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Explainable AI - What it is and Why it’s Important

Computer_Human_Trust

The ripple effect of advancements in technology is quickly shaking up the marketing scene in a big way. Companies and marketing consultancy firms have been at the forefront of ushering in the new technology era through embracing AI in marketing. However, not all are well versed with the idea of artificial intelligence in marketing, with many viewing it as a black box delivering recommendations with little understanding of how they were arrived at.

Many in marketing leadership recognize that AI can increase efficiency and insights derived from a marketing campaign. The challenge, however, is the question of how they can trust the decisions made by an AI system and act on them? This is in the light of the fact that AI-driven decision recommendations result from complex processes, and to the end-user, the decisions can sometimes be opaque. This is where explainable AI comes in.

What Is Explainable AI?

Explainable AI, abbreviated XAI, is a form of artificial intelligence designed to explain the process of its decision-making and the rationale behind its recommendations in terms that users can understand. The mode of operation of XAI is based on the following facets:

  • Weaknesses and strengths of the program
  • The specific method the program used to reach a particular decision
  • The rationale behind selecting a particular decision and not other alternatives
  • How much trust can be accorded to the different choices made 
  • Errors common with the program
  • Possible solutions to the errors

Explainable AI opens a new plane in the human-machine collaboration platform. With Explainable AI, marketers and organizations can move on with the implementation of AI decisions faster and more accurately because they understand the reasoning behind it. XAI does not intend to replace human input in the process but is a rather complementary asset in marketing decisions.

Does XAI Matter to Marketers?

Currently the explainability of an AI model is primarily dictated by the degree to which marketing leadership wants to understand the process before implementing a decision. Marketers might not be concerned with how the AI system runs, but they may want to have a grasp of the elements that will influence the suggestions and decisions made by the system. Having knowledge of the details helps the team plan for actions to take in order to maximize results of executing program strategy and tactics.

XAI matters to marketers because understanding the factors behind a recommendation by the AI model simplifies the process of model interpretation. XAI also helps marketers make sound decisions about strategies and more easily educate an organization’s leadership team on campaign results. With XAI, the users can justify why they adopted a given strategy or adapted tactics based on insights learned.

Principles of Explainable AI

Explainable AI runs on four principles:

Explanation

This principle gives XAI the obligation to give a detailed explanation by providing evidence and the rationale behind outputs provided. The explanation principle seeks no quality of explanation from the XAI, but rather it just wants the system to be able to provide an explanation of itself and the decision. Other principles guide the quality of explanation.

Meaningful

It seeks to ensure that the end-user of a decision can understand the explanation given by the XAI. The meaningful principle establishes the need to customize the explanation for various audiences and not simply present a rigid all-encompassing explanation that may not be relevant to a broad audience.

Explanation Accuracy

This principle helps in regulating the quality of explanation provided. The main emphasis of this principle is an accurate explanation instead of decision accuracy. Explanation accuracy principles enlist the Meaningful principle to regulate the quality of decisions floated by the XAI.

Knowledge Limits

It mandates that a system defines its knowledge limits, which means declaring any specific instances that the system is not programmed or authorized to operate. The key concern of the knowledge principle is to avert instances of inaccurate explanations and inaccurate outputs.

Conclusion

XAI is the key to the next level of complimentary human-machine collaboration in businesses. With XAI, humans will more fully comprehend the basis and rationale of decisions and outcomes generated by AI technology. This knowledge gives stakeholders an edge in developing strategies and explaining their decision to higher leadership echelons. When upper management understands how AI recommendations were reached, continued funding of AI programs is more likely to occur. 

Embracing XAI is an important step towards improving the acceptance of AI in decision support. Incorporating AI, however, can be frustrating if the process by which recommendations are made and decision options considered are not handled by professionals who understand its dynamics. The Matters Group works with growth-minded companies seeking to learn about and leverage the power of artificial intelligence in marketing. 

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