How do you combat yourself against biased server infrastructure? 

It's important to be aware of potential biases in server infrastructure and take steps to mitigate them. Here are some ways to combat bias:

* Diverse Training Data:  Ensure that the data used to train AI models is diverse and representative of the real world. This helps prevent models from learning and perpetuating existing biases.
* Regular Audits and Monitoring:  Regularly audit your infrastructure and models for biases. This can involve analyzing data, evaluating model performance on different groups, and using fairness metrics.
* Transparency and Explainability:  Make the workings of your models transparent to understand how they are making decisions and identify potential biases. 
* Human Oversight:  Incorporate human oversight into your systems to review decisions and identify potential biases. 
* Bias Mitigation Techniques:  There are various techniques, such as adversarial training and fair representation learning, that can be used to reduce biases in AI models.

By taking these steps, you can help ensure that your server infrastructure is fair and unbiased.