How to Prevent Violating Compliance Regulations When Developing AI Products

Nitya Umat
How to Prevent Violating Compliance Regulations When Developing AI Products

Nitya Umat

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C.E.O.

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November 23, 2023

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How to Prevent Violating Compliance Regulations When Developing AI Products

How to Prevent Violating Compliance Regulations When Developing AI Products

Artificial intelligence has been the top tech trend for several years due to its significant contributions to data and machine-intensive sectors such as manufacturing, healthcare, and finance.

Recently, there has been a surge in interest from end users in the last two years, especially with the development of image and text generators that allow people to create visuals and texts with just a click.

It may appear that AI platforms are creating content from scratch. Still, they are trained on snippets of questions and data from the internet, including text and images.

While these platforms are helpful to users, they do come with legal risks such as copyright infringement, failure to comply with open-source licenses, and intellectual property infringement. Governments worldwide are aware of these risks and are implementing new regulations and penalties to address unethical AI models.

When launching an AI project, companies must prioritize understanding the potential risks and building a system that complies with ethical and legal standards.

This article explores the various aspects of AI compliance during software development, including types of legal issues, preparation for AI regulation, and regional AI regulations.

What does AI Compliance entail?

An AI compliance check is a process that ensures that AI-powered applications comply with the regulations and laws of the region where they operate. 

The process involves checking various factors, including:

Legal Issues Involving Artificial Intelligence

A Machine Hand showing, Legal Issues Involving Artificial Intelligence

On a smaller scale, AI misuse may seem restricted to issues such as copying or accessing restricted data. Still, on a larger scale, it poses more significant challenges.

Poorly constructed AI systems can endanger fair competition, cybersecurity, consumer protection, and even civil rights. Therefore, both companies and governments must develop an honest and ethical model.

Copyright

Thanks to generative AI, businesses now use technology to create copyrighted material. However, determining whether the content was created by an author's creativity or AI technology can be challenging.

To ensure legal compliance, the Copyright Office has guided examining and registering works that include AI-generated material. The guidance outlines the necessary steps to take in such cases –

● Copyright laws protect human-created works, but with the increasing use of AI in art and literature, there's a need to consider safeguarding AI-generated works.

● It's essential to determine whether AI's contributions were due to "mechanical reproduction" or the author's "original conception through AI."

● Applicants submitting materials for copyright registration with AI-based content must disclose this to ensure appropriate protection.

Open-source

Developers frequently rely on AI-powered code generators to assist with auto-completion or suggest code based on their tests or inputs. However, several challenges are involved in creating compliant AI models for these generators. –

● Is it considered copyright infringement to train AI models using open-source code?

● Should the developer or user be responsible for complying with open-source requirements?

● Should developers license their applications under open source if they use AI-based code?

Ethical Bias

The use of AI facial recognition technology has resulted in instances of racial discrimination. 

For example, in 2020, black individuals were mistakenly arrested due to a computer error. Google Photos labeled black people as "Gorillas".

These occurrences highlight the fact that even though AI technology is highly advanced, it is still created by humans with inherent biases.  

It is essential for companies developing such systems to be mindful of these biases and take steps to prevent them from being incorporated into their technology.

IP Infringement

Several cases have been filed against AI tools globally, accusing them of using third-party IP-protected content to train models or generate output.

Ensuring GDPR Adherence in an AI Project

Despite strict regulations, businesses often need help to build compliant AI models. This is due to various reasons, such as a lack of knowledge about compliance, developers' limited understanding, and sometimes ignorance.

Additionally, there may be functional reasons contributing to this issue. To gain a deeper understanding, we can examine some examples from the perspective of GDPR compliance for AI projects.

Purpose Limitation

According to GDPR principles, businesses are required to disclose the purpose for collecting and processing individuals' information.

However, it can be challenging as technology may use data to discover patterns and gain new insights that may not align with the original purpose of the data.

Discrimination

GDPR requires developers of AI technology to take measures to prevent any discriminatory impact resulting from their creations.

However, ensuring that the AI model does not produce any discriminatory or immoral outputs can be a daunting task, especially in today's rapidly changing social landscape, where ethical considerations are of utmost importance.

Data Reduction

According to GDPR, the data collected must be "adequate, limited, and relevant".

This implies that AI development teams must exercise caution when incorporating data into their models and should determine the appropriate amount of data needed for their projects.

Since this can vary, teams must consistently assess the type and quantity of data necessary to meet the data minimization requirement.

Transparency

Companies must be transparent about data collection and usage to give users a voice in the process. However, many AI models, particularly advanced software, are challenging to understand as they operate in black boxes, and their decision-making processes are unclear.

While technical issues can arise with AI development, it is crucial for businesses not to use them as excuses for creating flawed AI models. In order to prevent this from becoming a widespread practice, international AI laws and regulations have been put in place.

Over 60 nations have implemented such laws since 2017, keeping pace with the rapid implementation of new AI models.

How to Create an AI Model that Complies with Regulations

With the increase in global regulations surrounding AI, it is now essential for businesses to prioritize legal compliance when creating AI models. Companies can follow these steps when investing in AI development services to ensure their projects adhere to legal standards.

Ensure you have permission to access the data

When designing a model, it is important to prioritize users' privacy per AI compliance regulations.

This entails collecting only necessary data while clearly stating the purpose and duration of data collection. Most importantly, user consent must be obtained before collecting any data.

Maintain a record of accumulated data

To ensure compliance with AI regulations, businesses must accurately categorize and track the location and use of all collected PII.

This is necessary to protect users' rights to privacy. Additionally, businesses should have a system in place to identify which information is stored in each dataset to implement effective security measures.

Understanding Cross-Border Data Transmission Regulations

When an AI system involves cross-border data transfer, it is essential for developers to take into account the regulations in the receiving countries. They should create appropriate data transfer mechanisms that comply with these regulations.

For instance, if GDPR applies to data processing and personal data is transferred to a non-EEA country, a thorough transfer impact assessment must be conducted.

It is essential to use practical approaches when developing AI applications to ensure that the risks associated with AI technology are appropriately addressed.

However, it is crucial to remember that it is impossible to completely safeguard the application from every potential risk due to the varying contexts of each industry.

Therefore, the role of AI risk managers is crucial in determining when intervention is necessary.

We hope this article has provided you with a better understanding of what to anticipate from the legal framework concerning AI technology in the near future and how to prepare for a compliant AI model.

At Helmsman, our systematic approach to AI compliance checks helps businesses in mitigating legal risks such as copyright infringement, open-source compliance, ethical bias, and IP infringement. We understand that companies need to prioritize legal and ethical considerations while developing and deploying AI projects, to ensure the responsible and compliant use of AI technology in the ever-changing regulatory environment. 

By partnering with Helmsman, your business can stay ahead of the curve and confidently embrace AI solutions while staying compliant with legal requirements.

Nitya Umat

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