By Andy Karuza, head of marketing at Teracube. Innovative product developer and marketing leader helping startups get from $0 to $50m.
As artificial intelligence and machine learning technology continue to advance the digital business landscape, you may ask yourself: Can I trust these systems to keep my brand reliable and to remain ahead of the competition?
Building trust in AI is critical to successfully adopting technology-driven strategies that push the envelope and drive efficiency in business operations. While some may be hesitant to fully integrate these technologies into workflows and put processes on autopilot, we have been using AI and ML technology for years. Google Maps, text editors and chatbots are all examples of AI technology that we use frequently—and most people don’t think twice about the accuracy or reliability of their applications.
Still, there are some genuine concerns about how much we can rely on these technologies as they become more advanced and hold more weight in successfully executing critical aspects of our businesses. So, how can companies continue to learn about these technologies to gain enough trust to adopt them on a larger scale?
Evaluating AI Performance And Processes
Trusting AI-driven technology for business starts with trusting its performance and processes. You may already know that a stable and trustworthy AI executes tasks using robust and up-to-date datasets compiled specifically for the industry or market in which it operates. The overarching concern then is how well and how quickly an AI can model data to make predictions appropriately.
The foundation of trust in AI lies in high-quality data. Without timely, tangible and accurate data, you can expect AI data modeling to fall short of your needs and expectations. Businesses can ensure high-quality datasets by vetting and minimizing the number of data sources used. Ultimately, data must be compatible with an AI’s systems and processes to remain accurate and viable.
Another way you can ensure dependable AI performance is by consistently cleaning your data. In basic terms, data cleaning remediates flawed or corrupt data within a dataset—which is the primary cause of inaccurate data modeling and ineffective predictions. A common issue with datasets occurs when data is compiled from numerous sources, enabling duplication and mislabeling errors within a system. When an AI struggles to recognize incorrect data within a dataset, it causes modeling inefficiencies and inaccurate outlooks.
While there is no fixed rule for how to best clean your data, you can enhance data cleaning processes by integrating a repeatable framework into your workflows. This could be anything from scheduling weekly data checks to having monthly meetings with data management teams to ensure your systems are up-to-date and using the most effective solutions. These processes allow you to, at the very least, keep your data cleaning process consistent.
Considering The Ethics Of AI Technology
One of the biggest concerns for businesses using AI technology to execute tasks and run processes is its role in ethical operations. AI ethics looks at automated technology’s overall transparency, which is void of human thought and decision-making capabilities.
The level of operational transparency required for an industry varies by application, yet there are some underlying principles that every market can follow. In general, AI transparency outlines how a model functions within a business’s internal operations—which can change significantly depending on the industry. The algorithm an AI uses should be clearly identified and understood by end-users and the general public.
By clearly laying out AI processes to end-users, you eliminate the risk of misunderstanding and allow those involved a more comprehensive view of how the technology operates and how decisions are made.
Preserving Privacy And Data Rights
As businesses grant AI and ML technology more responsibility in day-to-day operations, user privacy and data rights become a more apparent risk. This leaves many wondering how companies plan to address the concern. While data privacy has historically been a barrier to adopting automated technology on a broader scale, new advancements in AI technology have begun to solve some of the most significant obstacles.
Privacy-enhancing technology now supports data privacy and protection, allowing companies to collect data from privacy-compliant sources. As ethical data concerns continue to gain momentum, fair-trade data should become the norm across business landscapes.
While the concerns over AI are undoubtedly becoming more comprehendible, businesses using AI technology must continue to act and operate in ways that foster trust for everyone. By doing so, we allow new opportunities to enhance business operations and open the door to a future that benefits everyone, including the standard end-user.