How to Avoid Compliance Violations While Developing AI Products

Artificial intelligence has remained the top tech trend for several years through its contributions in data and machine intensive sectors like manufacturing, healthcare, and finance. However, it is only in the last two years that the technology has been witnessing a high interest from end users. With applications like image and text generators people are now able to produce visuals and texts in a single click.

While it looks like these AI platforms are producing material from scratch, that is quite not the case – these AI platforms have been trained on question snippets and data lakes that work by processing archives of text and images from the internet. Although useful to the end users, the approach comes with legal risks like copyright infringement, non adherence to open source licenses, and intellectual property infringement, etc. These risks that generative AI poses are not unseen by the governments across the globe that are constantly coming up with new rules and penalties around unethical AI models.

For a company preparing to launch their AI project or build a legally compliant application, it is critical they understand the risks and build a system that doesn’t fall under the legal radar at the back of these ethical issues. In this article, we are going to dive into the many facets of AI compliance during software development – the types of legal issues, ways to prepare for AI regulation, and the AI acts followed by different regions.

Also Read: How to build an ADA and WCAG-compliant application

What is AI Compliance?

It is a process that ensures AI-powered applications are compliant with the regulations and laws of the region it is operational in. here are the different factors AI compliance check consists of –

The Legal Issues with Artificial Intelligence

While on a micro level it may appear that the problems with end-users usage of AI are limited to plagiarism or access to unsharable data, on a macro level non AI compliance causes bigger challenges.

The threats stemming from using a poorly built AI system can affect fair competition, cybersecurity, consumer protection, and even civil rights. Therefore, it is critical for companies and governments to build a fair, ethical model.

Copyright

With the onset of generative AI development services, businesses have started creating copyright material through technology. The problem with this lies in the inability to understand whether the data has been generated with the creativity of an author or whether is it the AI who is the author.

To give this a legal compliance take, the Copyright Office issued Guidance on the examination and registration of works containing AI-generated material. It states the following –

• Copyright can only protect material produced by human creativity.
• In the case of works with AI-based material, it will be considered whether AI contributions resulted from “mechanical reproduction” or are an author’s “own original conception, to which they gave visible form through AI.
• Applicants carry a duty to disclose the involvement of AI-based content in the material submitted for registration.

Also read: Cost of developing an AI content detection tool in 2023

Open-source

AI-driven code generators often use AI to assist developers in auto-completion or suggesting code at the back of developer tests or inputs. Here are some challenges associated with developing compliant AI models around code generators –

• Does the training of AI models with open-source code mean infringement?
• Who is responsible for meeting the open-source compliance criteria – developer or user?
• Will AI-based code use by developers creating new software require the application to be licensed under open source?

IP Infringement

Globally, multiple infringement lawsuits have been filed against AI tools, accusing them of the fact that they are training their models or generating output on the basis of third-party IP-protected content.

Ethical Bias

There have been numerous incidents where AI facial recognition technology has led to racial discrimination. Whether it was the case where in 2020, black people were arrested because of computer error or Google Photos labeling black people as “Gorilla“. Irrespective of how smart the technology is, it cannot be ignored that it is built by humans with biases

Also read: How Explainable AI can Unlock Accountable and Ethical Development of Artificial Intelligence

For companies looking to build similar solutions, it is crucial that they do not let these biases come into the system.

GDPR Compliance for AI Projects

With this being said, it is crucial to understand why businesses fail to build compliant AI models despite strict regulations. There can be several reasons behind it, ranging from the inability to know the compliance, developers’ lack of understanding, and sometimes simple ignorance. However, there can be some functional reasons behind it too.

Let us look into a few of them from the outlook of GDPR compliance for AI projects.

Limitation of purpose

The GDPR principle makes it necessary for businesses to inform the data subjects the purpose for which their information is gathered and processed. The challenge with this is that the technology uses data to find patterns and get new insights, but that might not be the real purpose of that data.

Also Read: AI Regulation and Compliance in EU:  Charting the Legal Landscape for Software Development

Discrimination

GDPR requires AI developers to take steps against the discriminatory impact the technology can carry. While it is an ethical need of the hour, for a developer operating a fast changing social scenario, preparing the AI model against every discrimination and immoral output can become challenging.

Minimization of data

GDPR says that the information gathered should be “adequate, limited, and relevant”. This means the AI development teams should be very careful when using data for their models and must be clear on the quantity of data required for their project.

However, this is not predictable, so the teams must regularly evaluate the data type and amount which they need to address the data minimization requirement.

Transparency

Lastly, the users should have a say on how their data is being used by the third parties, for this, businesses will need to be clear on what data they are using and how.

The problem with this is that a majority of AI models operate in black boxes, and it’s not clear how they make decisions, especially when we talk about advanced software.

Even though these are all genuine technical issues, when it comes to IT ethics, it is critical that businesses don’t use them as shields to develop a defective AI model. To ensure that the practice doesn’t become mainstream, several AI laws have come into place on a global scale.

Almost 60 nations have introduced artificial intelligence laws and regulations since 2017, a line of action that matches the speed at which new AI models are implemented.

Here’s an infographic giving a brief view into those laws.

Now that we have looked into the challenges of AI development from the legal standpoint and the rough draft of applicable laws on a global level, let us get down to the ways you can build a compliant AI application.

Also read: How much does it cost to develop a legally compliant chatbot like ChatGPT

How to Develop a Compliance-friendly AI Model

Owing to the rise in AI regulations on a global level, it has become critical for businesses to focus on legal compliance when they build AI models. Here are some ways companies can ensure their project is legally compliant when investing in AI development services.

• Ensure you are allowed to use data

AI compliances state that users’ privacy should be the guiding principle of model design. This means that you should keep the amount of data you need to collect to a minimum, specify the exact reason for data collection, along with the time till when you will be using their data. Ultimately, it is important to note that the users should give their consent for data collection.

• Explainable AI methods

This approach helps solve the black box effect by helping humans understand what is inside the AI systems and how the model makes decisions. This, in turn, helps researchers know the exact amount of data they will be requiring to improve the model accuracy to meet the data minimization requirements.

• Keep track of collected data

AI compliances require businesses to know the location and use of collected PII. Correct data categorization is needed to comply with users’ right to protected information. Moreover, businesses must have an approach to know which information is stored in which data set to prepare accurate security measures.

• Understand inter-country data transmission rules

When there is cross-border data transfer in an AI system, the developers should consider the regulations that will apply in the receiving countries and build appropriate data transfer mechanisms accordingly. For example, if GDPR is applicable to data processing and the personal data gets transferred to a non-EEA country, a proper transfer impact assessment should be conducted.

Using such approaches when developing AI applications goes a long way in ensuring that the risks associated with the technology are addressed properly. However, companies and regulators should be mindful of the fact that it is impossible to safeguard the application from every potential risk as the industry context works on a case-by-case model. Because of this, the role of AI risk managers will remain critical as they will be able to gauge when an intervention is needed.

[Also Read: Harnessing the Power of AI for Enhanced Risk Management in Business]

We hope that the article helped you understand what to expect from the legal structure surrounding AI technology in the coming time and the ways to prepare yourself for a compliant AI model.

FAQs

Q. Are there any legal issues with artificial intelligence?

A. Yes. There can be a number of legal and ethical issues associated with a poorly built artificial intelligence model.

• Copyright
• Open-source license misuse
• IP Infringement
• Ethical bias like racial discrimination.

Q. Why is it difficult to make a legally compliant AI model?

A. On the technical end, it can be difficult to build a legally compliant AI because while the technology uses customers’ data to find patterns and get new insights, it gets difficult to ascertain the real purpose of that data. Next, the AI development teams can never be certain on the quantity of data required for their project. Lastly, a majority of AI models operate in black boxes, and it’s not clear how they make decisions, especially when we talk about advanced software.

Q. How to ensure legal compliance in AI?

A. Although the laws and regulations around AI legal compliance are constantly evolving, here are some things you can do to ensure that your model is closest to being compliant –

• Ensure you are allowed to use data
• Explainable AI methods
• Keep track of collected data
• Understand inter-country data transmission rules

THE AUTHOR

Sudeep Srivastava

Co-Founder and Director