Privacy and artificial intelligence: are they incompatible?

robot programming in python with human, digital painting.

Prompt: a robot and a person programming in Python, digital painting.

Introduction

In today's digital age, the artificial intelligence (AI) It is becoming an integral part of our lives, transforming the way we work, communicate and solve problems. One of the fastest growing areas in AI is natural language processing (NLP). Its applications raise questions about which data is used, who can access it and how it is protected.

Despite the benefits, the use of personal data in training these models poses privacy and security concerns. In this article, we'll explore how AI models are trained, the risks associated with using personal data, and how OpenAI addresses these concerns with enhanced privacy options.

How artificial intelligence models are trained

The language models behind tools such as ChatGPT learn patterns from large datasets. Methods vary, but a simplified description distinguishes pretraining from subsequent stages of refinement.

1. Pretraining

During this phase, the model is exposed to enormous amounts of text, whether it is text extracted from the Internet, such as websites, blogs, forums and social networks; as texts not available on the internet. By processing this data, the model learns grammatical structures, vocabulary, and language patterns. However, you may also be exposed to erroneous, biased or inappropriate information.

2. Subsequent refinement

After pretraining, selected examples, instructions and human feedback can be used to refine the model’s behaviour. These stages aim for more useful and coherent responses, but do not eliminate errors.

Privacy risks in artificial intelligence

Training datasets may contain personal data. Using it poses risks both to the people concerned and to the organisations developing and using the systems:

1. Violation of privacy

If an AI model is trained with personal data, there is a risk that it could learn and reproduce private or sensitive information, which could expose users to privacy breach risks.

2. Exposure to cyber attacks

Personal data used in training AI models can become an attractive target for cybercriminals. If attackers access this data, they can use it for malicious purposes, such as identity theft or extortion.

3. Misuse of information

Users' personal data may be misused by companies or third parties accessing AI models, which could lead to discrimination, manipulation or exploitation of users.

4. Lack of informed consent

In many cases, users may not be aware that their personal data is being used to train AI models. This raises ethical and legal issues related to informed consent and the right to informational self-determination.

5. Legal responsibility

Processing data or generating outputs in breach of applicable rules can lead to liability and penalties. Obligations depend on the context, the type of data and the jurisdiction. We already covered the topic of AI and copyright in more depth in a previous article.

Prompt: an illustration of a cyberattack, with a person at several computers.

The OpenAI option to protect user data

To address privacy concerns, OpenAI provides controls over whether new conversations from certain personal accounts are used to improve models. Availability and conditions depend on the product and account, so users should check the current documentation. These controls have several implications:

1. User control

Users can have greater control over their personal information and how it is used in the development of artificial intelligence.

2. Risk reduction

Turning off training limits that use of new conversations. It does not automatically delete chats, disable storage or eliminate every privacy risk.

3. Trust in AI

This privacy-focused approach can increase user trust in the technology, encouraging broader and more responsible adoption of artificial intelligence in various applications.

Conclusions

Artificial intelligence, particularly natural language processing and models like ChatGPT, have enormous potential to improve our lives in multiple ways. However, it is also essential to address risks related to the privacy and security of personal data throughout a system’s lifecycle. Training-use controls are one part of that protection, alongside data minimisation, security, retention and people’s rights.

These types of measures are crucial to guarantee a ethical and sustainable development of AI, but no single setting guarantees absolute privacy. Organisations need to explain their practices, assess risks and remain accountable for their handling of data.

As users, we can demand that our data be used safely, understand the purposes of processing and exercise the rights that apply. Consent is one possible legal basis, but it is not the only one in every context.

References

AI Model: How Does It Work? (2022). Koombea; Koombea. https://www.koombea.com/blog/ai-model/#:~:text=AI%20models%20don’t%20have,teach%20in%20the%20same%20way.

Written by

Josep Moré Gili

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