---
# System prepended metadata

title: X AI in HRM

---

# eXplainable AI in HRM

### Project Idea:
Developing an Ethical XAI Framework for Human Resource Management

### References:
1. [Role of AI in Human Resource Management](https://www.retorio.com/blog/what-ai-in-human-resource-management)
2. [Responsible artificial intelligence in human resources management: a review of the empirical literature](https://link.springer.com/article/10.1007/s43681-023-00325-1)
3. [XAI Implementation methods](https://arxiv.org/pdf/2403.08946.pdf)
4. [Artificial Intelligence Applications in Human Resource Management](https://arxiv.org/ftp/arxiv/papers/2308/2308.09798.pdf)
5. [Collection of research materials on XAI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI)
6. [Guide to Explainable AI](https://www.youtube.com/watch?v=vNup3PygoWI)
7. [Role Of Generative AI And Large Language Models in HR](https://joshbersin.com/2023/03/the-role-of-generative-ai-and-large-language-models-in-hr/)
8. [Stanford Seminar - Human-Centered Explainable AI](https://www.youtube.com/watch?v=ZxPV_KVq-tI)
9. [IBM product](https://www.ibm.com/products/watsonx-orchestrate/human-resources)
10. [LLMs in HR analytics](https://resources.workable.com/tutorial/llms-in-hr-analytics)
11. [Designing human resource management systems in the age of AI](https://link.springer.com/article/10.1007/s41469-023-00153-x?fromPaywallRec=false)
12. [Re-Inventing Human Resource Management Through Artificial Intelligence](https://link.springer.com/chapter/10.1007/978-981-16-3250-1_12?fromPaywallRec=false)
13. [IBM HR analytics: Kaggle](https://www.kaggle.com/code/faressayah/ibm-hr-analytics-employee-attrition-performance)
14. [Explainable AI coursework, Spring 2023, Harvard University](https://interpretable-ml-class.github.io/)
15. [Book: Explainable AI for Practitioners, Oreilly](https://www.oreilly.com/library/view/explainable-ai-for/9781098119126/)


### Possible Workflow: 

``` mermaid
graph TD;
    A[Understanding HR Data and Defining HRM Processes] --> B(Gather HR Data Sources);
    B -->|Example: Collecting employee demographics, performance metrics, and feedback data.| C(Analyze Preprocessed Data);
    C -->|Example: Analyzing employee performance data to identify areas for improvement.| D(Identify Key Performance Indicators KPIs);
    D -->|Example: Defining KPIs such as employee turnover rate, productivity, and satisfaction.| E(Develop Custom XAI Algorithms);
    E -->|Example: Creating XAI algorithms to explain hiring decisions based on candidate profiles.| F(Train XAI Models using HR Data);
    F -->|Example: Training XAI models using historical HR data to predict employee performance.| G(Design APIs/SDKs for Integrating XAI Algorithms into HRM Software Platforms);
    G -->|Example: Developing APIs to integrate XAI algorithms into HRM software platforms.| H(Conduct Unit Testing to Verify Functionality and Accuracy of XAI Algorithms);
    H -->|Example: Testing the functionality of XAI algorithms to ensure they provide accurate explanations.| I(Evaluate Effectiveness of XAI Algorithms in Providing Transparent Explanations);
    I -->|Example: Evaluating the impact of XAI algorithms on hiring decisions and employee satisfaction.| J(Documenting the development process and technical specifications of XAI algorithms.);
    J --> K(Share Insights);
```

### Possible Empirical metrics:

1. Accuracy of XAI Algorithms:

    > Example: The XAI algorithm achieves an accuracy of 85% in providing explanations for HRM decisions.
        Analysis: Analyze the precision, recall, F1 score, or accuracy of the XAI algorithm on a test dataset. Compare the algorithm's performance against baseline models or human-level performance to assess its effectiveness.

2. Stakeholder Satisfaction:

   >  Example: 85% of HR professionals rate the usability of XAI-enabled HRM systems as satisfactory.
    Analysis: Analyze survey responses, feedback forms, or user satisfaction scores to assess stakeholder satisfaction levels. Identify common feedback themes and prioritize improvements to address any concerns raised.

3. Reduction in Bias:

    > Example: The XAI framework reduces gender bias in hiring decisions by 50% compared to traditional methods.
    Analysis: Compare the outcomes of hiring decisions made using the XAI framework with those made using traditional methods. Analyze the demographic distribution of hires to identify any disparities and assess the effectiveness of the XAI framework in mitigating bias.
    
4. Transparency Improvement:

    > Example: After implementing the XAI framework, there is a 30% increase in employees' understanding of the factors influencing HR decisions.
    Analysis: Conduct surveys or interviews with employees to gauge their understanding of HR decisions before and after the implementation of the XAI framework. Measure the increase in transparency and identify areas where further improvements can be made.

