Expert systems techniques and artificial intelligence in human resource management #254025

Upon completion of this course, participants will be able to:

• Understand Expert Systems & AI Fundamentals: Differentiate between expert systems, machine learning, and other AI techniques, identifying their unique applications in HR.

• Design HR Knowledge Bases: Structure and codify organizational HR knowledge, policies, rules, and procedures into a formal knowledge representation framework.

• Develop Rule-Based Reasoning for HR: Design and implement “if-then” rule engines for critical HR functions such as recruitment shortlisting, compensation benchmarking, performance evaluation, and compliance checking.

• Evaluate ES and AI Solutions: Assess the technical feasibility and strategic value of implementing an expert system versus other AI solutions for a given HR problem.

• Manage Implementation and Integration: Plan the development lifecycle of an HR expert system, including integration with existing HRIS (Human Resource Information Systems) and data architecture.

• Address Ethical and Practical Challenges: Identify and mitigate risks related to bias in rules, system transparency, user trust, and the maintenance of the knowledge base.

This course is designed for HR and technology professionals who are involved in designing, implementing, or managing advanced HR systems and processes:

• HR Systems Managers & HRIS Analysts

• HR Directors & VPs of Talent/People Operations focused on technology strategy

• Organizational Development & Effectiveness Specialists

• Compensation & Benefits Analysts and Managers

• Talent Acquisition Technologists and Recruitment Process Designers

• IT Professionals and Business Analysts supporting the HR function

• HR Consultants specializing in process optimization and digital transformation

• Chief Knowledge Officers and Data Governance Managers

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI and Expert Systems in HR

• AM: The Architecture of Intelligent HR Systems

  • Introduction to AI in HR: From chatbots to decision support systems.

  • Core Concepts: Defining Expert Systems, their components (Knowledge Base, Inference Engine, User Interface), and how they differ from Machine Learning.

  • Historical and modern applications of ES in HR: from benefits advisors to complex policy interpreters.

• PM: Identifying ES Opportunities in the HR Value Chain

  • Mapping HR processes suitable for rule-based automation: compliance, grading, screening, policy interpretation.

  • Workshop: Analyzing HR workflows (e.g., leave approval, job leveling) to identify expert system opportunities.

Day 2: Building the Knowledge Base: Capturing HR Expertise

• AM: Knowledge Engineering for HR

  • Techniques for extracting knowledge from HR subject matter experts: interviews, protocol analysis, and observation.

  • Representing HR knowledge: Rules, frames, ontologies, and decision trees.

  • Structuring organizational policies, employment law, and compensation bands into a codifiable format.

• PM: Practical Rule Design

  • Writing effective “if-then-else” rules for HR scenarios.

  • Handling uncertainty and confidence factors in human decisions.

  • Hands-on Exercise: Codifying a complex company leave policy into a set of logical rules.

Day 3: The Inference Engine: Applying Logic to HR Decisions

• AM: Reasoning Methodologies

  • Forward Chaining (Data-Driven): Using employee data to trigger conclusions (e.g., identifying training needs).

  • Backward Chaining (Goal-Driven): Working backwards from a goal (e.g., “Is this candidate eligible for a visa?”) to required data.

  • Selecting the right reasoning strategy for the HR task.

• PM: Developing a Prototype ES for a Core HR Function

  • Case Study: Building a rule-based system for initial resume screening based on key criteria.

  • Case Study: Creating a compliance checker to ensure new hire offers adhere to internal equity and legal standards.

  • Workshop: Using a simple ES shell or logic programming environment to build a basic prototype.

Day 4: Implementation, Integration, and Validation

• AM: The System Development Lifecycle for HR ES

  • Project planning, prototyping, and testing methodologies.

  • Integration strategies: Connecting the ES with HRIS (e.g., Workday, SAP SuccessFactors), ATS, and other data sources.

  • User Interface design: How HR business partners and employees will interact with the system.

• PM: Validation, Maintenance, and Trust

  • Techniques for verifying and validating the system’s decisions against human experts.

  • The challenge of maintenance: Keeping the knowledge base updated with changing laws and policies.

  • Ensuring explainability: Designing systems that can explain their reasoning to build user trust and aid debugging.

Day 5: Advanced Topics, Strategy, and Capstone

• AM: Hybrid Systems and The Future

  • Combining Expert Systems with Machine Learning (e.g., using ML to suggest new rules from data).

  • The role of Natural Language Processing (NLP) for parsing employee documents and queries.

  • Ethical considerations: Preventing bias in rule design and ensuring fairness.

• PM: Capstone Project and Strategy Session

  • Capstone Exercise: Teams work to design a comprehensive ES solution for a complex HR challenge (e.g., an employee relations advisory system or a career pathing tool), outlining the knowledge base, rules, and implementation plan.

  • Developing a business case for an ES project: Calculating ROI through consistency, time savings, and risk reduction.

  • Course Wrap-Up: Final presentations and development of a personal action plan.