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Government Use of AI/ML

Leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies to improve public services, policy making, and governance.

Overview

This specialized course is designed for government officials, policymakers, and public sector employees who need a comprehensive overview of how AI and ML can be applied in various government sectors, including healthcare, transportation, public safety, and more.

 

Through a combination of theoretical knowledge, case studies, and practical exercises, participants will learn about the opportunities, challenges, and ethical considerations of implementing AI/ML solutions in government operations. The course aims to equip attendees with the insights needed to make informed decisions about AI/ML adoption and to foster innovation in public services for the benefit of society.

 

This course is ideal for government officials and public sector employees at all levels who are interested in exploring how AI/ML can be used to enhance public services and governance. Join us to gain the knowledge and skills needed to lead the way in adopting AI/ML technologies for the public good.

Reserve Your Seat
$695 (USD)
1 day/8 hours of instruction
Group (3+): $595 USD
GSA: $585 USD

Next Upcoming Course

There are currently no scheduled classes for this course.
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Government Use of AI/ML Schedule

Delivery
Date
Reserve your seat
There are currently no scheduled classes for this course. Please contact us if you would like more information or to schedule this course for you or your company.

Full Course Details

1. Introduction to AI/ML in Government

  • Overview of AI and ML technologies
  • Potential benefits of AI/ML in government services
  • Examples of government use of AI/ML

2. Key Areas of AI/ML Application

  • IP Management: Automating data analysis and search processes.
  • Fraud Detection: Identifying fraudulent information using machine learning.
  • Data Analytics for Policy: Analyzing filing trends for policy insights.
  • Customer Service: AI chatbots for real-time applicant assistance

3.Developing an AI/ML Strategy for Government

  • Identifying opportunities for AI/ML implementation
  • Building a skilled team and fostering a culture of innovation
  • Partnerships with tech companies and academic institutions

4.Ethical and Legal Considerations

  • Understanding bias, fairness, and transparency in AI/ML models
  • Data privacy and security in government use of AI/ML
  • Regulatory frameworks and compliance

5. Overcoming Challenges in AI/ML Adoption

  • Addressing public concerns and building trust
  • Managing costs and ensuring scalability
  • Keeping pace with technological advancements

6. Case Studies and Best Practices

  • Analysis of successful AI/ML projects relevant to government use cases
  • Lessons learned and recommendations for implementation
  • Interactive discussion on AI/ML projects in participants' areas of work

7. Planning and Implementing AI/ML Projects

  • Defining project objectives and scope
  • Selecting the right tools and technologies
  • Measuring impact and iterating on solutions

This course has no prerequisites. 

However, learners who lack prior knowledge or experience with AI or Machine Learning would benefit from taking one of these courses prior to Government Use of AI/ML:

  • Conceptual Overview of AI and Machine Learning will provide a basic knowledge of relevant terminology and concepts.
  • ChatGPT Prompt Engineering Primer will provide a working knowledge of large language model AI systems like ChatGPT and CprimeAI. This includes basic terminology, basic prompt writing, and using such systems via pre-existing web-based interfaces. 

After completing this course, you will be able to:

  • Understand the potential of AI/ML technologies to transform government services
  • Identify opportunities for AI/ML implementation in their respective sectors
  • Develop a strategic approach to adopting AI/ML in government operations
  • Navigate ethical, legal, and public trust issues related to AI/ML use
  • Apply best practices and lessons from case studies to their own AI/ML projects

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