As companies increase their cognitive capabilities, they will also have to address the risks that are presented by investing so heavily in new technology. Any new IT will have its own set of challenges and pitfalls, and AI is not unique in that regard, but similarly to the expanse of benefits it can provide, AI also has a very broad spectrum of risk factors that must be addressed prior to implementation.
One of the first considerations should be how to ethically implement AI strategies. A question of morality has been brought up in recent years, as the data needed to run machine learning and cognitive programs has become more and more intrusive and personal in people’s every-day lives. Companies obviously must be able to acquire that large-scale data in ways that don’t violate the privacy rights that we all have.
On a related note are the legal ramifications of that data. Data exploitation has reached never-before-seen levels in recent years, and with it have come waves of new laws and regulations governing the use of data, how it can be gathered, and the rights of all parties involved.
A secondary legal issue has arisen only recently but presents an interesting problem. Regulations in many industries require that the decisions made by AI programs be easily explainable, which is it not such an easy task. The decisions these programs make rely on calculations made on massive amounts of data which, as the calculations become more layered and complex, can be incredibly difficult or basically impossible to accurately and simply portray. But in some cases knowing why exactly the machine made a decision is important, for example in the acceptance or denial of home loan applications. Figuring out ways to make these explanations more easily accessible and less complex is one of the most important next steps in our journey with advanced learning.
Perhaps the most obvious risk connected with AI projects is that of cyber safety and the vulnerabilities associated with cognitive technology. One primary concern is how to select, store, and draw from data that may be used in the learning process. The more data that is included, the more precise the model will become, but data can be costly to both obtain and store, and there are still perceived risks about the use of public cloud storage. Companies must weigh the costs and benefits of incorporating more data into their systems against what to do with that data afterwards.
Another cyber-related risk is that of the security of the program itself. Machine learning can help in numerous ways across branches and industries, but should the data or system be tampered with in any way, the machine has basically no way of knowing that its output is the result of faulty input. Intentional deception of the program must be noticed by those in charge of it, and in some high-value cases constant monitoring is required.
A final consideration in some cases is the ability to entrust a machine with high-risk or critical scenarios. One example is with the development of self-driving cars. The slightest malfunction or misreading in a vehicle at high speeds can end in a tragedy, obviously raising questions of reliability and liability. It is one thing to trust machines with the optimization of business processes, but quite another to allow them to handle instances of potentially life or death. We have not yet reached a place in society where we trust AI in that instance, and we must improve upon our current technology immensely before we get there.