Machine learning offers new opportunities, but with power comes responsibility - ethical considerations cannot be ignored.
A video summary of the web article.
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.
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.
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.
"On average for any given term: 62% of respondents had not heard of it, or had heard the term but didn’t know what it was or had only a basic understanding, 13% of respondents had a high or expert level of understanding."
Applications for ML
There are a variety of applications for ML. These could assist the work of professional accountants in various ways, for example:
• Intelligent book keeping
• Improving fraud detection
• Making sense of complexities in taxation
• Effective non-financial reporting