Now consider that it is possible to set up bot-based accounts to rate programmes or products millions of times. Think how easy it is to change the recommendations you receive by buying something for someone else. At that stage, it would not be difficult for someone to develop ‘misleading’ data that would directly feed into AI systems to make them produce faulty predictions.Ĭonsider, for example, Amazon or Netflix’s recommendation engines. However, what happens when this training process is automated? This does not very often occur during development, but there are many occasions when we want models to continuously learn from new operational data: ‘on the job’ learning. Read here Data poisoners attack automation This article will explore how AI vendors can improve the understanding of machine learning for the benefit of end-users. Improving understanding of machine learning for end-users This means that, as far as possible, the data used for training genuinely reflect the outcomes that the developers want to achieve. They thoroughly examine and explore the data, remove outliers and run several sanity and validation checks before, during and after the model development process. As a rule of thumb, when more data are available to train the model, its predictions will be more accurate and stable.ĪI systems that include machine learning models are normally developed by experienced data scientists. The model can then use what it has learned to predict the future. This data ‘teaches’ the model to learn from the past. From this data, we already know the outcome that we would like to predict in the future and the characteristics that drive this outcome. We train these models to make predictions by ‘feeding’ them with historical data. Back to basics with machine learningīefore we discuss data poisoning, it’s worth revisiting how machine learning models work. To guard precious data against it, businesses must fully understand the severity of the threat, what it takes to poison data, and how they can protect against it throughout the whole process of creating AI systems. The key to a successful antidote lies in more than simply fixing the problem after it has occurred.

But another trend could pose a threat to the trustworthiness of those systems: data poisoning. If machine learning isn't properly managed, data poisoning can be a threat to infrastructure.Īn increasing number of organisations are turning to machine learning models to aid the development of their AI technologies.

Poison antidote