Let’s accept it: Barely an industry left where AI technology innovation has not created wonders. They are bringing unimaginable things to the table at a dramatic rate. With AI advancements, it has become challenging for humans to understand how AI algorithms are driving results. The calculation process appeared as a Black box because neither data scientists nor engineers that have created algorithms unable to identify how the AL algorithms are computing the results.
The transparency in AI systems helps in understanding how AI algorithms bring the expected outcome. If the algorithms are not performing as expected, it’s easy to identify the problems and erase them. Here, comes the role of explainable AI.
Let’s understand what’s Explainable AI
ML models and neural networks that are impossible to understand for a human to interpret are now possible with explainable AI. It eliminates the long-standing risk in AI models training in the sense that production data won’t get deviated from training data. The explainable AI encourages users’ trust, auditability, and system performance while minimizing security, legal, reputational, and compliance risks.
Large organizations are increasingly preferring the implementation of explainable AI to ensure the model’s explainability, fairness, traceability, and accountability. For instance, when a vendor management team in an organization cancels the contract with a supplier due to some reason, then the supplier could know the reasons behind the decision through explainable AI.
How to embrace AI-based decisions?
Different industries embracing explainable AI to make intelligent decisions with stellar transparency. Let’s see how?
Healthcare organizations have to comply with HIPPA regulations to ensure patients’ data protection. Especially, precision medicines fall under this category where a patient’s genetics, previous medical health history, patient’s family’s clinical history, and other sensitive data are collected and leveraged. Here, the companies need to understand how AI algorithms are leveraging data to compute results. When the logic behind connecting the dots becomes well-known, the people would have more trust in the machine results as they know the stuff behind the actions of the machine.
Customer experience optimization
The AI software created by a Chicago-based company specializes in pattern detection and forecasting. It claims unbiased and predictive data analytics with the similarity-based learning methods that are used to train algorithms. It enables the machines to tell ‘why’ behind every projection and justify the conclusion drawn. The accurate identification of the variables used in decision-making makes the AI model more transparent.
AI-powered NLP algorithms analyze data, that’s both structured and unstructured like- inventory records and manuals respectively provided by IoT sensors. IoT sensors predict preventive equipment and send alerts to the concerned personnel, and explainable AI algorithms highlight the factors that brought variation in the performance to know why such conclusions are drawn.
Explainable AI plays a vital role in improving autonomous vehicles. It explains the cases where the accident is unavoidable, in addition to measures that can help in increasing passenger safety assurance. The factors that are impacting the decisions are brought to the limelight.
Challenge of explainable AI for enterprises to tackle risk areas
Companies are making efforts to make AI explainable, but it has not solved the problem completely. There are a lot of unresolved issues that exist in explainable AI space. The enterprises are facing technical challenges that need to fix before they embrace them.
Striking balance is difficult
Although explainable AI is making the decision process transparent to human observers. However, this AI approach makes the companies sacrifice algorithmic complexity and sophistication aspects because it involves reduced number variables usage in explainable AI models. On the flip side, it impacts accuracy level. Here, maintaining a trade-off between transparency and accuracy.
IP concerns are intolerable
As mentioned above that making an AI model explainable means compromising over data secrets. It deters the users from explainable AI leverage as it makes the solution transparent and can lose the competitive edge. The IP concerns slowing down the AI progress.
“Understandable AI” means a lot
Explainable AI is now easy to understand for data scientists and engineers, but when it comes to end customers, things will be different. The end users are not as tech-savvy as engineers and data scientists, which means making the AI model understandable for end-users requires companies to invest a lot in various things. For instance, documentation writers need to hire to make them understand complex technical concepts.
Testing & monitoring outcomes for enhanced transparency
The technological advances and strategic leverage of explainable AI can make AI comprehensible to humans. Large enterprises need to handle technology in parts, and continuous testing can make the decision-making more transparent. Here are a few steps you should follow-
- Develop explainable AI models with the following activities involved- collecting, classification, and training. Eliminate the features and their proxies that make the outcomes biased.
- Build a mechanism that describes the model’s explainability along with various methods for different activities. Depending on how the model is solving the problem, and the advantages and disadvantages of using the model help in creating a combination of models that are leveraged in the future.
- Define outcomes and methods, and then test them to identify which will monitor the AI model.