How can we reduce or eliminate bias in machine learning algorithms?

Reducing bias in ML models is a non-trivial problem. There are 3 opportunities to inject bias in a model: data prep, model development and output interpretation. Data bias reduction involves having a representative data set (easier said than done). Model bias can be somewhat reduced by trying out various approaches and network architectures (trade-off between agility and robustness). Interpretation bias is the most human of all biases and can be reduced through double blind peer review of model output and identifying counterfactuals.

Several biases can influence the underlying dataset and the machine learning model. Data sampling bias, variable-measuring instrument bias, bias caused by confounding variables, data collection bias, problem awareness bias, algorithmic biases, cognitive biases, confirmation bias, data processing bias, unsolicited data manipulations, the bias introduced by deception or lying, prejudice and stereotyping, institutionalized bias, risk/reward-driven bias, self-serving bias, social-standing bias, societal bias, priming, racial bias, ethnocentrism, gender-centrism, sensationalism bias to name a few. There is a lot of material available to understand each of such biases, so I won't define them here. However, the ML scientist or modeler should be mindful of such biases and must verify if any such biases can impact their ML design. #ml #machinelearning #ai #bias #mlmodels #scientificresearch

Machine learning?bias generally stems from problems introduced by the individuals?who design and/or train the machine learning systems. Collect More Data & Use Balanced Datasets: Make sure your dataset is balanced and has an equal number of data points for each class. Remove Unnecessary Features: Unnecessary features can be variables that do not contribute significantly to the predictive power of the model. Implement Regularization: Regularization is a technique used to reduce the complexity of a model and prevent overfitting. Use Cross-Validation: Cross-validation is a technique used to evaluate a model by splitting the data into training and test sets. By using cross-validation, model performance can be evaluated on unseen data. Monitor Training Performance: This can help you identify any potential issues early on and adjust the model accordingly. Include bias metrics in your evaluation: Make sure to include metrics that measure bias in the evaluation of your model. Use algorithms that are less susceptible to bias: Choose algorithms that are less prone to introducing bias, such as logistic regression or decision trees. Ensemble methods: Ensemble methods combine multiple models to reduce bias and improve accuracy.

Very interesting question. ? It seems to me however that the problem is not in the Machine Learning algorithm as much as the data or information fed to the Machine Learning Algorithm where one has to be careful. ? If there is bias in the data then that is what the Machine Learning Algorithm will learn. ? In fact, that is the essence of Machine Learning ? For example, if there is Data on people from a particular neighborhood where most of the people end up as criminals and that criminal data is included, the Machine Learn algorithm will tend?to put people from that neighborhood into the "Potentially Bad"category if there isn't further descriptors on people in that neighborhood that show the difference between Bad Actors and Good Actors. ?