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As of 2021, 91.5% of companies report continued investment in artificial intelligence (AI). As organizations consider their next big AI solution, there are two key components to keep in mind during this quest: a strong user interface (UI) and unbiased results.
Poor UI design is a major reason why certain technology doesn’t achieve high adoption rates within organizations. If the user interface of an AI solution is easy to use, has strong performance, and has compelling branding and design features, business impact and usage will skyrocket.
But of course it does not stop with appearance and practicality. Ensuring organizations implement bias-free AI technology is key to continued success. AI algorithms are shaped by the data used to train them. That data, and the training process itself, may reflect biased human decisions or historical and social inequalities, 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 market for AI technology is booming
As AI continues to evolve, it will impact not only how businesses operate, but also how we function as a society. In fact, the use of AI is so widespread that the market size is expected to grow from $86.9 billion in 2022 to $407 billion in 2027.
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Whether it’s using AI in intelligent document processing (IDP), fraud detection software, self-driving cars or chatbots, this emergence has complicated the definition of AI. To keep it simple, AI tries to mimic the human approach to common problems. As time goes on, AI will continue to get smarter as we continue to learn and use its capabilities for maximum potential and problem solving.
Today, we have reached a critical turning point in AI technological advancement and are able to tackle everyday tasks and overcome challenges in new, efficient and innovative ways. That said, AI has also become a saturated market: those seeking to solve day-to-day business problems now find it difficult to pinpoint leading solutions. Many companies are looking for tips on which fundamental elements are most important when evaluating AI technologies, and the UI design and unbiased results should stand out.
Prioritize a strong user interface
Deep learning is a form of machine learning (ML) based on artificial neural networks. These are mathematical structures loosely inspired by the shape and function of the brain, and they can learn, for example, by learning in a way similar to the way humans learn.
Deep learning has exploded in recent years, constantly pushing the boundaries of what is possible with AI. It is by far the fastest evolving area of AI, and at this point, non-deep learning areas of AI could be labeled as niche.
To further explain, when a human corrects an AI error, the AI is not allowed to repeat the same error again. Unfortunately, if usage is limited, AI can no longer learn by doing, for example, and will eventually lead to reduced results and poor data quality. In fact, poor data quality has cost organizations more than $12 million annually and can significantly harm business operations. Without an easy-to-use UI, employees will not use the AI solution, and those who do will use it less than recommended or use it incorrectly. All this devalues the AI investment because the models don’t learn or get better.
For example, AI is being programmed into cars and the user experience is key to adoption and success. Lane assist technology has particular safety benefits, but the experience can be very startling and unpleasant for drivers as they drift into a different lane. Depending on the car model, the steering wheel may move automatically, sound alarms or flash on the dashboard.
If lane assist technology is too sensitive or erratic, it can cause major disagreement among drivers, hurting adoption rates. Ultimately, technology has stopped accumulating the knowledge needed to improve its capabilities. This applies to all deep learning AI technology. With many still not understanding the full scope of AI and its benefits, a powerful and easy-to-use user interface should be paramount to ensure a continued and successful investment.
Remove AI bias from the equation
Bias is everywhere, and AI is no exception. AI bias is the underlying bias in data used to create AI algorithms, and it’s usually — usually subconsciously — built into technology from the start. This can be done by training models on data influenced by repeated human decisions and behaviors, or on data that reflect second-order effects of societal or historical inequalities. This can lead to discrimination and other social consequences.
User-generated data can also create a feedback loop that leads to bias, and bias can be introduced into data by 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 socially unacceptable or illegal. For example, Amazon recently discovered that the 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 applicants were men, it was trained to favor men. While this was a seemingly innocent mistake, its impact and effect on women’s career advancement was enormous.
Furthermore, one of the biggest problems with biased AI technology is that it can leverage human and societal biases Scale, which consistently produce inaccurate results and damage the trust between the end user and the supplier. Making sure each potential vendor prioritizes and consistently researches AI bias is key. Whether it be racial profiling, gender bias, inequality in hiring and/or age discrimination, bias should be kept in mind by all companies when in the market for new AI-powered technologies.
Combining a strong user interface with bias-free AI for maximum success
When developing a product, bias can play a critical role in the success of a user interface. Furthermore, there can be AI bias improved with a strong user interface.
For example, a graphic designer may want to include photos they find attractive and thought-provoking on the landing page of a software platform. That is a completely biased opinion and not based on market research or customer feedback. These photos can affect the user experience and by removing 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-thinking technology partner, it is important to inquire about these elements – and their evolutions – in advance.
While it’s clear that AI technology adds a lot of value to organizations, there’s still a lot to learn, so it’s critical to have a checklist of the key components that need to be implemented and remain front and center during the technology trajectory.
In other words, finding a solution that not only has a strong user interface, but also works proactively to remove biases is the key to a sustainable, highly adopted, reliable and scalable solution that will take businesses to the next level.
Petr Baudis is CTO and lead AI architect at Rossum.
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