It’s no secret artificial intelligence is transforming business and life in general. AI is empowering organizations to raise productivity, lower costs and broaden horizons. At the same time, human oversight and control is essential. Beyond leveraging machine learning and automated decisioning, ensuring good outcomes and fully understanding predictions requires lifting the hood and inspecting the source data upon which those predictions and decisions are based. In a word, truly optimized AI requires explainability.
Explainability answers the ‘why’ behind automated outcomes. AI platforms that deliver full transparency enable subject matter experts, data scientists and tech staff to truly understand its dynamic power. Some prime reasons to add explainability to your AI:
Failing to dig below the surface of predictions may miss true paydirt – Yes, machine learning can effectively detect customers ready to churn. But understanding why they churn can be infinitely more enlightening. Will making a small change in user experience have an outsized impact? What timely offer will entice them to stay? Will changing a single rule parameter dramatically improve conversions? You won’t know for sure until you truly know for sure. Systems that facilitate easy exploration and visualization of big data reveal new insights into related factors, and discernments otherwise beyond reach. Beyond saving your bacon, AI explainability may generate innovative ways to bring home more of it.
No system is completely bias-free, none – Bias creeps into machine learning in the most-surprising, counter-intuitive ways. Amazon famously abandoned its initiative to screen job applicants based on an AI algorithm in 2017 after its programmers discovered it dinged female candidates based on the company’s prior history of hiring almost exclusively men for engineering roles. Human oversight is vital to ensure outcomes don’t go askew. At the same time, the greatest infiltration of harmful biases into automated systems is due to unintended human influences, primarily in the data chosen to feed into them.
So no, hitting “deploy” on decision logic and waving goodbye won’t work, as many organizations, who’ve based their rules logic on biased ML, have discovered in very-hard, very-embarrassing ways.
Coming laws and regulations will likely require AI transparency – Today’s lawmakers and regulatory authorities grew up in the digital age and are not likely to be tech-averse. However, its ubiquity has grown, so has its enmity from concerned taxpayers and consequently their representatives. Laws requiring AI transparency and full disclosure to applicants on their weighted decision factors are under consideration across America and the European Union. Having comprehensive explainability in place enables user organizations to stay in front of any oncoming regulatory wave.
Consumer trust is hard to earn, and for many impossible to regain – The only thing more important than an organization’s brand is nothing. Disney, Apple and Nike are among a precious few brands that are virtually built of granite, much too adored to be seriously tainted by scandal, as was the case with Apple and Nike and their use of lowly paid, poorly treated Chinese workers. However, the vast majority of businesses, even large concerns, would face serious peril in the face of any negative event that draws significant public attention.
Trust is equally important when it comes to technology. In a recent survey, 48% of college grads stated they do not trust AI. However, thanks to explainability users can trust AI, the predictions it makes and the decisions it influences. Users can look under the hood and see what inputs influenced each prediction and decision.
Now that responsibility for decision outcomes is increasingly shifting from workers to machines, leveraging AI explainability to assist human oversight will increasingly become a must-have capability. Transparency in AI decisions is good for everyone – customers, regulators and most-especially user organizations. In business as in life, trust is a very precious item.
AI tracks, reports and even recommends KPI actions – When determining key performance indicators and subsequent actions, nothing beats big-data predictions. Machine learning can recommend actions based on KPIs and visualize the data they’re based on. ML systems reveal the numbers behind the numbers, making huge data sets easily comprehensible through representative graphics. Through dynamic tracking, AI-powered systems facilitate a continuous positive feedback loop, updating and refining predictions as real-time data dictates.
Fulfilling corporate governance and public reporting requirements is no fun. That is unless you’re a powerful, user-accessible automation system capable of submitting accurate filings instantly, wherever they need to be filed. Getting reporting right demands the accuracy that comprehensive AI featuring Process Automation can provide. Soon, organizations not leveraging AI power for reporting, tracking and KPI actions will be at a disadvantage to the majority of their competitors who are.
Trust = Adoption = Faster ROI – Investing in AI transparency and bias protection is not just good ethics, it’s good business. According to an article by McKinsey, companies realizing the biggest AI return, those attributing at least 20 percent of earnings to AI, are the most likely to leverage some form of explainability. Organizations that establish consumer digital trust through such methods as AI explainability increase their profitability by up to ten percent or more. Explainability empowers humans to trace bad outcomes to their source and course correct accordingly, facilitating a most-positive feedback loop.
Clearly, the push for AI transparency and explainability will only increase. Consumer demand, government oversight, tracking and reporting advantages and proven bottom-line benefits are all driving the move toward automated systems equipped with robust, dynamic, user-accessible explainability. InRule Technology was among the first to offer explainable decisioning and machine learning, equipping our first customer with explainability over 20 years ago. Today, our robust, accessible explainable Decisioning, ML and Process Automation platform is integral to the AI-powered initiatives of leading mortgage lenders, insurance companies, government agencies, global airlines, state corrections departments, pharmaceutical manufacturers and specialty retailers, among a growing list of users. For more information on how our platform delivers the “why” behind the outcome, shoot us a line.
Better yet, start your explainable, AI-powered journey today. Request a free demo.
