Ensuring AI Compliance & Optimizing Performance with Observability

Mar 25, 2024 | by R. Bentivoglio, Technology

What if, just a few years ago, someone told you that companies would have to start deeply monitoring their AI artefacts to meet regulatory requirements?

Generative AI and its surrounding technologies, which emerged last year, are driving the widespread adoption of AI and the subsequent need for clear and stable regulation.

 

Companies will soon be required to provide various types of evidence about their use of AI, at least in EU countries for now. They will have to truly demonstrate an ethical and responsible use of AI. To achieve these goals, they will certainly need to define an ad-hoc strategy and develop a comprehensive program on top of it.

Non-compliance with the rules may lead to fines ranging from 35 million euros or 7% of the global turnover to 7.5 million or 1.5% of turnover, depending on the infringement and size of the company.

It is evident that controlling AI has become of paramount importance, at least from a financial point of view. However, is financial risk the only potential consequence a company may face for failing to comply with regulations?

The Risks of Uncontrolled AI

 

  • Legal liability: Companies can be sued for damage if their AI systems cause harm or discrimination. This could entail monetary damage, injunctions to stop using the AI system, or even criminal charges.
  • Regulatory fines: Companies can be fined by government agencies if they fail to comply with AI regulations. The amount of the fine can vary depending on the severity of the violation and the company’s size.
  • Reputational Damage: Companies can suffer reputational damage if their AI systems are found to be biased, unfair, or otherwise harmful.

In addition, the company should consider the following indirect risks, to name a few:

  • Drop in sales and loss of market share: Customers may lose trust in companies that use AI that is not trustworthy or ethical. This could lead to lost customers, investors, and partners and eventually loss of market share.
  • Difficulty in recruiting and retaining talent: Companies may find it difficult to recruit and retain top talent if they are not seen as being responsible and ethical in their use of AI.
  • Inability to access new markets: Companies may be unable to access new markets if they are not compliant with the regulations of those markets.
  • Failure to meet industry standards: Business may fall behind their competitors if they are not compliant with the industry standards for AI ethics and responsibility.

The AI Compliance Solution

 

To mitigate these risks, companies should develop and implement comprehensive AI compliance programs. Drawing from industry-defining experiences such as the GDPR in data privacy, it is crucial to adopt solutions that can effectively manage and control AI systems in accordance with regulations.

In addition to consistently applying thorough data governance and striving to implement Responsible AI, adopting ad-hoc strategies is essential to mitigate many risks.

The MLOps Approach

MLOps methodologies play a crucial role in helping organizations meet regulatory requirements. They achieve this by automating and standardizing AI development and deployment, monitoring and tracking AI model performance, providing traceability and explainability, supporting human oversight, and fostering collaboration.

Fortunately, there is a wealth of excellent tools focused on the development and deployment phases. However, the same cannot be said for the subsequent stages. Finding comprehensive tools that specialize in the production lifecycle of AI proves challenging. This challenge is particularly pronounced in the field of Generative AI Observability, which remains under heavy research and development.

How Radicalbit Can Help

 

So, what are the benefits of Radicalbit, the-ready-to-use MLOps & AI Observability platform?

  • First, it provides a safe environment for experimenting with generative models, thanks to a visual playground for tuning model parameters. It also provides an easy way to define test sets aiming to validate the quality of the results.
  • Second, it provides a unified platform for deploying, serving and scaling all types of AI workloads, varying from machine learning artefacts to deep learning, computer vision, and generative AI.
  • Finally, it offers a comprehensive dashboarding system to keep a close eye on the behaviour of the models and their running costs.

Radicalbit helps you increase the confidence in the AI predictions and also react to potential issues, protect your business from legal liability, reputational damage, and other risks.

Why do we need MLOps?

Why do we need MLOps?

MLOps and AI infrastructures are topics that have been widely discussed in recent months, even more so after the rise of technologies around LLMs like ChatGPT. In this blog post, we’re going to give a short and gentle introduction to these concepts by introducing their basic aspects.

Radicalbit joins World Summit AI 2023!

Radicalbit joins World Summit AI 2023!

We are thrilled to announce that Radicalbit will be joining World Summit AI in October 2023 as a sponsor and speaker. Alessandro Conflitti, Head of Data Science at Radicalbit, will deliver an address entitled “Zen & the Art of Drift Detection: How Not to Lose Your Sleep When Drift Occurs in Your Data Stream ”.