When Machine Learning goes off the rails? Here are some of the key considerations for executives. These algorithms may not always make ethical or accurate decisions, so it’s important to understand the risks involved.
In addition, if a machine-learning algorithm makes an incorrect recommendation, this can have serious consequences. These risks are not necessarily within the scope of a company’s control, but can be a real threat if not managed properly.
One of the major risks of machine learning is bias. It can result in investment losses, biased hiring or lending, or even car accidents. Companies should evaluate the risks of machine learning before adopting it for their business. Since machine learning algorithms are based on probability, they are prone to error. Several factors will influence the number of errors they make, including the quality of data used and the type of algorithms used.
One of the most important factors that determine whether a machine learning algorithm is correct is the environment it’s working in. The algorithm must be trained in an environment that changes over time. In this environment, car autopilots operate in an ever-changing environment. In the same way, credit scoring, trading, and pricing systems must adapt to constantly changing market regimes. Without the ability to monitor the changes in the environment, machine learning algorithms can fail to detect a pattern or make an accurate prediction.
In other cases, data feeds an algorithm with inaccurate results. This can lead to covariate shifts, which occur when the data source differs from the data fed to the algorithm. Even though concept drift is rare, data feeding an algorithm may not be representative of that population. For example, a medical device company may develop a machine-learning-based system with data from large urban hospitals. This could lead to inaccurate results because a large urban hospital may have more patients from a certain sociodemographic group, or even a higher incidence of a specific medical condition.
Another issue with the use of AI is its impact on society. A recent white paper by the European Commission urged European businesses to use AI with European values. However, such AI might not be easily exportable to other regions with different values. Rather than prioritizing business profits, these companies should focus on protecting the environment and making sure the algorithms are working properly. These factors are crucial for the future of our society.