Collingridge’s Dilemma resembles the mysterious title of Sherlock Holmes. In fact, this is one of the best explanations for the difficulty of controlling high risk technology. Essentially, it is an imbalance between incomplete information and established power.

“While it is easy to change, we cannot foresee its need. When the need for change became evident, it became expensive, difficult and time consuming, ”says researcher David Collingridge. written in his book. NOT. Social control of technology..

How can you deal with things known and unknown? This dilemma arises today when regulators attempt to assess the impact of artificial intelligence on the financial sector. The benefits of AI are clear, but the risks are often ambiguous in terms of increased efficiency and improved service.

In a previous treatise published while attending the Massachusetts Institute of Technology, Gary Gensler He warned that the widespread adoption of deep learning artificial intelligence models could increase the vulnerability of financial systems. Gensler currently chairs the United States Securities and Exchange Commission and is able to address concerns raised previously.

There is no shortage of opinions on the principles that should govern AI. At least one nonprofit, according to Algorithm Watch 173 sets of AI principles It is open to the public around the world. We cannot dispute the precious intention contained in these guidelines and promise fairness, accountability and transparency. But the challenge is to translate noble principles into daily practice. Complexity, ubiquity, opacity in so many AI use cases.

An automated decision-making system approves mortgages and consumer loans and assigns credit scores. The natural language processing system performs sentiment analysis of the company’s financial statements and creates personalized investment advice for individual investors. Insurance companies use image recognition systems to assess the cost of auto repairs.

The use of AI in these cases can affect the rights and wealth of individuals and customers, but does not pose systemic risk. Many of these concerns are covered by future legislation, including the EU rule on AI. These legislative initiatives use appropriate and unbiased data for organizations deploying AI systems, ensure their results are in line with their goals, explain how they work, and if issues arise. Take on responsible responsibilities that help you determine responsibility.

A lesser-known question concerns the use of AI-powered trading systems that can destabilize financial markets. Sarah Gadd, Head of Data and AI Solutions at Credit Suisse, says there is a risk of grazing, games or collaborative behavior if the system is all trained with the same data and the same type of algorithm.

“These need to be watched very carefully or should not be used,” she says. “To shut off power in milliseconds and place someone who can fall back, you need a suitable circuit breaker. You cannot replace human intelligence with mechanical intelligence.

However, some have pointed out that a lightning crash had occurred long before AI was used in financial markets. The question is whether AI systems make them worse. AI systems aren’t magic, they say they’re just statistical methods Ewan Kirk, founder and former CEO of Cantab Capital Partners, investment funds that use trading algorithms. “AI is good at only finding incredibly subtle effects that contain large amounts of data and are probably not systematic in nature,” he says. He adds that the reason for Kill Switch is probably not because the AI ​​program could bring down the financial system, but because of a bug.

The best way to solve Collingridge’s dilemma is to increase your knowledge of AI within your organization and in society and identify the power of persistent interest that can prevent needed changes. Several regulators have already filed proceedings, hosted AI forums, developed regulatory sandboxes to test and validate algorithms, and implemented their own machine learning systems to monitor the market. ..

However, as former US Secretary of Defense Donald Rumsfeld said, there are unknowns and unknowns, and it is argued that the precautionary principle should be developed under certain circumstances. Regulators must be prepared to ban the use of the most exotic or poorly designed AI systems until they have a better understanding of how they work in the real world.

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