How can you eliminate neural network bias in recruitment?

1 Audit your data

The first step is to audit your data, which is the input for your neural network. You need to check if your data is representative, balanced, and diverse, and if it reflects the reality and the goals of your recruitment process. You also need to identify and remove any sources of bias, such as missing values, outliers, noise, or irrelevant features. You can use tools such as pandas, numpy, or scikit-learn in Python to explore and clean your data, and visualize it using matplotlib or seaborn.

2 Choose your algorithm

The next step is to choose your algorithm, which is the logic and the structure of your neural network. You need to select an algorithm that is suitable for your problem, your data, and your objectives, and that can handle the complexity and the uncertainty of your task. You also need to avoid overfitting or underfitting, which can affect the accuracy and the generalization of your model. You can use tools such as TensorFlow, Keras, or PyTorch in Python to build and train your neural network, and compare different architectures and hyperparameters.

3 Evaluate your model

The third step is to evaluate your model, which is the output of your neural network. You need to measure the performance and the quality of your model, and how well it meets your expectations and requirements. You also need to assess the fairness and the ethics of your model, and how it affects the candidates and the stakeholders. You can use tools such as scikit-learn, sklearn-metrics, or Fairlearn in Python to test and validate your model, and calculate different metrics and indicators.

4 Explain your model

The fourth step is to explain your model, which is the reasoning and the justification of your neural network. You need to understand how your model works, what factors influence its decisions, and what are the trade-offs and the limitations of your model. You also need to communicate your model to others, such as the candidates, the employers, or the regulators, and provide them with clear and understandable explanations. You can use tools such as LIME, SHAP, or ELI5 in Python to interpret and visualize your model, and generate feature importance or decision rules.

5 Monitor your model

The fifth step is to monitor your model, which is the feedback and the improvement of your neural network. You need to track the performance and the behavior of your model over time, and detect any changes or anomalies that might affect its reliability or its fairness. You also need to update your model regularly, and incorporate new data or new feedback that might enhance its accuracy or its ethics. You can use tools such as MLflow, TensorBoard, or Seldon in Python to manage and deploy your model, and monitor its metrics and logs.

6 Review your model

The final step is to review your model, which is the reflection and the evaluation of your neural network. You need to review your model periodically, and check if it still aligns with your goals and your values, and if it still meets the standards and the regulations of your industry or your domain. You also need to review your model critically, and consider the impact and the consequences of your model, and if it respects the rights and the dignity of the candidates and the society. You can use tools such as AI Ethics Checklist, Responsible AI Toolkit, or Ethical OS to audit and review your model, and identify and mitigate any risks or harms.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?