Most companies can mitigate the risks of applying AI and advanced analytics, but they need three tools to do it, which we will discuss in this article. It’s been proven that artificial intelligence is a double-edged sword, neither side is well-understood, and this can prove to be risky!
There are plenty of positives to artificial intelligence, and the main one is that our digital technology is improving the way that we live in ways which we could only have dreamed.
We can simplify the way that we shop, enhance the experiences that we have with our healthcare, and we can view the benefits to businesses, especially. Nearly 80% of executives that are using artificial intelligence see more value from it than they thought. Not every company is – as yet – embracing artificial intelligence in its entirety. However, this doesn’t mean that it’s not on the cards and as well as achieving better progress, a business can achieve the holy grail of “intelligence,” which makes the potential of artificial intelligence for a business enormous. It has been suggested that artificial intelligence can deliver a global economic output of $13 trillion in the next ten years alone; which is a tremendous value! However, as artificial intelligence is currently generating massive consumer benefits and value to businesses, it’s also got some serious issues and consequences going on.
The effects of artificial intelligence in business environments aren’t always positive, and it’s essential to be able to prevent or mitigate these risks as much as possible.
The effects of AI apply to all advanced analytics, and the most visible ones include:
- Manipulation of political systems
- Privacy violations
These issues are more than enough for people to take pause and be cautious about the future. There are even more consequences out there that are not yet experienced, known or wanted, and we need to be mindful of these. The problem is that because we don’t know the other risks, executives are overlooking the possible perils or overestimate their own abilities. This can be devastating to a business in the long term, and it’s why it’s essential to learn the risks of artificial intelligence early on.
The Risks of Artificial Intelligence
There are plenty of risks to consider that come with the introduction of artificial intelligence to your business, and these include the following:
Data ingesting, sorting, and using has become challenging over time as the amount of data that is churned by sources like mobile devices and social media increase. The result is a natural pit to fall into, such as using or revealing data that is sensitive, which was initially hidden. In healthcare, for example, a patient’s name may have been moved from one section of a medical record that is used by an artificially intelligent system. It could then be present in the doctor’s notes section of the record, and it’s essential to consider this risk for your business. With the rules of GDPR and CCPA, you have to manage your reputation risk.
We rely heavily on the technology available to us for convenience and ease. The process issues across the whole system can affect the performance of artificially intelligent systems. There have been reports of financial institutions that have run into trouble after its compliance software failed to spot issues in trading because of the data feeds failing to include customer trades.
Security is a HUGE factor when it comes to the use of artificial intelligence, and that’s because of the exploitative nature of fraudsters and nonsensitive marketing, health, and financial data. If security precautions are not sufficient, then the fraudsters can create false identities with ease. They can then target companies that were unwilling and unwitting accomplices in their crime, which leads to consumer backlash, penalties, and repercussions by regulatory bodies.
Artificially intelligent models can be a source of problems when they are delivering biased results. This can happen if there is an under-represented population in the data that was used to train the model in the first place. AI models could fail or glitch, discriminating unintentionally against the protected classes and other groups, and they can do this by weaving together income data and zip code data. However, it’s harder to spot it when AI systems are lurking in software-as-a-service offerings. When there are new programs introduced by vendors, they’re also introducing the models that are glitching and exposing hidden vulnerabilities in the systems.
Issues In Interaction
People and machines interface differently, and that makes it a key risk area. There are challenges in automated transportation, infrastructure systems, and manufacturing, and accidents are possible if this is not acknowledged. If systems aren’t recognizing when they could be overruled, then injuries are a possibility and can be avoided if caught early. Of course, it’s not all the fault of machinery; human error can and does happen. However, lapses in data management can compromise the security and compliance ahead. Personnel on the front line can also contribute to this unintentionally, and when a sales force is more adept at selling, it inadvertently trains AI-driven sales tools to exclude specific customers. Rigorous safeguarding is necessary to prevent any of this happening.
Three Risk Management Tools
There are three tools for risk management to prevent artificially intelligent systems from going wrong, and these include:
Organizations must use a structured identification approach. This can pinpoint the risks and save them the time of starting from scratch. Risk identification is an art and can be directly deployed in artificial intelligence.
If you sharpen your thinking about the risks, you can institute robust controls enterprise-wide. The application of these controls has to guide the development and use of artificially intelligent systems. This can help you to put strong policies into place as well as worker contingency planning.
You need to reinforce controls specific to AI, depending on the nature of the risk. As much as enterprise-wide controls are essential, they are not always sufficient to counteract all the threats. Other levels of nuance are needed, and the controls following will depend on the complexity of the algorithms and their data needs.