Managing Business Risk with AI
Developed countries in AI and cyber capabilities have a clear head start in establishing the control mechanisms to provide security for their citizens. Unfortunately maintaining that comparative advantage requires a significant ongoing commitment from a plethora of resources.
First of the three specific risks includes hidden biases, not necessarily derived from the part of the designer but from the data provided to train the system. For example, if a system learns which job applicants to select for a job by using a data set of decisions made by human recruiters in the past, it may unknowingly learn to perpetuate racial, gender, ethnic or other biases. These biases may not appear as an explicit rule but embedded in interactions among the thousands of factors considered.
The next risk is that unlike traditional systems, neural networks deal with statistical truths rather than literal truths. Thus it makes it difficult to prove with complete certainty that a system works in all cases, particularly in situations where it is not represented in training data. Limitations to verifiability can be a concern in mission-critical applications.
The third risk is that when learning systems make errors, diagnosing and correcting the precise nature of the problem can be a bit difficult. What led to the solution set may be complicated, and the solution may be far from optimal. If the conditions under which the system was trained to happen to change the appropriate benchmark is not the pursuit of perfection, but rather, the best available alternative.
Risk managers are more prone to integrate unknown unknowns into their risk calculations, but this presumes that they do have a firm grounding in the subject matter.
For instance, as cyber risk evolved, many risk managers had the opportunity to become more familiar with what the risk is. The insurers have had time to develop new insurance products to address these risks.
Risk managers need to become more knowledgeable about the threats that continue to be produced from AI. Some organizations devote resources to develop these systems internally, but few recognize the need to anticipate the threats to allocate resources specifically designed to address such threats.
Risk managers have a vital role to play by ensuring that management is well aware of the potential threats while proposing solutions for those threats to be neutralised.
The AI world that gets created is kinder to organisations that excel at embracing the technology and anticipating its impacts. In future, the organisations who attempt to maintain this wall between human and machine are going to be at an ever-greater competitive disadvantage. In comparison with their rivals, who prefer to break this barrier and make use of Artificial Intelligence in every possible way to integrate their capabilities with those of humans effectively.
These organizations that can quickly sense and respond to opportunities will acquire the opportunities in the AI landscape. In the near term, AI isn’t replacing risk managers, but risk managers who use AI are going to replace the rest.
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