How do you deal with data bias and fairness in ML vs DL outcomes?

n addressing data bias and fairness in ML vs DL outcomes, I meticulously examine training data for biases, implement bias detection tools, and employ fairness-aware algorithms. Regular monitoring and adjustments during model training help mitigate biases. Additionally, I prioritize diverse datasets and promote ongoing evaluation to ensure equitable outcomes in both machine learning (ML) and deep learning (DL) models.

Any data subject to human selection runs the risk of embedding human bias into an algorithm. Example: when trying to identify the best associates in a company to glean off best practices, polling for opinion introduces favoritism, bias, and prejudice. The problem is made worse when the workforce is more homogeneous (ex: 65% white, cisgendered, straight, and male), with the majority demographic more likely to share biases and prejudice. Even sales numbers, which seem objective, are subject to customer biases and can feed into algorithms. Solution: Instead of polling for peer opinions or sales, use more objective data such as service level agreement (SLA) rates (customer emails answered within 2 business days, sales calls made, etc.).

Thank you for raising such an interesting question about data bias. I have a unique perspective on this issue that I'd like to share. I welcome your feedback and thoughts on this issue, and I'm also curious to hear your views. Let's continue the conversation and learn from each other."https://www.linkedin.com/pulse/how-do-you-deal-data-bias-fairness-ml-vs-dl-outcomes-kareen-santana

I think apart from choosing the right method we should also consider large amount of diverse data sets which are better representative of data, because if we consider that it may act as a better representation of the population and we can figure out the difference from output of two different kind of data sets and then it may in reducing the biases. We should also consider that we may never achieve a perfect kind of data sets so its better to keep reducing the biases and the system should always consider a possibility of bias.

The project focused on developing a medical diagnosis model. Using confusion matrices, the model's predictions were compared against actual outcomes, showing true positives, false positives, true negatives, and false negatives. The ROC curve illustrated the trade-off between sensitivity and specificity. High precision indicated accurate positive predictions, while high recall suggested capturing most true positives. The F1-score balanced precision and recall. To ensure fairness, the model's performance was assessed across gender groups. Fairness indicators revealed discrepancies. Visualizations like heatmaps showcased prediction patterns. If the model favored one gender, precision-recall curves for each group helped identify disparities.

The project consisted on developing a credit approval model. Initial evaluation showed 85% accuracy. Delving deeper, confusion matrix revealed high false positives for risky loans. ROC curve indicated room for improvement. Precision was 80%, recall 70%, and F1-score 75%. Fairness assessment exposed bias. Validation against new data with cross-validation showed slight drop in performance, hinting at overfitting. Testing against adversarial examples uncovered vulnerability. This process guided model refinement for enhanced real-world performance, fairness, and resilience.

Led a project focused on healthcare AI predicting disease risk. After the assessment, explained the model's predictions to doctors, a selected group of patients, and key regulators. Utilized feature importance to highlight factors influencing predictions like genetics and lifestyle. Decision trees visually depicted the model's logic. Employed natural language generation to craft user-friendly explanations. Developed dashboards for doctors to understand model insights. Reports detailed model accuracy and limitations, helping users make informed decisions. Transparent communication ensured trust, ethical use, and addressed societal concerns.

Data bias and fairness are crucial issues in any data-driven project, especially when using machine learning (ML) or deep learning (DL) techniques. Therefore, how you approach a project from very beginning is key. Plan to answer these questions BEFORE you begin, Determine if your data is biased. What are the sources, formats, and characteristics of the data? How representative, complete, and accurate is it? How does it reflect the real-world problem and context you are trying to solve? Can you mitigate the bias in the data or access unbiased data? How might you fairly evaluate the outcomes and measure their performance, accuracy, and fairness? How will you explain the outputs? Only being your work when you can answer those questions.

It is also important to understand the business process or functional use of that data. Often you will find what the data reflects and expected outcomes don’t match up or there gaps. If you explore the data without understanding the business context and need then your analysis and algorithms may not be completely accurate

Outside of specific algorithms, I have found that breaking up a given task into as many distinct parts as possible is hugely helpful to reasoning about what is happening.