Measuring bias and fairness in machine learning is really about making sure the model treats people fairly, no matter who they are. We want to avoid situations where the system gives better results to one group over another, like approving more loans for one gender, or misclassifying certain races more often.

To do that, we look at how the model performs across different groups. Are the predictions accurate for everyone? Are some people consistently getting worse results, even if they should be treated the same? Tools like accuracy comparisons, fairness dashboards, and confusion matrices can help highlight these differences.

There are also specific ways to measure fairness. For example, we might check if people from different backgrounds have an equal chance of getting a positive result, or if the model is equally accurate across groups. But here’s the key.... fairness isn’t just about numbers. It’s also about understanding people, context, and the real-world impact. That’s why it’s so important to involve diverse voices and think through the ethical side, not just the technical one.

Makin a fair AI model means blending data with empathy, and being willing to question the results, even when the math looks right.

Machine learning models identify the existing problems in the dataset to normalize and optimize them to work for most of the samples/data points. However, actuals information had a possibility of diverging from the prediction with the inference due to entrophy or bias presence in the real time application datasets. The bias can be reduced using various mathematical error feature engineering principles like mean squared errror, mean squared log error, assigning specific weights vector to each of the input values to shrink the error value. Most of the existing Machine Learning models provides an equation with bias introduction during training the model. The bias will be continued in the inference model to simulate the model to make it stable for making an testable ML model for deployment.

Bias is always an assumed component with an assignment of introduced values based on the respective ML model.

For instance, If we consider a regression model our formula looks like the following.

Y = b0 + X*w. where b0 = bias value is an assumption based on the

regression dataset values.

X = input vector or array.

w = weights vector or scalar depending on the input

combinations.

An other Feature engineering principle would help in ML to help understand about the reliability and cohesiveness of a specific model is to find the accuracy of the Model. The accuracy significantly impacted by the probability of model giving the expected value compared to actual datapoint in training data. The Accuracy represented with

Accuracy = (True Positive+True Negative)/(True positive+True Negative +False positive+False Negative);

TP = True positive TN= True Negative. FP= False positive FN=False Negative.

TP = probable occurances of outcomes with depedency/Total number of outcomes

TN = Probability of an event not occuring with dependency/Total number of outcomes

FP = Probability of an event of outcome without dependency/Total number of outcomes

FN = Probability of an event not occuring without dependency/Total number of outcomes.

The accuracy score determines to consider whether the ML model is suggested to consider for a prediction process or performing clustering to form insights from the given dataset. The score of Accuracy lies between 0 and 1 with a value nearest to 1 gives the best model to be considered and a value closer to 0 would be avoiadable from the inference.

Dear Alwielland Q. Bello ,

t involves measuring the difference between the model's predicted outcomes and the actual outcomes to determine the extent of bias present in the model. The goal of bias estimation is to ensure that the model is fair and unbiased in its predictions

Bias in machine learning refers to the systematic error or deviation in a model's predictions from the actual outcomes. In other words, it is a form of inaccuracy in a model that occurs when the model makes assumptions or generalizations based on limited or incomplete data. This can result in the model being skewed towards certain patterns or outcomes, leading to unfair or discriminatory results. Bias can arise from various sources, such as a skewed or imbalanced dataset, incomplete feature representation, or the use of biased algorithms. It is essential to identify and correct biases in machine learning models to ensure their accuracy and fairness in real-world applications before using them in decision-making positions.

https://kili-technology.com/data-labeling/machine-learning/bias-estimation-a-complete-guide-for-machine-learning-engineers

Regards,

Shafagat