With the surge of AI models in everyday use, examining the trustworthiness of these models remains a crucial concern. Trustworthiness is defined by several key factors: vulnerabilities inherent in the model architecture that can be exploited by adversaries, leading to faulty model use; vulnerabilities in the training data that result in unfair demographic biases, along with mechanisms to mitigate these biases; and latent representations of models that can be used to recover sensitive training information. This presentation will explore these aspects in depth, discussing how adversarial attacks can compromise model integrity, the impact of biased training data on model fairness, and the risks associated with extracting sensitive information from model representations. Additionally, we will examine algorithmic strategies designed to enhance the robustness and fairness of machine learning models, ensuring their reliability and ethical deployment in real-world applications.