Can You Measure the Integrity of Your ML Programme?

ML Programme

No matter what your company is trying to achieve, artificial intelligence and machine learning have the ability to completely change the landscape you are working within, pushing you further towards your goals by entirely automating previously time-consuming assignments.

To get to that point, however, you need to know that you can trust it.

If you are using an ML programme, large amounts of your company’s data are being consumed and analysed, with the ML programme learning its patterns and trends before making accurate, automated decisions.

With this in mind, you need to be able to trust the AI’s decisions, ensuring that you have both data integrity and ML integrity before applying the technology efficiently to your operations.

What Is ML Integrity?

While AI is a very exciting prospect for a lot of businesses, there’s no denying that it is being used as a “buzzword” – or “buzz letters”, for that matter.

By this, we mean that AI is such a hot topic in the world of technology and businesses that a huge number of companies want to jump on the bandwagon without really knowing how they can implicate it. For them, AI is more of a marketing ploy to entice consumers, rather than a technology that can actively change the way they do their business.

The problem with this is that they start using AI and ML without ensuring it is responsible yet. So what is responsible AI? Well, responsible AI is a term used to describe AI technology that has been developed and deployed with a firm stance on functionality, ethicality, and legality.

In other words, it is developed to be trusted with large amounts of data, working inside a strong governance framework, and without the risk of poor data quality, bad decision-making, loss of security, hallucinations, and ML bias.

How Is ML Integrity Achieved?

A lot of businesses wrongly believe that ML bias is inevitable, and any form of integrity can only be achieved through sufficient data integrity – if the data is correct, then the ML will be too.

But this is not the case. Of course, every company needs data integrity, especially if you are looking at ML tech to analyse historical data and apply it to your company’s future. But it’s important to know that ML integrity is available now, straight off the bat.

What you need is a specific responsible AI platform that can enable governance across your AI systems, ensuring that ML remains correct, compliant, and, most importantly, transparent.

The problem with AI systems is that they’re complex, which means observability becomes difficult. With a responsible AI platform, however, you can observe your AI through centralised management, allowing you to keep a finger on its pulse and an eye on its integrity. This not only ensures ML integrity, but also harnesses the full benefits of AI integration, making it more streamlined, seamless, and successful.

Once again, AI and ML have the potential to push your business towards the future. But you need to take the necessary steps first to ensure that the future is a bright one.

[Image source:]

Leave a Reply

Your email address will not be published. Required fields are marked *