Discover how Howso revolutionizes data insights by delivering understandable AI and leveraging causal AI principles to unlock the full potential of your data and AI models.

In today’s data-driven landscape, extracting meaningful and actionable data insights is crucial for businesses aiming to stay ahead of the competition. Traditional methods often require building numerous models, partitioning data, and grappling with imperfect data analysis techniques. Enter Howso, a groundbreaking platform that delivers superior insights through understandable AI and causal AI, allowing you to fully leverage your data and AI models.

Unparalleled Insight into Data’s Utility with Understandable AI

When determining if your data is adequate to accurately predict the target feature of interest, traditional AI/ML methods involve holding out test data and building models on subsets. This approach often leads to signal loss due to data partitioning and provides limited data insights. With Howso’s understandable AI, you can assess prediction statistics of any feature without holding out any data, ensuring no signal is lost. Howso automatically calculates a wide range of statistics for predicting every feature in your dataset, delivering out of the box insight into your data’s overall predictive utility for any use case.

Transparent Prediction Explanations with Understandable AI

Explaining why a prediction was made is crucial for building trust in AI systems. Traditional methods often fall short in providing full transparency. Howso offers full transparency by showing the data that influenced the predictions, similar cases to provide confidence in the prediction,  and any counterfactuals involved. By highlighting uncertainty and the probability of various data elements, Howso’s understandable AI ensures you have comprehensive understanding into exactly why every prediction was made.

True Feature Importance and Causal Relationships

Determining feature importance traditionally relies on models like SHAP to assess how features influence predictions. While useful, these methods often overlook deeper causal relationships and can require building thousands of models. Howso transcends model-level insights by determining feature importance at the data level using causal AI principles. This allows you to uncover asymmetries in predictions and discover causal relationships within your data. By understanding how features contribute to prediction uncertainty, you gain richer understanding of your data’s underlying dynamics.

Superior Anomaly Detection with Causal AI

Detecting anomalies typically involves building additional models and calibrating them—a complex and time-consuming process. Howso provides superior anomaly detection by identifying which data points are suprising given the other data, i.e., anomalous, and why. It even shows counterfactuals—what changes are needed for an anomaly to no longer be considered anomalous. Operating directly on the data enables Howso to detect patterns of unusual data density, indicating sophisticated anomalies that might have been missed.

Effortless Data Updates and Edits

Adding new data, editing existing data, or removing data traditionally requires rebuilding models and revalidating results—a time-consuming endeavor. Howso streamlines this process, allowing you to directly add, edit, or remove data without the need for rebuilding models. This flexibility ensures your models remain up-to-date, reflecting the latest data for optimal data insights.

Seamless Analysis Across Data Segments

Understanding how predictions change across different parts of your data is essential for making informed decisions. Traditional methods require partitioning the data, building multiple models, and validating each against test subsets—a time-consuming and labor-intensive process. Howso simplifies this by allowing you to assess prediction statistics across various data conditions without any data partitioning or model building. By specifying how to condition the data, Howso leverages causal AI principles to provide a deeper understanding of how different segments interact, enhancing insight into your data. For example, you will automatically understand the impacts of adding or dropping features on your data’s utility, useful for engineering the right dataset for your use case.

Precision Handling of Missing Data

Handling missing or null values is a common challenge that can impede data insights. Traditional approaches often involve imputation methods requiring extensive feature engineering or one-hot encoding, which can introduce trade-offs between data quality and complexity. Howso’s approach lets you load data directly with null values, using understandable AI to discern when they hold semantic meaning versus   merely adding noise. You can scaffold data through imputation while tracking changes, enabling you to inspect or reimpute as new data comes in, thereby enhancing your data insights.

Granular Uncertainty Measurement

Estimating uncertainty around predictions often requires building separate models or calibrating existing ones, which can be cumbersome. Howso’s information theory-based  approach offers direct measurement of uncertainty around every data point and prediction. This method provides precise uncertainty estimations directly from your data, enhancing where you should and should not be confident in predictions made with your data.

Advanced Data Similarity and Confusion Analysis

Understanding the similarity or likelihood of confusion between data points is vital for various applications. Traditional methods involve training models or using low-accuracy k-nearest neighbor implementations. Howso allows you to query the surprisal of confusion between data points, providing advanced analysis of data characteristics, including anomalousness and density. This capability, powered by causal AI principles, offers refined data insights into the similarities and differences within your dataset.

Comprehensive Support for Learning Methods

Engaging in supervised, semi-supervised, or reinforcement learning usually demands managing numerous tools and models. Howso simplifies this by offering comprehensive support for a wide range of learning methods, including anomaly detection and bias mitigation. Powered by understandable AI and causal AI principles, Howso allows you to execute sophisticated tasks seamlessly, enhancing your data insights without the complexity of juggling multiple tools.

Conclusion

Howso revolutionizes the way you gain data insights by delivering understandable AI and leveraging causal AI principles. By eliminating the need for excessive model building and data partitioning, Howso empowers you to focus on extracting actionable insights. Whether you’re handling predictions, anomalies, or data updates, Howso offers a streamlined, cutting-edge solution that unlocks the full potential of your data and AI models.

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Try it out now with Howso Playground here.