AI and Data Privacy Paradox

"Privacy is a fundamental human right that underpins freedom of association, thought and expression, as well as freedom from discrimination."

Why should I care

The paradox of AI and data privacy arises from the fact that AI systems require large amounts of data to function effectively, but at the same time, the collection, storage, and use of this data may compromise individual privacy.

Living in a digital era where our every action generates data, stored and used by someone; protecting privacy has become increasingly difficult. Privacy is more vulnerable in case of Unstructured data, such as #audio, #image, and #video. These data contains more private information than structured tabular data. As Machine Learning and #AI continue to advance, the ability to extract meaningful information from unstructured #data becomes easier, increasing the potential for misuse.

Example: Unlike written text, people may unwittingly reveal sensitive information when they speak, without realizing the risks posed by their words. It's no wonder why audio data has become a top target for data privacy concerns. Same true for Video data as well.

In my experience, while designing #AI systems, #dataprivacy is one of the critical concerns raised by all privacy and data security officers in the past. Many projects are put on hold indefinitely unless it pass the data privacy and security criteria.

Don't let data privacy restrictions sink your AI project.

1. Reduce data sample size:?Minimizing the amount of data you use to develop prototype, which will lower the risk of exposing private information.
2. Develop your own AI: While expensive, creating an AI from scratch grants complete control over the resources used, such as data, algorithms, and processing power. Although it's important to keep in mind that developing something as advanced as ChatGPT is a considerable challenge. What you develope today, may be older in near future and you need to spend resources to uplift it.

1. Utilize AI SAAS with "Opt Out":?Another option is to use pre-build cloud AI services with opting out from training using your data. Many Cognitive Services nowadays offer an "Opt Out" option, which ensures that your data won't be used to train a centralized model. It's crucial to avoid using Cognitive Services or AI Services that lack an "Opt Out" option, as the consequences could severely impact your business's reputation.?fyi:?IBM was the 1st implemented opt out.

1. Containerized SAAS AI: This is my favorite. You can download the cognitive service container in your local hosted environment(server or local machine) and use as a local service, without sending your data to the cloud. Periodically, usage metrics for the containerized service are sent to a Cognitive Services resource in Azure in order to calculate billing fo the service.
2. Data Anonymization :?By applying data anonymization techniques, you can protect sensitive data while still allowing AI models to access it. This method helps maintain data privacy and facilitates ethical AI development.

Final thoughts

Ensuring data privacy is both essential and non-negotiable. However, this shouldn't impede your AI development efforts. By utilizing one or more of the technology solutions mentioned above, you can safeguard data without compromising your AI development. It's crucial to prioritize data privacy while also exploring responsible and ethical ways to leverage AI. By striking this balance, you can confidently advance your AI initiatives while upholding data privacy principles.

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Thanks for reading. In this article, I aim to share my experience on efficiently managing data privacy in AI projects. My goal is to assist the AI business community in preparing for an efficient AI-driven business with the aid of the latest technology. I hope that this article is helpful, worth reading and I encourage you to share it and provide your feedback. Please don't hesitate to reach out to me at [email protected]

Further readings:

1. https://www.sciencedirect.com/science/article/pii/S1045235421001155
2. https://news.harvard.edu/gazette/story/2020/02/surveillance-capitalism-author-sees-data-privacy-awakening/

#artificialintelligence #ai #machinelearning #data #cognitiveservice #azure #google #aws #dataprivacy #privacy #responsibleai #rai