The cost of getting AI wrong extends beyond the financials—lost revenue, fines from compliance failures—to reputational, brand, and ethical concerns
by Martin Sokalski 20 August 2019
Key business decisions at scale have a determining effect on success; as an example, should we approve a credit card for a customer?
Among the decisions for each customer: the annual percentage rate, the spending limit, and a long list of other factors. Machine learning models are typically making these decisions for millions of customers.
In a very real sense, given the scale, the business is in the hands of a handful of smart data scientists—and the machines they build and train—using ground truth created from historical loan data.
Autonomous algorithms: then vs. now
Most algorithms today are relatively simple and deterministic: they produce the same output from a predetermined set of states and a fixed number of rules. The approaches for evaluating them for validity and integrity are largely established and adopted. In fact, in our estimation, over 80 percent of the leading practices needed to maintain their accuracy and effectiveness are known.
Think of expert systems in manufacturing. Think of actuarial science that uses deterministic rules or decision tables in insurance. Think of robotic process automation in financial services.
It isn’t that hard to determine whether the conclusions they reach are acceptable—and sound and scalable supervision is relatively easy.
These rules can get very complex, especially when the number of attributes (also known as features, or variables) in the data or the number of records increases.
Machine learning and deep learning—and other types of AI—are creatures of a different kind. They are trained to learn from data (commonly referred to as ground truth) instead of being explicitly programmed, which means they can “understand-learn-uncover” the nuances and the patterns in the data, they can handle a very large set of attributes, and are often significantly more complex in how they do what they do.
Think of training a prediction model from a set of a million past loan applications, which in turn uses 100 attributes. Think of detecting a tumor from a million MRI images. Think of classifying emails. Once trained and evaluated, these models can be provided with new or unseen data from which they can make predictions. They are probabilistic in nature and respond with a degree of confidence.
While all of these aspects are good, it can be unclear what the models are doing: what they learn, particularly when employing opaque deep learning techniques such as neural nets, how they will behave, or whether they will develop unfair bias over time as they continue to evolve. That’s why understanding which attributes in the training data influence the model’s predictions has become very important.
Algorithmic risk: trust in the machine
Let’s take a closer look at a potential problem for the business leader in the loan division of a big financial firm.
If an error hides within an algorithm (or the data feeding or training the algorithm), it can influence the integrity and fairness of the decision made by the machine. This could include adversarial data or data masking as ground truth.
The business leaders are on the hook for preserving the brand reputation for the firm, even as the AI models increasingly make decisions that might not be understood or in line with corporate policies, corporate values, guidelines, and the public’s expectations. Multiply these issues by the number of algorithms the loan division is utilizing. This is when trust weakens or actually evaporates.
Keeping AI in check
A number of techniques, including those based on renormalization group theory, have been proposed. As models across AI tasks—including computer vision, speech recognition, and natural language processing— become more sophisticated and autonomous, they take on a higher level of risk and responsibility. When left untrained for long periods, things can go awry: runtime bias creep, concept drift, and issues such as adversarial attacks can compromise what these models learn. Imagine compromised MRI scans or traffic lights being manipulated in a smart city.
Continuous-learning algorithms also introduce a new set of cybersecurity considerations. Early adopters are still grappling with the magnitude of risks presented by these issues on the business.
Among the risks are adversarial attacks that hit the very foundation of these algorithms by poisoning the models or tampering with training data sets, potentially compromising privacy, the user experience, intellectual property, and any number of other key business aspects. Consider the impact on lives or an environment of an adversarial attack in medical devices or industrial control systems. Tampering with data could disrupt consumer experiences by providing inappropriate suggestions in retail or financial services. Such attacks might ultimately erode the competitive advantage that the algorithms were intended to create.
With complex, continuous-learning algorithms, humans need to know more than just the data or attributes and their respective weights to fully realize the implications of the AI getting it wrong or going rogue; they need to understand aspects such as the context and intended purpose under which the model was developed, who trained them, provenance of the data and any changes made to it, and how the models were (and are) served and protected. And they need to understand what questions to ask and what key indicators to look for around an algorithm’s integrity, explainability, fairness, and resilience.
This opinion was originally published as part of Controlling AI, a KPMG research campaign investigating responsible design and operation of AI programs.
Martin Sokalski is a global leader for KPMG’s Emerging Technology Risk practice. He helps organizations around the globe embrace the “art of the possible,” enabled by emerging technologies like artificial intelligence, by facilitating ideation, innovation, and responsible adoption.
Martin regularly speaks at conferences and contributes to thought leadership on artificial intelligence, digital transformation, and emerging technologies. He believes that adoption of AI at scale is currently inhibited by lack of trust and transparency, explainability, and unintended bias and aims to work with industry leaders to solve for that challenge.