Artificial intelligence (AI) rapidly changes business landscapes in today's technologically advanced world. Yet, the intricate workings of AI often remain concealed in a black box, making transparency a top concern for businesses and regulators.
A Critical Trust Issue
A 2021 IBM report (Global AI Adoption Index 2021) highlights that 90% of businesses utilizing AI urgently need more precise insights into AI's operations. Over 75% of IT professionals further assert the importance of trust in AI's fairness, safety, and reliability. These concerns emanate from a profound need for more clarity about the processes AI algorithms employ to arrive at their conclusions.
The Economic Implication: Prediction as a Commodity
Drawing from the book "Prediction Machines," (Agrawal, Gans, and Goldfarb 2018), one can understand AI's value through the lens of economics. AI, at its core, is a prediction tool. Businesses can reap significant economic benefits as their prediction capabilities improve and become cheaper. However, for these predictions to be effective and trustworthy, clarity and transparency in how they are made are paramount.
The Rise of Explainable AI (XAI)
In scenarios where significant decisions hinge on AI predictions, understanding the 'why' behind those decisions becomes indispensable. This understanding is the premise of explainable AI (XAI). The growing industry of XAI aims to make AI's decision-making transparent, with projections suggesting that XAI providers could see revenues exceeding $14 billion by 2025.
The Dividends of Transparency
Beyond just elucidating the inner workings of AI, XAI amplifies the technology's credibility. As "Prediction Machines" emphasizes, trust in AI predictions can lead to more informed decision-making. When companies understand the limitations and strengths of AI predictions, they can utilize them more effectively, optimizing outcomes and minimizing risks.
XAI doesn't just unravel the mysteries of AI; it makes the technology more reputable. It is naive to think that prediction algorithms won't need governance and be auditable. Trust is the underlying catalyst for this interest in XAI, especially for systems that make impactful decisions without thorough evaluation.
It's alarming how many are inclined to accept AI explanations without question, based on the incorrect presumption that they grasp the underlying mechanisms. Therefore, it's crucial that AI explanations not only elucidate how the model operates but also clearly outline its constraints. Prediction algorithms are only as good as the data that trained them.
The Cost of Complacency
In 2018 a self-driving Uber ran over and killed Elaine Herzberg as she walked her bicycle across a road in Tempe, Arizona. The AI training data had never included data on the exact circumstances the algorithm encountered that fateful night. The Uber didn't even stop after hitting Elaine and continued to drag her under the car. When AI/ML algorithms come across something new, they barf, and the consequences have been deadly.
Consumer Trust and AI Adoption
Transparent AI models can garner increased consumer trust. Agrawal and his co-authors point out that as the cost of prediction drops with advancements in AI, the value of judgment – human decision-making based on AI predictions – will escalate. For consumers to trust these judgments, they need to trust the underlying AI models, underscoring the importance of transparency.
AI's burgeoning influence in business is undeniable. However, as "Prediction Machines" explains, the real power of AI lies in its predictive capabilities. For these capabilities to be fully harnessed, trust and transparency are imperative. As we stand on the cusp of an AI-driven future, ensuring that these systems are transparent, ethical, and understood is not just an aspiration but a necessity.