Expect the Unexpected from Explainable AI in the 21st Century

Analytics Insight explains the unexpected challenges from Explainable AI in 2021.

The emergence of cutting-edge technologies has introduced another form of AI known as Explainable AI or XAI in the global market. It is a set of frameworks that help human users understand and trust the interpreted predictions and solutions from machine learning algorithms. The advancements of AI technologies are creating challenges for humans to comprehend the entire process of receiving specific outcomes from these machine learning algorithms. The black box models are created from real-time data that are making it impossible for humans to understand the calculation process. Sometimes the functionalities of ML models or neural networks are difficult to comprehend due to the complicated process. But it is essential for companies and start-ups to have a complete understanding of the rapid decision-making process. It is not often suggestible to blindly trust the AI models because their performance can change if there is a shift in the type of data or biased results based on the demographic and geographic segments. Thus, Explainable AI is the key requirement to promote end-users trust in large-scale implementation of AI models with appropriate explainability and accountability.

Explainable AI helps organizations to make the stakeholders understand the types of behaviors of AI models through monitoring model insights. There are multiple benefits of Explainable AI such as simplifying the complicated process of model evaluation, continuous monitoring and managing AI models to optimize business insights, and mitigating risks of unintended bias by keeping the models explainable and transparent. That being said, certain concerns with Explainable AI are rising too.

The first concern is the primary function of Explainable AI – explanation with transparency. This policy is becoming a threat for organisations that are continuously innovating new AI models or technologies with machine learning algorithms. The reason is that the creators have to explain and be transparent about the whole process and performance of the whole model to the stakeholders for a better understanding. The firms do not want to disclose all types of confidential information, trade secrets, and source codes to the public for security concerns. Then what will happen to the intellectual property rights that distinguish each company from one another? This is one of the unexpected challenges from the Explainable AI to innovators and entrepreneurs.

The second concern is that machine learning algorithms are highly complex and intangible in nature. Software developers or machine learning engineers can make common people understand the process of creating algorithms but the inner tangible process is very difficult to explain. Customers use these AI products subconsciously in their daily life such as face recognition locks, voice assistants, virtual reality headsets, and so on. But do they really need to know the complicated process in this fast-paced life? This information tends to become a little uninteresting and time-consuming to some stakeholders.

The third concern is for organizations to tackle different forms of explanation for different users with different contexts. Even if any company wants to follow the Explainable AI policy of making people understand the algorithms, different stakeholders can ask about different explanations such as technical details, functionalities, data management, factors affecting the result, and so on. The explanation should reflect the needs and wants of the stakeholders effectively for better stakeholder engagement. But sometimes it is impossible for organizations to answer so many questions at one time.

The fourth concern is receiving unreliable outcomes from these black boxes. Users should trust business insights from AI models, but it consists of potential risks. The system can generate misleading explanations due to a change in data. Then, the users will trust the error with utmost confidence that can lead to a massive failure in the market. These explanations are useful for the short term but not for the long-term plans.

That being said, despite the unexpected challenges from Explainable AI, companies can consider these five essential points to drive appropriate insights from AI models— monitor fairness and debiasing, analyze the models to drift mitigation, apply model risk management, explain the dependencies of machine learning algorithms as well as deploy the projects across different types of clouds.