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Machine learning (ML) is a sub-set of artificial intelligence (AI). It is generally understood as the ability of the system to make predictions or draw conclusions based on the analysis of a large historical data set.

The exponential increase in the availability of data, and unprecedented computing power for processing this data, have contributed to moving AI from fiction to fact.

AI means a lot of different things to different people and a wide range of terms are usually involved when talking about it. Broadly speaking, there are two levels of AI – specific/weak and general. Most business applications involving machine learning refer to weak AI. In order to make sense of the AI landscape, it can be helpful to learn what the different terms refer to.

A figure of circles describing how the different terms are related: machine learning; deep learning; natural language processing; artificial intelligence; data analytics; robotic process automation. Artificial intelligence is the bigger outer circle with machine learning as a smaller circle within. Inside the machine learning circle are two smaller circles - deep learning an natural language processing. Circling the the border of the artificial intelligence circle is data analytics with half of its circle being inside the artificial intelligence circle and half of it outside. In the bottom right corner is the robotic process automation circle - not connected to the other circles.


ML is being increasingly used in accounting software and business process applications. And as a finance professional it is important to develop an appreciation of all this.

Ethical considerations

Professional accountants need to consider, and appropriately manage, potential ethical compromises that may result from decision making by an algorithm. They must remain engaged in AI and its component parts, including machine learning.

The ethical challenges posed by ML are explored in this section by focusing on five areas:

  • Dealing with bias
  • Strategic view of data
  • Assigning accountability 
  • Looking beyond the hype
  • Acting in the public interest

Skills in a machine learning environment

The ability of AI to take over jobs is a narrative often recited in the media. And there is certainly some truth about the ability of these technologies to do a variety of tasks more efficiently.

But even sophisticated technology such as AI struggles to replicate the full contextual understanding and integrated thinking of which humans are capable.

Now is a good time to start building greater knowledge and awareness in this area. The technology has moved beyond unrealistic fantasy to real business applications. Some will embrace it. Others will fear it. But only the reckless will avoid finding out more about it.

ACCA author, Narayanan Vaidyanathan