¿Cómo puede optimizar los modelos de aprendizaje automático para la confianza y la transparencia?

Title: Overcoming Bias and Variance in Automated Hiring Processes Automating hiring with ML seems smart—predicting candidate success based on algorithms. But beware of bias and variance. Bias arises from flawed assumptions, favoring a group. Variance is model sensitivity to data fluctuations—overfitting. Balance is key. Address bias by diverse training data, prevent variance by not overcomplicating the model. Striking this equilibrium is vital for fair and accurate automated hiring. if this seems interesting, then stay tuned.

Bias is like a basketball player who always shoots with the same force, no matter the distance. Similarly, a computer predicts with a fixed idea, even when situations differ. Variance is when a player's shots vary a lot in force at the same spot. Likewise, a computer with high variance gives different predictions for similar situations. We aim for balance. A player adjusts shots for distance (low bias) and avoids extreme inconsistency (low variance). This accuracy applies to models too, making reliable predictions for diverse scenarios. In other words, your problem statement and your solution must carry out the balancing act of bais and variance.

An example of explainability technique that is worth mentioning is LIME (Local Interpretable Model-agnostic Explanations), a state of the art method that helps humans to interpret ML models.

I would add pytorch captum alongside TensorFlow Model Analysis or AI Fairness 360. Captum is model interpretability for pytorch.

There are various methods to test your model. One way is to use TensorFlow and evaluate your results. Another approach is to split your data 80:20, reserving 20% for testing and the rest for training. This approach provides clear answers. Another valuable tool is the "Confusion Matrix," which works with categorical variables. It shows accuracy in False positives and False negatives—crucial because we often have biases. For instance, if only 1 out of 100 visitors buy from your website, a model might predict no future purchases with 99% accuracy. Yet, as a business owner, I want to identify that 1 person who will buy from us.

Let's take the use case of credit risk and fraud detection in near real-time. The machine learning model can decide whether to allow or block a transaction based on a known set of variables. However, it can also result in false positives and false negatives which will have an immediate impact on the customer experience. Thus, it is crucial for an organisation to be transparent about the model and point out its limitations in an easy to understand way. This will build long term client relationship besides providing mechanism to raise issues which will further help in fine tuning the model. Some of the more easily explainable ML models such as Decision Trees can be communicated easily, but may not provide the same accuracy as Ada Boost e.g.

In my experience, any ML model built for an end-state solution need to be configured for the stakeholder and solve a targeted use case. For e.g., a stakeholder who understands how ML operates may find it valuable to have a simulator which he/she can use to play around with the model parameters and fine tune the outcome. While a stakeholder from the business would benefit from a scenario planner wherein he/she can modify the source factors to observe the end-state outcome change in real-time allowing for effective business planning. This facilitates adoption which in-turn leads to trust and advocacy.