Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
As of 2021, 91.5% of businesses report an ongoing investment in artificial intelligence (AI). As organizations consider their next big AI solution, there are two key components that must be kept top of mind throughout this search: A strong user interface (UI) and bias-free results.
Poor UI design is a leading reason why certain technology doesn’t gain high adoption rates within organizations. If the UI of an AI solution is easy to use, delivers strong performance, and has engaging branding and design features, its business impact and usage will skyrocket.
But, of course, it doesn’t stop with just looks and usability. Ensuring that organizations implement bias-free AI technology is key for ongoing success. AI algorithms are shaped by the data used to train them. That data, and the training process itself, can reflect biased human decisions or historical and social inequities — even if sensitive variables are removed. To maintain and build trust with new AI capabilities, companies must always value and enforce usability and accuracy while continuing to raise their expectations of such technology.
The AI technology market take off
As AI continues to evolve, it impacts not only how businesses operate, but how we function as a society. In fact, AI usage is so prevalent that the market size is expected to grow from $86.9 billion in 2022 to $407 billion by 2027.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Whether it be the use of AI in intelligent document processing (IDP), fraud detection software, self-driving cars or chatbots, this boom has left the definition of AI convoluted. To keep it simple, AI aims to mimic the human approach to common problems. As time goes on, AI will continue to become smarter as we continue to learn and utilize its capabilities for maximum potential and problem-solving.
Today, we have reached a pivotal turning point in AI technological advancements and are able to tackle mundane tasks and overcome challenges in new, efficient, and innovative ways. That said, AI has also become a saturated market: Those looking to solve everyday business problems are now finding it difficult to pinpoint leading solutions. Many businesses are seeking tips around what foundational elements are most important when evaluating AI technologies, and its UI design and bias-free results must stand out.
Prioritizing a strong user interface
Deep learning is a type of machine learning (ML) based on artificial neural networks. These are mathematical structures loosely inspired by the form and function of the brain, and they are able to learn by example in a way that is similar to the way humans learn.
Deep learning has evolved explosively over the past years and is constantly pushing the envelope of what’s possible with AI. It’s by far the fastest evolving area of AI, and at this point, non-deep learning areas of AI could be labeled as niche.
To explain further, whenever a human corrects an AI mistake, the AI should not repeat the same mistake again. Unfortunately, if usage is limited, AI can no longer learn by example and will ultimately provide diminished results and poor data quality. In fact, poor data quality has cost organizations more than $12 million on a yearly basis and can significantly hurt business operations. Without a friendly UI, employees won’t use the AI solution, and those that do will use it less often than recommended or won’t use it properly. All of this devalues the AI investment because the models are not learning or getting better.
For example, AI is being programmed into cars, and the user experience is key to its adoption and success. In particular, lane assist technology holds safety benefits, but the experience can be very startling and off-putting for drivers if they drift into another lane. Depending on the car model, the wheel may automatically move, alarms may go off or flashing may occur on the dashboard.
If lane assist technology is overly sensitive or erratic, this can cause great strife for drivers, hurting adoption rates. Ultimately, the technology has stopped gaining the knowledge it needs to improve its capabilities. This goes for all deep learning AI technology. With many still not understanding the full scope of AI and its benefits, a powerful and easy-to-use UI must be at the forefront to ensure an ongoing and successful investment.
Removing AI bias from the equation
Bias is everywhere, and AI is no exception. AI bias is the underlying prejudice in data that’s used to create AI algorithms, and it is typically — usually unconsciously — built into technology from inception. This can happen by models being trained on data that is influenced by repeated human decisions and behaviors, or on data that reflects second-order effects of societal or historical inequities. This can result in discrimination and other social consequences.
Data generated by users can also create a feedback loop that leads to bias, and bias can be introduced into data through how it is collected or selected for use. Depending on the solution, AI bias can also lead to algorithms full of statistical correlations that are societally unacceptable or illegal. For example, Amazon recently discovered that its algorithm used for hiring employees was biased against women. The algorithm was based on the number of resumes submitted over the past ten years, and since most of the applicants were men, it was trained to favor men. While this may have been a seemingly harmless oversight, its impact and effect on the advancement of women’s careers was vast.
Further, one of the largest issues with biased AI technology is that it can deploy human and societal biases at scale, continuously providing inaccurate results and hurting trust between the end-user and vendor. Ensuring that any potential vendor prioritizes and consistently conducts research on AI bias is the key. Whether it is racial profiling, gender prejudice, recruiting inequity and/or age discrimination, bias is something all companies need to keep top of mind when on the market for new AI-powered technologies.
Combining a strong UI with bias-free AI for maximum success
When developing a product, bias can play a pivotal role in the success of a UI. Further, AI bias can be improved with a strong UI.
For example, a graphic designer might want to include photos they that find engaging and thought-provoking on the landing page of a software platform. That’s a completely biased opinion and not based on any market research or feedback from customers. These photos can impact the user experience, and by eliminating photos selected based on personal preference, bias can be avoided. These two components of AI technology can quickly become intertwined, and if organizations are looking for a forward-looking technology partner, it is important to inquire about these elements — and their evolutions — from the forefront.
While it’s clear that AI technology brings a plethora of value to organizations, there is still much to learn, so having a checklist of the important components to be implemented and remain the focus throughout the technology’s journey is crucial.
In other words, finding a solution that not only has a strong UI but proactively works to cut out bias is the key to a long-lasting, highly-adopted, trusted, and scalable solution that will take businesses to the next level.
Petr Baudis is CTO and chief AI architect at Rossum.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!