As the capabilities of AI grow, so does the complexity of the problems it is solving. But complexity often leads to confusion, especially when the decision-making process feels opaque. Given that the success of AI is fundamentally predicated on an end-user's trust, recommendations lacking in transparency can quickly breed doubt and resistance. Explainable AI (XAI) is a way to ensure that the expansive potential of AI can be realized while continuing to build broad trust in its power.
Of course, just introducing AI into the workflow is not enough; widespread adoption only takes place when users feel they understand the "why" behind the suggestion or choice a system makes. Good XAI offers exactly that, empowering people to act by exposing the data and processes informing the AI's suggestions.
But XAI isn't just for the good of the end-users; it's also vital for ensuring responsible use of AI. Algorithms may act objectively, but that doesn't mean they're always fair; a creator's biases can result in cascading effects leading to flawed, if not dangerous, recommendations. XAI ensures that fairness, accountability and compliance are always built into the process at large.
What is XAI?
Explainable AI is exactly what it sounds like: AI where the outcomes, as well as the processes used to reach them, are easily comprehensible by humans. And "comprehension" isn't purely subjective—interpretable AI is driven by four established principles:
It is explainable: the system must be able to produce comprehensive and detailed reports about how it makes specific decisions.
It is meaningful: the explanations the system generates must be customizable for different audiences. XAI must be fluent in the languages of both the highly technical and the novice end-user.
It is accurate: reports should correctly reflect the processes the AI uses, the sources from which it draws its data, and the reasoning behind its choices.
It operates within known, set boundaries: most importantly, AI should understand its limits and work within them. This is particularly key to building, and maintaining, trust among stakeholders.
Why XAI?
XAI can lead to better processes and outcomes at every level of a business, while simultaneously helping to mitigate broader strategic risks. How? Fundamentally, XAI aims to eliminate the human biases that (consciously or otherwise) impact every choice we make. Interpretable AI ensures that fairness and transparency, and not the subconscious, are the determining factors in crucial decisions.
One way XAI achieves this is with thorough documentation. By detailing the data used to make a recommendation, as well as the weight assigned to each data point, developers and ML specialists can understand and quickly intervene when changes are needed. Removing these often subtle discriminatory elements from decision-making processes not only reduces legal and regulatory risks at large, but is also shown to generate better results in everything from loan approvals to college admissions to medical care.
All AI requires some measure of human management. In practice, this means regular performance monitoring and testing to ensure model accuracy and to prevent scope drift. XAI's ability to model and project business outcomes means decision makers have the information they need to make choices that minimize risk and maximize impact.
XAI also helps maintain compliance with requisite privacy laws while hedging against security risks. Perhaps more importantly, interpretable AI safeguards organizations from the potentially catastrophic reputational impacts that can sometimes happen with production AI. In best-case scenarios, flawed algorithms might result in faulty facial recognition systems, or the inability of smart wearables to capture results; at worst, they have led to housing and financial discrimination, inequities in prison sentencing, and preventable deaths.
Because explainable AI promotes end-user trust, it also drives broader adoption of the technology. As employees come to understand what informs the AI's reasoning, and get to witness better outcomes firsthand, their use of AI becomes more confident, more frequent, and more productive. In fact, some companies using XAI have seen improvements in model accuracy that resulted in a nearly 4x growth in profits.
Iterating toward better AI
The reason why explainability speeds up adoption of AI is simple logic: understanding how a decision is made creates more trust in the process; increased trust translates to more willingness to use AI; the more AI is used, the more effective its modeling and recommendations become; the more effective they become, the more trust, productivity and business success they create. Rinse and repeat.
XAI empowers employees with the insights they need to make the best choices about their AI, leading to improvements in processes and performance across an organization. Alongside these benefits are the cost savings and peace of mind that come from robust, verifiable compliance with dynamic security, financial, and legal regulations. Last, but certainly not least, XAI fosters a culture where transparency and ethics are at the forefront. Surfacing and correcting latent algorithmic biases not only builds better business outcomes; it builds a better world.
