Built Transparency & Trust in your AI Models With Real Time Risk Monitoring


Responsible AI

Is your machine learning model fair enough? What if the lending AI discriminates on gender and race? What if the accuracy of medical AI depends on a person’s annual income or on the GDP of the country where it is used? Today’s AI has the potential to cause such problems.

To prevent harmful outcomes, we research and identify best fitted libraries to develop Responsible AI metrics, a governance framework that promotes the practice of building generative AI that is reliable, transparent, accountable and ethical. We help our clients implement a safe and non-discriminatory model that consist of these factors:

  • Bias

    How do you prevent your model from making predictions that do not favor or discriminate against certain individuals or groups. Bias can impact machine learning systems at pretty much every stage. Bias is a preference or prejudice against a particular group, individual, or feature and comes in many forms.

  • Drift

    Generative AI models are trained on historical data to learn a static mapping between their input and output variables and operate within spec. Drift occurs when the models are deployed on continuously streamed data, whose nature is likely to change over time (data or concept drift), model performance may suddenly and substantially degrade, forcing continuously update the models to reflect the new data distribution.

  • Explainability

    Being able to understand why the model generated a certain result and explain why certain prediction was made. It is about interrogating a model, gathering information on why a particular prediction (or series of predictions) was made, and understand the model and instance view. Instance View tells you for a particular prediction, what factors contributed.

  • Data Privacy

    Sometimes the training and input data can be quite sensitive and it is essential to consider the potential privacy implications in using sensitive data. This includes not only respecting the legal and regulatory requirements, but also considering social norms and typical individual expectations.

  • Carbon Footprint

    It estimates the amount of carbon dioxide (CO2) produced by the cloud or personal computing resources used to execute the AI code.

Exploratory Data Analysis (EDA)

Exploratory Data Analysis is the critical process of running initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions. EDA is an analysis approach that identifies general patterns in the data. These patterns include outliers and features of the data that might be unexpected.

We have a deep bench of “data hunters” with extensive experience in time series data that help our clients unlock the hidden patterns.


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New York

175 Varick Street, 8th floor

New York, NY 10014

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