Leo is a software engineer turned digital artist who is fascinated by the use of AI in creating art. He developed an AI artist called ‘Iris’, who is trained on a historical art dataset. Iris can create beautiful paintings in no time when given a detailed prompt.
However, Leo notices some problematic patterns within the paintings, such as placing people of a certain color or facial features in the background or highlighting people from a certain community as important. To resolve this issue, Leo opted to sanitize the dataset on which Iris was trained. After doing so, the paintings created by Iris improved and became bias-free.
So what exactly is data sanitization? It is a process of permanently deleting data from storage devices in such a way that it is impossible to recover the eliminated data. By removing corrupted and biased information, you can make the training dataset clean and reliable. AI systems built on top of such data are highly accurate and generate correct outcomes.
In this blog, let’s try to understand how data sanitization is important to develop and deploy ethical AI.
Why is There a Need for Data Sanitization?
To Protect Sensitive Data
If you want to protect outdated sensitive data records such as customer details, financial transactions, and healthcare information, data sanitization can be a perfect solution. It helps prevent the possibility of recovering and misusing such data for cyberattacks or digital fraud.
To Comply With Data Regulation Guidelines
There is a clause for the right to be forgotten under data regulatory frameworks like HIPAA and GDPR. These frameworks contain guidelines that allow an individual the right to ask the data controller to erase data, and they are obligated to do so without any delay. Therefore, data sanitization is essential to comply with global data regulation guidelines.
Data Storage Management
Businesses should regularly clear unwanted and older data from their digital systems for smooth functionality. This is because outdated data can slow down or make their data-driven operations erroneous.
For example, a hospital has a data record of two patients named Jack Smith. One of them was an old patient who had stopped using the hospital services. At one instance, another Jack Smith, who still takes services from this hospital, asks for a digital consultation. The hospital, by mistake, sends a prescription based on the health history of another Jack Smith. To avoid such grave errors, it is better to sanitize data regularly.
Prominent Techniques of Data Sanitization
Physical Destruction
This is the most predictable as well as hazardous method of data sanitization, in which the storage system, such as a hard drive of a laptop, is destroyed. This is done with the help of industrial shredders that break a device into pieces. Degausser machines are another alternative for data sanitization that provides a strong magnetic field to destroy the data stored on hard disk drives and tapes. The major disadvantage of the physical destruction method is that it is expensive and environmentally harmful.
Data Masking
Data masking is a popular technique used not only for data sanitization, but also for complying with data regulatory frameworks like GDPR. Randomization, word shuffling, and character replacement are some of the approaches involved in data masking. It allows you to sanitize data even when you are using the device. As a result, this approach is much better than the physical destruction approach.
Cryptographic Erasure
While storing data, you can use a strong cryptographic key for protection. If you want to sanitize such data, you just need to delete the cryptographic key. Doing so securely deletes your data, making it unrecoverable. However, this method is complex as it involves securely managing a cryptographic key before it is used maliciously by some miscreants.
Data Erasure
Data erasure is another good approach in which you can make data unrecoverable by overwriting. This technique involves replacing useful data with random 0s and 1s to destroy the original data. The data erasure method is environment-friendly but can be time-consuming.
Data Sanitization and AI: The Importance of Forgetting to Build Ethical AI**
When we talk about AI ethics, we usually discuss fairness, transparency, and accountability. However, just like real-life scenarios, where, sometimes, it is better to forget the memory of bad experiences, while developing AI systems, deletion is the best possible option. Data sanitization allows you to erase data that can introduce bias, violate privacy, or generate any harmful outcomes.
Here are some reasons why data sanitization is an important process for building ethical AI systems:
Introduces Selective Amnesia: Since AI models are designed to act like a sponge, continuously absorbing data. Sanitization techniques help in introducing selective amnesia. Using data sanitization, you can delete the problematic aspects of training data that include biases and can produce harmful results.
Creates a Defense Mechanism: Mass surveillance is the biggest ethical risk posed by AI technology. These systems don’t forget and can recreate an individual’s entire profile from the data stored over a period of time. The rise of generative AI in recent times has raised serious concerns among its users regarding data privacy, making the right to be forgotten clause redundant. In such times, data sanitization can help in developing a defense mechanism by deliberately removing data so that users’ privacy can be protected.
Prevents Ethical Decay of AI: What is culturally and socially acceptable today can become questionable in a decade or so. In such cases, sanitizing data is a better approach so that AI systems do not show any problematic or biased behavior.
Benefits of Using Data Sanitization for Ethical AI
Data Protection
Data sanitization techniques like data masking help you in protecting personally identifiable information (PII) such as a person’s name or address. Sanitizing such details before the dataset is used to train an AI model ensures that the personal data of any individual is not exposed to the AI system. This practice also helps in complying with global data protection standards like HIPAA and GDPR.
Bias Elimination
You can use a data sanitization process, such as cryptographic key erasure and data erasure, to delete biased data records from the training dataset. Biased data can include racist, misogynistic, and stereotypical references. By removing such data, you can ensure the development of an AI system that is fair and inclusive.
Developing Reliable AI Systems
Data sanitization allows you to remove unclean, duplicate, outlier, and incorrect values from datasets. Such consistent and complete datasets are useful for training accurate and reliable AI systems. Thus, it is advisable to sanitize training data beforehand to build robust AI applications.
Final Thoughts
Data sanitization is essential for protecting and storing data effectively. While developing AI, sanitizing the training data can aid in developing an ethical system that is free from bias and discrimination. However, you should be careful not to remove useful information during the removal process. Additionally, you must choose a data sanitization technique that can handle high-volume data efficiently at the enterprise level. By considering and implementing such measures, you can utilize the process of data sanitization in a better manner to build a robust AI system.
