Explainable artificial intelligence (XAI) is helping companies and developers analyse algorithms transparently, assessing exactly how they work out solutions and improve them. In the years ahead, XAI will fundamentally change widespread AI adoption.
What Is XAI?
Explainable AI came about in response to a growing need for comprehensive analysis of algorithms’ logic, particularly why they are flawed or broken. Users saw issues with AI, such as biased responses, but weren’t sure how to resolve them. XAI enables close inspection of AIs’ inner workings, opening the previously locked doors of algorithmic logic to allow for a behind-the-scenes look.
XAI generally takes one of two approaches: black-box analysis or interpretable models. Black-box analysis is the traditional method since it simply opens up an algorithm’s preexisting box and examines the data inside. Interpretable models are more like XAI that is decipherable by design.
These models are intended to be analysed, like a computer in a glass case. They can be highly complex to create, but demand for user-friendly XAI is encouraging developers to continue researching and innovating interpretable technology.
Applications allow XAI to shape overall AI adoption through increased transparency and accountability.
AI has become more popular over recent years for recruiting purposes, but that’s beginning to wane. This is because of the rising awareness of data bias within AI. For example, Amazon used to use AI as part of its recruiting process — though job applicants weren’t aware. It ended the algorithm’s use in 2018 because it demonstrated an inherent bias against female applicants.
Data bias is theorised to occur due to subtle biases within the AI’s training data. In Amazon’s case, something implied male applicants were more qualified or somehow preferable to any candidate with the word “woman” or “women” in their resume. Issues like this are notoriously difficult to detect until the AI has already been in use for a while.
XAI would enable developers to pick up on data bias flaws much sooner. In the case of recruiting, they could closely analyse how the AI marked someone as “qualified” during the testing phase. AI is useful for speeding up the recruiting process, but it needs XAI to maintain trust and transparency about how applicants are fairly assessed.
2. Consumer Trust
Only 25% of Americans trust AI, as of 2018 polls. Clearly, AI still has to prove itself in the U.S. if it is to take off. Distrust in AI could be due to various factors, such as lack of understanding or even conspiracy theories. Whatever the case may be, XAI provides an ideal solution, utilising straightforward technology to reveal how AI truly functions. Publishing and discussing developments in XAI will further help to strengthen public trust.
XAI isn’t just useful for improving accountability. Access to all of an AI’s internal processes allows for much swifter innovation. XAI enables developers to see exactly how an algorithm is operating so they can quickly spot things that could be optimised or new opportunities for application or development.
For example, maybe an algorithm is doing the same check on input multiple times, leading to inefficiencies. Identifying new possibilities for utilising AI through XAI will foster increased adoption, such as new business applications or UX features.
There is no doubt that AI is extremely complex, but developing a robust understanding of it is crucial to taking full advantage of its capabilities. XAI can help developers, users and consumers better understand how algorithms interpret data and process solutions.
While the technology isn’t perfect, it can still be a highly valuable tool for improving AI and fostering trust. The better people can make sense of AI and its capabilities and limitations, the more they will trust it. Everyone has the peace of mind afforded by XAI’s inherent accountability.
Greater accessibility of AI data could help improve the security of algorithms and the people who use them. AI training data can also be tainted with strategically programmed weak spots that allow hackers to get in through a back door. It can be just as hard to detect as biased data, so improving developers' and users' tools for spotting it would be groundbreaking. Watching how an AI processes information could help developers catch problems earlier.
Safer, Smarter AI
XAI could be the key to bringing artificial intelligence to the mainstream and restoring public confidence in its fairness and security. Engineers and data scientists can utilise XAI to increase trust while also discovering new ways to improve AI. Greater transparency and accountability could take this technology to new levels.
About the author
Devin Partida is a machine learning and AI writer. Her work has been featured on Techopedia, KDnuggets, Business2Community and Entrepreneur. To read more from Devin, please visit her personal website at DevinPartida.com.