Covering Disruptive Technology Powering Business in The Digital Age

In decision-making, it is important for an individual to observe what is happening, consider all the variables and choose between options before ending up with a conclusion. The same applies to businesses; Companies should first inspect all the data that is coming in and make decisions from this.

With the exponential growth of data, that is becoming increasingly difficult and time-consuming to do manually, which is why organisations are utilising AI to automate their analysis and to effectively gain insights and predictions. However, the usual AI algorithms operate more in line with what they call a “black-box” approach, wherein system or device workings and processes are hidden and not understandable by humans.

Such an approach can leave the end-users clueless as to the real accuracy or inherent biases that may exist because a “black box” AI’s reasoning is opaque. It does not provide a clear explanation as to how it came to a certain result. As the technology continues to mature, organisations are realising that it is becoming more and more important to know how and why complex machine learning algorithms make decisions and come up with conclusions for ethical, legal and business reasons.

As companies rely more on AI to make highly important predictions and business decisions, the need to have explanatory capabilities in order for users to understand why certain decisions were made is becoming more crucial. For this reason, there is an emerging approach to AI that is more transparent and benefits both the technology and its users – called explainable AI.

Explainable AI has the ability to explain the reasoning behind its algorithms, show different variables that will justify its decisions and characterise the strengths and weaknesses of the whole process. Explainable AI allows users to understand the output, giving them better insights and make it easier for them to improve the algorithm of the AI.

The following are the main benefits of explainable AI to businesses:

Transparency

Explainable AI features transparent processes, unlike in a black-box approach. With this approach, users can feel rest assured that the thought process of the AI is not obscured, and they can easily explain the relation between input and output. Depending on the system, the paths that the algorithms take can be seen, from the root inputs, the probable options and the end results. This is all a vital step in creating trustworthy enterprise systems.

Regulations

Since black-box AI hides the processes behind its analysis, it can violate some regulations that require transparency in the system. There are now more regulations emerging that require greater transparency in what, why and how data is used, such as the EU’s General Data Protection Regulation (GDPR). Explainable AI will allow companies to adhere to these regulatory standards or policy requirements.

Trust

With decisions that are accompanied with understandable reasons, users can assess and justify the result the explainable AI gives. This will improve the overall AI implementation within an organisation, help build greater trust and confidence in the technology and safeguard against bias, as users can ensure that the AI system does not use data in ways that result in prejudiced or discriminatory outcomes.

There is a growing number of approaches to AI, with systems and algorithms that are becoming highly complex. Hence, if organisations do not start making AI more transparent, interpretable and most importantly, accountable for its decisions, the problems will only become worse as AI gets infused into more areas of operations.

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