5 SaaS Toolkits to Build Explainable AI
Explainable AI (XAI) is a design decision to build algorithms with transparency in mind. It helps machine learning (ML) developers view their algorithms’ abilities and limitations throughout the algorithmic training process, which allows technical teams to decrease the risk of bias entering their algorithms.
Algorithm bias can have serious consequences for end users. From credit cards that offer lower limits for women to correlating recidivism with race, the consequences of not knowing how AI works are well-documented. As a result, XAI is no longer optional.
If you want to build and deploy AI in your business, everyone from senior stakeholders to customers will expect you to explain how it works. The good news? Increased demand for XAI means an increased range of tools and techniques your technical team can use to get started with building explainable AI.
These five Software-as-a-Service (SaaS) tools can aid your technical team’s explainable AI efforts (and were cited in a recent Gartner article as example techniques and methodologies that can help build XAI; full research available to Gartner clients).
When reviewing the toolkits below, look for features that address explainability and interpretability. The former will help you hold your model accountable during algorithmic training, while the latter will help you explain the model’s results to stakeholders and customers. (Products presented in alphabetical order.)
DataRobot’s software lets teams build and deploy their own AI models in-house, without the need for explicit programming. If you don’t have a data scientist on your team yet (or your business can’t afford one), DataRobot can serve as your substitute. It automates standard data science tasks and aims to help customers solve specific business problems (as one example, United Airlines used DataRobot to predict which customers are most likely to gate-check bags).
Since DataRobot automates machine learning, it also supports interpretable models. The tool includes a model blueprint, which shows you the preprocessing steps that each model uses to make its conclusions. This feature makes DataRobot an especially strong choice for teams building models that must comply with regulatory agencies.
DataRobot also has prediction explanations, which show the top variables impacting the model’s outcome for each record. This is important since algorithms assign different weights to various data points throughout the training process, which impacts its recommendations. Prediction explanations prevent possible bias by explaining how each algorithm reaches its conclusions.
Cost: DataRobot offers custom three-year contracts based on your business goals. Before choosing your software configuration, you’ll speak with a member of DataRobot’s customer-facing data science team. You can get started by contacting them here.
With one billion users on its platform, Google Cloud’s suite of platform services is hard to match in size and scope. It includes a robust suite of tools for AI and machine learning. In November 2019 Google Cloud added an explainable AI service, which evaluates algorithmic models throughout the product lifecycle.
Features such as AutoML Tables and AI Platform give users transparency to know if they should improve their models’ datasets and/or architecture. Once you deploy models on AutoML Tables or AI Platform, you’ll get real-time scores indicating how certain factors impact final results. When used in tandem with Google Cloud’s continuous feedback feature, you can compare model predictions and optimize performance.
