Artificial Intelligence (AI) and Machine Learning (ML) are constantly evolving fields that have transformed the way organizations use data to make data-driven decisions in the business world. However, the complexity of these fields has often led them to be reserved for data science experts and programmers. But what if I told you there's an approach to make these powerful resources accessible to everyone, regardless of their technical expertise? This is where Explainable Artificial Intelligence (XAI) comes into play.
Making Artificial Intelligence Accessible for Everyone
While AI is a broad field that encompasses a wide variety of techniques, XAI is a subset focused on making models more understandable for people who do not have experience in data science. This "no-code" approach allows a wide range of professionals, from business domain experts to data scientists, to accelerate their analytical capabilities and make the most of available data.
The importance of explainability in AI goes beyond avoiding incorrect decisions. Overall, people still do not fully trust AI, especially when it comes to replacing humans. Studies have shown that AI can be perceived as more reliable than human-written descriptions, at least when it comes to utilitarian and functional qualities. However, people trust AI as long as it works in collaboration with humans rather than replacing them.
Internal Transparency and Trust
Even with current models, there is a possibility that bias and degradation may be introduced into the results. Data often contains biases, whether intentional or not. Various factors such as age, race, gender, health history, financial situation, income, location, and more can introduce biases into the data. This bias can affect how AI models learn and generate results.
Model training is not always a clean process. Models trained on a specific dataset may work perfectly with that data, but when faced with real-world data, the results may be significantly different. This raises the challenge of ensuring that models are fair, accurate, and transparent.
In addition, AI models often need to adapt to changes in data or regulations. The introduction of new regulations can have a significant impact on how models must be adjusted and explained. XAI makes it easier for developers to update and improve models while measuring their effectiveness and complying with new regulations.
Interpretation vs. Explanation in AI
While analysts and data scientists build models, understanding the results is essential for executives and other leadership roles. This is where XAI plays a crucial role. The main difference between interpretable AI and XAI is that the latter focuses on transparency and understanding of the models, while the former focuses on interpreting the results.
XAI guides the development and deployment of AI models through a series of key questions. For example, who should the results be explained to? Why are the results needed to be explained and for what purpose? What are the different ways to explain the results? What aspects need to be explained before, during, and after building the model?
Accessible Machine Learning with Alteryx
The Alteryx Machine Learning platform is a prime example of how XAI and the democratization of AI are being achieved. It offers a "no-code" approach, meaning that you don't need to be an expert programmer to build advanced AI models. The platform includes a training mode that guides users through the model-building process intuitively.
Additionally, Alteryx Machine Learning uses world-class algorithms such as xgBoost, LightGBM, and ElasticNet, increasing confidence and understanding of the models. It also focuses on deep feature synthesis, an automated feature engineering technique that detects high-quality features and builds on relationships in the data.
The Alteryx platform makes it easier to understand why certain decisions are made and how the results are obtained. It offers a range of tools for evaluating feature importance, generating partial dependence plots, and conducting Shapley impact analysis. This allows users to better understand how models behave and how predictions are generated.
Rights: Alteryx