When we make a decision, there’s usually a reason behind it. The same goes for machine learning models. Though the process is less emotional, they’re still connecting data in order to come to a conclusion.
Most companies that rely on these predictions don’t bother thinking about the “why” behind them, but this transparency is actually crucial. Why the “why?” It’s not just something that’s good to have—it’s necessary information.
Why the “Why?”
Emerging legislation is pointing toward greater transparency in AI-enabled applications. In some cases, it is already required by law—or soon will be. It may be required to document in detail why, for example, a benefits claim was denied —including all the predictive factors that went into the decision. But the “why” goes beyond legal requirements. Our predictive technology clusters based on actions, outcomes, or other meaningful business objectives, creating segments that have intent and action, not just descriptive statistics. The result? Transparent, actionable intelligence.
Transparency Can be Transformative
xAI Workbench enables teams to develop a wide variety of machine learning models, at massive scale, each with unparalleled explainability. That means the models don’t just provide you with predictions but give you every single reason behind each one. Our suite of modeling engines provide solutions that deliver any combination of similarity search, classification, clustering, and recommendation. This kind of information isn’t just for data scientists, but for anyone looking to enhance processes and outcomes.
Dynamic predictive segmentation (DPS) is the future. Marketers require analytics-driven tools and it’s no surprise that dynamic predictive segmentation is the fastest growing segmentation method. Perceived benefits of adopting DPS include discovery of new opportunities, ability to react more quickly to competitors, increased customer engagement, and improved customer experience.
– Capture The Customer Moment With Dynamic Predictive Segmentation, January 2018. A commissioned study conducted by Forrester Consulting on behalf of simMachines, an InRule Technology company
Benefits of Explainable AI
- Improve transparency to comply with emergent legislation
- “No code” ease of use brings machine learning to anyone
- Extract actionable insights from your models
- Measure how key factors in your data change over time
- Rapidly identify data engineering errors
- Confirm robustness against adversarial examples
See More and Get More
Maybe you’re looking to improve your audience segmentation, understand why past models worked or failed, or you’re trying to earn consumer trust through transparency. xAI Workbench can help with all of that—and more! We’ve already helped global enterprises realize significant performance gains, efficiencies, insights and customer experiences.
- Audience segmentation
- Fraud prevention
- Identity resolution
- Customer predictions
- Insights and analytics
- Adaptive authentication
- Anomaly, pattern, and trend detection
- Every prediction comes with feature weights and nearest neighbor objects with keys tied back to the training object database.
- Agglomerative and K-Means clustering support dynamic predictive clustering for unparalleled segmentation.
- No need to fill in missing values, normalize data or one-hot encode categoricals. Data sets are stored in folders and can span files, so all files in a folder can be treated as a single data set.
- Automatic stemming and conversion to bag of words for language string input enables data input for use cases involving ingesting product names, social media comments, web pages, etc.
- See and adjust data quickly for data input changes with automated model debugging by revealing the changes associated with model performance.
- Model analytics reporting are provided, as well as real-time prediction monitoring and notification of statistical issues.