I have been asked many times in the last six months to relate my AI Technology build experience (Wipro Technologies, Ai-XPRT and now at ESG Disclose) to automate the assurance of unstructured data using Artificial Intelligence and to share the best practice with our wider community. I am not a programmer nor a mathematician but here is the very iterative process we took to build AI enabled assurance platforms.
1. First, gather all relevant data sources that will be needed for the AI automation process. This could include databases, CSV files, or web scraping tools to collect data from online sources.
2. Next, perform initial data cleaning and preprocessing to ensure that the data is in a usable format. This could involve removing any duplicates, missing values, or outliers in the data.
3. Once the data is cleaned, it is important to perform data exploration and visualization to gain a better understanding of the data and identify any potential patterns or trends. This will help inform the next steps in the process.
4. Based on the results of the data exploration, select the appropriate machine learning algorithms and techniques to use in the AI automation process. This could involve using supervised or unsupervised learning techniques, depending on the nature of the data and the desired outcome.
5. Split the data into training and testing sets in order to evaluate the performance of the machine learning model. It is important to use a representative sample of the data for this process in order to ensure accurate results.
6. Train the machine learning model using the training data, and evaluate its performance using the testing data. If the model is not performing well, consider adjusting the algorithms or techniques used, or adding additional data to the training set.
7. Finally, once the machine learning model is performing well on the testing data, it can be deployed for use in the AI automation process. It is important to continuously monitor and evaluate the performance of the model to ensure that it is accurately predicting outcomes and providing value to the business.
In conclusion, this is very much an iterative process and requires to be constantly monitored to improve the understanding through the refinement of the models. I hope this helps inform organisations and individuals when considering automating data assurance
#ai #data #machinelearning #automation
