Artificial intelligence (AI) has turned into a highly pervasive technology, and it has been incorporated in a wide array of industries across the globe. The tough competition in the market and success stories surrounding AI adoption are among the few major factors that compel more and more enterprises to adopt AI in various aspects of their business.
Machine learning (ML), the key component of AI technology, has become powerful to the level of displaying superhuman capabilities on most human tasks. However, this superhuman performance comes with higher complexity in the AI and ML models, turning them into a “black box,” a decision-making model too complex to be understood by humans.
Today, ML models are deployed to replace human decision-making in areas ranging from driving cars to the prevention of crimes to advertising. They are also employed in decision-making in investments, loan approvals, and hiring employees. The decisions made by these black box systems have the potential to influence business decisions and impact many lives. Thus, they come with severe ramifications.
Table of Contents
What is Explainable AI (XAI)?
The dire need to make the decision-making process of the algorithms understandable to the stakeholders has become a significant part of every business. It can also help to gain their trust and confidence in an enterprise’s AI decision-making process.
This demand for transparency in the decision-making process of the AI and ML models resulted in an increased interest in Explainable Artificial Intelligence (XAI). XAI is a field of technology that deals with the development of methods that explain and help users interpret ML models. In simpler terms, Explainable AI is an AI model built to provide an easily understandable explanation of how and why an AI system has made a specific decision.
Today, every enterprise should place top priority on a clear understanding of the inner functions of their AI system. It helps them face the persistent challenges posed by bias, accuracy, and many more problems associated with AI systems.
Also read: The Struggles & Solutions to Bias in AI
The Significance of Explainable AI in Businesses
Explainable AI has greater potential and strategic value to drive various businesses. Some of the benefits, include:
Accelerate AI adoption
As the complex black box decision-making process becomes easily understandable by everyone, it can build the trust and confidence of stakeholders in the ML models. This, in turn, increases the adoption rate of AI systems across various industries providing a competitive advantage to various enterprises.
Provide accountability
Explainable AI lets business leaders easily understand the behavior of AI systems and potential risks associated with them. It makes the leaders confident to accept the accountability for the AI systems in their business. It can also help to garner sponsorship for future AI projects. Greater support for AI from major stakeholders and executives can put an enterprise in a better position to foster innovation and transformation.
Provide valuable insights on business strategies
Explainable AI can bring valuable insights into key business metrics such as sales, customer behavior patterns, and employee turnover among others. These insights on valuable data help evolve business goals and improve the decision-making and strategy planning of various enterprises.
Ensures ethics and regulatory compliance
Some enterprises are compelled to adopt Explainable AI due to the new regulatory compliance requirements. Others face growing pressure from customers, regulators, and industry watchdogs to ensure their AI practices align with ethical norms and publicly acceptable limits. The implementation of Explainable AI can safeguard vulnerable consumers, ensure data privacy, strengthen the ethical norms of businesses, and prevent both bias and loss of brand reputation.
How to Implement Explainable AI?
Here are the five guidelines to effectively implement Explainable AI in your enterprise. It can also be taken together as a roadmap with some major milestones that can guide an enterprise to deal with the limitations and risks associated with XAI.
Diversification of XAI objectives
It is ML engineers who currently develop Explainable AI technology by placing the priority on both the needs of them and their company. But all these should be within the framework of legal regulations and industry policies and standards.
Diversification with a broader array of XAI objectives requires both greater awareness of the objectives and a shift in the motive to accomplish them. In order to motivate this shift, it is critical to include the needs of stakeholders, users, and communities in the standards and policy guidelines of Explainable AI.
XAI case studies are excellent tools that can help entrepreneurs and developers alike understand and develop more holistic Explainable AI strategies. In addition, there are a wide variety of guidance documents, recommendations, and frameworks that can give a walkthrough along the key solutions to support XAI that are useful to different stakeholders.
Put XAI metrics in place
Several attempts have been made to assess the explanation of AI, but most of them are either expensive or focus on a smaller part of a “good explanation” and fail to bring light to other dimensions. Holistic measurement of effectiveness requires the combination of a comprehensive overview of XAI approaches, a review of the various types of opacity, and the development of standardized metrics. Besides, to assess explanations of AI, the specific contexts, norms, and needs in each case, with both quantitative and qualitative measures, should be used. This will help businesses hold themselves accountable and deploy AI successfully.
Also read: Data Management with AI: Making Big Data Manageable
Bring Down Risks
XAI comes with the elements of risk. Explanations may be misleading, deceptive, or maybe exploited by cybercriminals. They can also pose data privacy risks since they can take out information about the XAI model or training data. Competitors can easily replicate proprietary XAI models or use the models for further research.
Every enterprise should implement practical methods for both documenting and mitigating these risks. These practical methods must be a part of the XAI standards and policy guidelines. At times, in the case of business decisions with higher stakes, it is always better to avoid the need for deep learning models and Explainable AI technology.
The prioritization of user needs
Until now, the development of XAI has primarily served the interests of AI engineers and businesses. It has helped debug and improve AI systems but failed to let users oversee and understand its intricacies.
Every enterprise should prioritize user needs for profitable growth and to build trust in users. Some of the key considerations include clarifying the context of an explanation to users, communicating the unpredictability associated with model predictions, and enabling user interaction with the XAI explanation. Businesses can also consider incorporating valuable ideas from the theory of risk communication.
Explainable AI isn’t enough
While useful, simply possessing a better understanding of how a biased AI model arrived at a result will do little to attain trust in users. Trust can only be built alongside testing, evaluation, and accountability measures that should go the extra mile to expose and mitigate known problems. For instance, the 2017 Loomis v. The State of Wisconsin case revealed that the racial bias of a criminal risk assessment algorithm not only violated due process but also highlighted the gaps in accountability.
Independent auditing and updated policies and standards, among other accountability measures, will also be needed to promote lasting trust in users.
