The past decade has ushered in tremendous and unprecedented political, social, and economic shifts which have upended the business world. To adapt to this rapid change, companies rely on digital transformation projects to upgrade and enhance every aspect of their operations.
One of the critical digital transformation tools executives hope will help them compete in the digital future is Artificial Intelligence (AI). Confidence and excitement are high on how AI will benefit organizations, but very few truly understand how it works and where it can go wrong. For example, while AI can be beneficial, it can also introduce unwanted biases that can skew decision-making—or worse, create regulatory compliance issues.
How do companies evolve while ensuring their new solutions don’t create even bigger problems than they were meant to solve? Emerging legislation is pointing toward a need for greater transparency in AI-enabled applications. In some cases, it is already required by law—or soon will be. As a result, it may be necessary to document in detail why, for example, a benefits claim was denied —including all the predictive factors that went into the decision.
Most companies that rely on machine learning predictions don’t bother thinking about the “why” behind them, but this transparency is crucial. Why the “why?” It’s not just good to have—it’s necessary information.
In a recent webinar, InRule examines these topics and how xAI Workbench, our suite of modeling engines, helps companies leverage powerful machine learning in their automated decisioning practice while addressing common challenges.
In this webinar, InRule’s Theresa Benson and Danny Shayman discuss the differences between declarative and non-declarative Artificial Intelligence, present market trends that are elevating the need for transparent AI, and why explainability is critical or companies looking to leverage AI in their digital transformations.
Market trends are driving the need for Explainable AI
While Artificial Intelligence has the potential to drive business growth in myriad ways, various factors are shaping how AI can be used. In each case, the way forward is clear: transparency and explainability are necessary components for the future of AI-driven applications.
Political trends in AI
Legislation and initiatives are being introduced globally around transparency and the “right to explanation” when AI and/or decision automation is used in public policy applications. For example, if your business works with the government, you may have to explain how certain decisions are made when AI is part of the calculations.
For example, the new decriminalization of certain offenses is leading to the widespread need for sentence recalculations. More than 20 states have passed reforms related to marijuana expungement, and each of those tens of thousands of cases needs to be examined individually. Even though AI is the most efficient approach, these judgments can’t be turned over to “black box” AI; a level of AI explainability is needed for each decision.
Economic trends in AI
Huge swings in housing markets have changed the game for the mortgage and banking industry. The housing boom is a huge opportunity for lenders—as long as they can process applications faster than their competitors. AI is proving to be a vital tool for lenders to stay on top of this influx of business.
Companies are re-thinking their work-from-home policies with no end to the pandemic in sight. AI’s ability to generate insights into project data creates visibility into employee productivity and performance, especially for a remote workforce. AI-driven business analytics enables leaders to design, quantify, assess, and streamline projects.
All of these applications are incredibly useful, but businesses can’t just blindly accept these insights and take action without some level of transparency into how the AI arrived at its conclusions.
Social trends in AI
There has been a major new priority and focus on diversity and inclusion impacting every corner of business where employees are involved. New policies and legislation are being formed to ensure decision automation and AI don’t exacerbate bias in the way people are hired and managed.
Consumers are increasingly sensitive to purpose-driven brands, and companies are scrambling to find their niche. AI can help brands best understand which causes are important to their customers and guide their Corporate Social Responsibility (CSR) initiatives.
When it comes to using Artificial Intelligence in the people side of the business—hiring, firing, customer engagement, etc.—it’s essential to have a level of explainability to ensure no unwarranted biases aren’t introduced into the calculations.
AI can transform your business, but it needs to be transparent
Traditional AI suffers from the “black box” problem; it provides predictions, but it can’t tell you how it arrived at a given prediction and how the various factors weighed on the outcome. You get a prediction, and you get a level of confidence, but that’s where it stops.
InRule Technology’s xAI Workbench allows teams to develop a wide variety of machine learning models at a massive scale, each with unparalleled explainability. That means the models don’t just provide you with predictions but give you every single reason behind each one. It opens the black box to understand the insights behind the predictions better.
By understanding it, you can act upon it with decision logic to maintain regulatory compliance. In addition, you can be more confident in your automated decisioning because you will be able to check under the hood to make sure that everything is working how it was intended.
Watch the webinar to learn more about Explainable AI and see a comprehensive demo of xAI Workbench.