The processes and systems in place with AI gone rogue have several financial and reputational risks, and mending these is a key challenge yet an important aspect of any business.
As financial innovation and financially engineered products took the world by storm, automation of tasks replaced manual work. Moreover, as the need for speed and large volumes of data takes over decision-making, small and medium businesses and even large conglomerates felt a need to implement AI systems. There will be a gradual shift from human intervention to AI-led processes and systems, but the probability of it being successful is something businesses have to wait and watch.
It is imperative to note that products and systems with AI gone rogue may become very expensive and bring a bad reputation to the business.
Implications of AI Gone Rogue
The ultimate objective of any AI-based system and process is that it should have a definite power to predict and churn output while maintaining a high level of accuracy and reliability. A sudden drop in predictability or accuracy of the desired output over a well-defined and consistent time serves as an early warning signal or a red flag of AI gone rogue. Many industry experts, analysts, and system engineers opine that incorrection assumptions, faulty data feeds, and prevalent bugs in the models are some prominent factors of AI gone rogue. A corrupt and questionable AI system makes a business vulnerably exposed to operational, reputational, and financial risks.
Some of the most problematic factors a business faces are that a faulty AI system leads to a loss in investor confidence and eventually a drop in sales and profit margin to a considerable extent.
How to Counter AI Gone Rogue
With the ever-increasing rise in AI-driven and powered products, businesses need to quantify, counter and manage these risks before it goes rogue. In the world of complexities, simple strategies to manage and mitigate risks always comes handy. The model developer first needs to understand the limitations of the model. Not every type of AI model can be used in every field. Some AI-based models have low latency while others have high latency and the applicability of such models depends upon the nature of the business. Each model needs to be reviewed periodically, which should be updated and free of any errors or software bugs.
All businesses need to have an effective monitoring strategy that helps detect errors, issues or bugs on a pre-event basis, reducing reputational loss and curtailing the financial loss of an organization.
