By Apoorva Kasam - June 20, 2023 5 Mins Read
Machine learning (ML) has become relevant due to its measurable impact on businesses. The use of ML-enabled systems helps enterprises make vital decisions. Thus, addressing bias and ensuring fairness in ML implementations is essential.
ML-based systems operate efficiently only when the data used to train them is accurate. Inherent biases in ML algorithm data could result in harmful and untrustworthy systems. Here are a few practices of how enterprises can address bias in ML.
A way to address ML bias is to assess the data and know how different biases impact the data used to train the model.
Have the businesses selected non-biased data? Have they ensured that the bias is not arising from data capture errors? Addressing these questions can help enterprises to identify accurate ML bias.
Organizations must state practice guidelines and procedures. It will help them identify, communicate, and mitigate potential bias. They must document cases of bias as they occur. They must outline the steps to recall bias and take action to reduce them.
As firms gather and collate data, they must know what a representative data set should look like. They must encourage the team to use their data analytical skills. It helps them check and track the model’s nature with the data characteristics. These factors must match to build a dataset with minimal bias.
Biases occur when choosing data from large data sets and during data cleansing. Businesses must document data selection and cleansing methods to ensure fewer bias-inducing mistakes. They must examine whether the models exhibit any issues. Adequate data selection and cleansing cuts the bias in future model iterations.
Adversarial learning includes two classifiers. It parallely trains a classifier and an adversary model. While businesses train the classifier to predict the task, the adversary exploits a bias. Companies can flag the introduced adversarial instances as threatening. These learnings help in preventing adversarial ML biases.
Businesses test ML models before placing them into operation. The evaluation focuses on model accuracy and precision. They must add effective bias detection measures in their model evaluation steps. They must remember that even if the model is accurate and precise, it could fail on bias measures, indicating training data issues.
There is a difference between the ML model’s performance in training and the real world. Businesses must set rules to check and review the operational ML model. It allows them to detect signs of bias in the results. This way, they can take required actions before the bias causes irrecoverable damage.
The training dataset must have a diverse and representative sample of the population. The lack of a representative data set indicates a biased ML model. Eliminating sensitive variables like race or gender from the training dataset reduces bias.
Techniques like bias correction, amplification, and data sampling mitigate bias in ML models. Using human oversight and decision-making helps detect and address any biases.
A way to cut bias is to pre-process the data to balance the different group representations. For instance, oversampling the minority class in a dataset offsets the class distribution.
The idea is to use fairness constraints during model training. Businesses can achieve this by adding a term to the loss function. It then penalizes the model for making unfair decisions towards specific groups.
Entreprises can use different fairness constraints as per the issue and the dataset. Here are a few of them.
Demographic parity ensures that the model makes independent decisions. The model must not discriminate the decisions as per protected characteristics. Predictive parity ensures that the model makes systemic predictions for many protected groups.
Equal opportunity ensures the model represents a similar positive rate across protected groups. It ensures that the model’s true positive rate for one protected group does not differ for another.
Calibration ensures that the model’s predicted probabilities resonate with the true positive rates. Businesses can achieve this by adjusting the model’s threshold. It helps them ensure the same number of false positive or false negative predictions across protected groups.
The language model data sets are vast for manual checks, but cleaning them is a good practice. Moreover, businesses must train their employees in tandem with historical data review. It will improve the ML models. They must communicate common biases and their sources. They can also provide real-life examples of biased decisions.
Clean data, appropriate algorithms, and human oversight are critical for implementing ML applications. Applying these techniques to every problem is challenging. It is true when the data is insufficient.
It is also essential to determine biases in human processes. Debiasing techniques help mitigate the impacts of hidden systemic biases and promote fairness. It also fosters positive outcomes in recruitment, brand awareness, and retention.
Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
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