By Andy Karuza, Head of Marketing at Terracube† Innovative product developer and marketing leader helping startups get from $0 to $50 million.
As artificial intelligence and machine learning technology continue to improve the digital business landscape, ask yourself: Can I trust these systems to keep my brand reliable and ahead of the competition?
Building trust in AI is critical to successfully adopting technology-driven strategies that push boundaries and drive efficiencies in business operations. While some may be hesitant to fully integrate these technologies into workflows and put processes on autopilot, we’ve been using AI and ML technology for years. Google Maps, text editors and chatbots are all examples of AI technology that we use often – and most people don’t give much thought to the accuracy or reliability of their applications.
Still, there are some genuine concerns about the extent to which we can rely on these technologies as they become more advanced and carry more weight in successfully running critical aspects of our businesses. So, how can companies continue to learn about these technologies to gain enough confidence to adopt them on a larger scale?
Evaluate AI performance and processes
Relying on AI-driven technology for business starts with trusting performance and processes. You may already know that a stable and reliable AI performs tasks using robust and up-to-date datasets curated 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 accurate predictions.
The foundation of trust in AI lies in: high-quality data† Without timely, tangible and accurate data, you can expect that AI data modeling will not meet your needs and expectations. Companies can guarantee high quality data sets by controlling 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 reliable AI performance is by consistently clean your data† In basic terms, data cleaning restores flawed or corrupt data within a data set, which is the primary cause of inaccurate data modeling and ineffective predictions. A common problem with datasets occurs when data is compiled from different sources, allowing duplication and mislabeling within a system. When an AI struggles to spot incorrect data in a dataset, it causes modeling inefficiencies and inaccurate forecasting.
While there is no hard and fast rule for how best to clean your data, you can improve data cleanup processes by incorporating a repeatable framework into your workflows. This can be anything from scheduling weekly data checks to monthly meetings with data management teams to ensure your systems are up to date and using the most effective solutions. At the very least, these processes allow you to keep your data cleaning process consistent.
Considering the ethics of AI technology
One of the biggest concerns for companies using AI technology to perform tasks and run processes is its role in ethical operations. AI ethics looks at the overall transparency of automated technology, which is stripped of human thinking and decision-making capabilities.
The level of operational transparency required by an industry varies by application, but there are some underlying principles that any market can follow. In general, AI transparency outlines how a model functions within a company’s internal operations, which can change significantly depending on the industry. The algorithm an AI uses must be clearly identified and understood by end users and the general public.
By dividing AI processes clearly for end users, you eliminate the risk of misunderstanding and give those involved a more comprehensive view of how the technology works and how decisions are made.
Preservation of privacy and data rights
As companies give AI and ML technology more responsibility in day-to-day operations, user privacy and data rights become a more obvious risk. This leaves many wondering how companies plan to tackle healthcare. While data privacy has historically been a barrier to the wider adoption of automated technology, new advancements in AI technology have begun to resolve some of the major hurdles.
Privacy-enhancing technology now supports data privacy and protection, allowing businesses to collect data from privacy-compliant sources. As concerns about ethical data continue to grow, fair trade data should become the norm in all business landscapes.
While concerns about AI are undoubtedly becoming more understandable, companies using AI technology must continue to act and operate in a way that fosters trust among everyone. By doing this, we open up new opportunities to improve business operations and open the door to a future that benefits everyone, including the standard end user.