Responsible AI – KaribuTechAI

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The Responsible AI Impact Assessment is a structured framework designed to comprehensively evaluate the potential impact of AI systems on individuals, groups, and society at large. Its core mission is to proactively identify and mitigate any adverse consequences that may arise from implementing AI solutions. This evaluation covers ethical, social, and environmental considerations spanning the entire machine learning lifecycle, from design and development stages to deployment and usage.

What we do

The Responsible AI Impact Assessment

Responsible AI is a set of practices used to ensure that artificial intelligence is developed and applied by a company in a secure manner and from every ethical and legal standpoint.

The goal of Responsible AI is to use artificial intelligence responsibly by examining the possible impacts of AI systems, enhancing transparency, and reducing bias in the development and use of artificial intelligence.

By applying its basic principles, it can be ensured that these systems collect, store, and use data in a way that complies with data privacy laws and prioritizes security against any potential threats in cyberspace.

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This evaluation covers ethical, social, and environmental considerations spanning the entire machine learning lifecycle, from design and development stages to deployment and usage.

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What we do

The Responsible AI Risk Management Flow

Screenshot 2024-03-03 at 10-13-04 Getting to know—and manage—your biggest AI risks

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The Responsible AI Governance

Responsible AI is a way of developing and deploying AI in an ethical and legal way.

Governments around the world are keeping a close eye on AI, readying themselves to regulate once the technology has matured.

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What we do

The Responsible AI on Generative Apps

In order to achieve these capabilities, the dashboard integrates together ideas and technologies from several open-source toolkits in the areas of

Error Analysis powered by Error Analysis, which identifies cohorts of data with higher error rate than the overall benchmark. These discrepancies might occur when the system or model underperforms for specific demographic groups or infrequently observed input conditions in the training data.

Fairness Assessment powered by Fairlearn, which identifies which groups of people may be disproportionately negatively impacted by an AI system and in what ways.

Model Interpretability powered by InterpretML, which explains blackbox models, helping users understand their model's global behavior, or the reasons behind individual predictions.

Counterfactual Analysis powered by DiCE, which shows feature-perturbed versions of the same datapoint who would have received a different prediction outcome, e.g., Taylor's loan has been rejected by the model. But they would have received the loan if their income was higher by $10,000.

Causal Analysis powered by EconML, which focuses on answering What If-style questions to apply data-driven decision-making – how would revenue be affected if a corporation pursues a new pricing strategy? Would a new medication improve a patient’s condition, all else equal?

Data Balance powered by Responsible AI, which helps users gain an overall understanding of their data, identify features receiving the positive outcome more than others, and visualize feature distributions.