The ill-posedness of the mean-field case arises from the inherent instability in recovering interaction kernels from observational data, particularly when the data is sparse or noisy. To address this challenge, effective regularization techniques can be employed. Regularization methods, such as Tikhonov regularization or Lasso regression, can introduce additional constraints or penalties on the estimated interaction kernels, promoting smoother or more structured solutions that are less sensitive to noise. By incorporating prior knowledge about the expected behavior of the interaction functions, such as smoothness or sparsity, these techniques can stabilize the estimation process. Furthermore, Bayesian approaches can be utilized to incorporate uncertainty into the model, allowing for a probabilistic interpretation of the interaction kernels. This not only helps in obtaining more reliable estimates but also provides a framework for quantifying the uncertainty associated with the recovered kernels, which is crucial for making informed decisions based on the model outputs.