-the-shelf software-as-a-service (SaaS) offerings to customizable platforms.
- Demonstration of selected AI tools designed for document analysis.
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Module 6: Developing and Executing an AI Initiative
- Procedures for initiating a pilot program.
- Establishing key performance indicators, such as processing time reduction and error minimization.
- The role of human oversight: Emphasizing the necessity of expert verification in AI-driven processes.
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Module 7: Ethical Standards and Risk Mitigation
- Protocols for data security and confidentiality within AI infrastructure.
- Identification of potential algorithmic bias and strategies for remediation.
- Establishing accountability and trust in automated analysis outcomes.
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Module 8: Conclusion and Implementation Roadmap
- Formulating a strategic action plan for AI integration within the licensing division.
- Final review and Q&A session.
Requirements
Intended Beneficiaries
- Licensing Division
- Administrative and records staff
14 Hours
Testimonials (1)
I got to learn more about Microsoft Copilot, something that I thought was the same as chatGPT but I got to discover more exciting options that I will forever use to make my life easy.