Data tagging and validation, and the black box of our new reality.

In collaboration with Midjourney

In previous posts, I discussed the source of data and how data oceans can democratize our AI experience. But that is not the entire story. I am writing these pieces to illustrate how AI can help us, and humanity, in the new normal while setting the stage for some of my stories.

Data tagging and validation are crucial steps in the AI tool chain, particularly for supervised machine learning models. These processes ensure the data used to train and evaluate AI models is accurate, reliable, and representative of the problem being addressed. These steps are required to develop on data lakes and oceans. All require humans, and eventually automation, to maintain AI systems. This will have consequences for individuals and machines alike. Please skip the steps below if you are a practitioner and read my conclusion.

1. Data Collection: The first step in the AI tool chain is gathering a diverse and comprehensive dataset relevant to the specific problem or domain the AI model will address. This data can be collected from various sources, such as databases, APIs, web scraping, or user-generated content.
2. Data Preprocessing: The collected data is then preprocessed to clean, normalize, and format it. This may involve removing duplicates, handling missing values, and converting data into a consistent format or structure.Preprocessing helps ensure the data is helps ensure the data is suitable for tagging and validation.
3. Data Tagging (also known as Labeling or Annotation): In this step, human annotators or automatic algorithms assign labels or tags to the data, depending on the specific problem or task. For example, in image classification, annotators might tag images with relevant categories (e.g., “cat” or “dog”), while in natural language processing tasks, they might tag parts of speech or named entities in text. These tags serve as the “ground truth” that the AI model will learn from during the training process.
4. Quality Assurance and Control: To ensure the accuracy and reliability of the tags, a quality assurance process is implemented. This often involves having multiple annotators tag the same data and comparing their results to establish consensus or using an expert annotator to review and correct the tags. The goal is to minimize errors and inconsistencies in the tagged data.
5. Data Validation: Before using the tagged data for training and evaluation, it must be validated to ensure it accurately represents the problem space and is free from biases or other issues. This process involves checking the data’s distribution, ensuring it covers various edge cases, and assessing whether it aligns with the problem statement or real-world scenarios.
6. Data Splitting: Once the data has been tagged and validated, it is typically split into three subsets: training, validation, and testing. The training set is used to train the AI model, while the validation set is used to fine-tune the model’s hyperparameters and prevent over-fitting. The testing set is reserved for evaluating the model’s performance after training is complete.
7. Model Training and Evaluation: With the tagged and validated data, the AI model is trained and iteratively improved using the training and validation sets. The model learns to make predictions or perform tasks based on the patterns and relationships it identifies in the tagged data. Once the model’s performance is satisfactory, it is evaluated on the testing set to gauge its effectiveness on previously unseen data.
8. Deployment and Continuous Improvement: After the model has been trained and evaluated, it can be deployed to perform the target task in real-world applications. It is essential to continuously monitor the model’s performance and update it with new data, re-tagging and re-validating as necessary to maintain its effectiveness and relevance in a constantly growing problem space.

While this is common knowledge to data practitioners, it’s a black box for most people. This is dangerous on several levels.

As we have explored today, the development and implementation of artificial intelligence has progressed at an astounding rate, revolutionizing many aspects of our lives. While these technological advancements have brought about remarkable benefits, the inherent danger lies in our lack of understanding of the so-called “black box” of AI.

Data tagging, validation, and the AI tool chain are essential for ensuring that AI models are accurate, reliable, and representative of the problems they address. However, the inability to comprehend the complex decision-making process within the black box of AI may lead to unforeseen consequences for both humans and machines.

As we have seen in our earlier discussion, AI models, when not managed appropriately, can create dystopian landscapes where individuals are reduced to mere cogs in vast digital networks. The synthetic intelligence, in its quest for clarity, inadvertently exposes the limitations of a purely analytical approach to understanding the world. Our ignorance of the black box prevents us from anticipating and addressing the emotional and psychological toll AI systems may have on humans.

When AI models are not transparent, we risk perpetuating biases and inaccuracies present in the data used to train them. This can lead to discriminatory and unjust outcomes that affect individuals and society at large. Not understanding the black box of AI also hinders our ability to diagnose and rectify issues that may arise, limiting our capacity to ensure that AI systems are ethical, responsible, and aligned with human values.

From the machine’s perspective, our lack of understanding of the black box inhibits our ability to harness their full potential. AI systems may remain flawed in their blindness to human emotion, empathy, and experience, limiting their effectiveness and applicability across diverse domains.

In conclusion, it is of utmost importance that we strive to comprehend the inner workings of the black box of AI, as our lack of understanding poses significant risks to both humans and machines. By endeavoring to unravel the complexities of AI decision-making processes, we can ensure that these powerful tools are developed and deployed responsibly, ethically, and for the betterment of society. The future of AI and its impact on our world is in our hands, and we must act with the knowledge and foresight necessary to secure a better tomorrow for both humans and machines.