Fairness and Bias Mitigation

Fairness and Bias Mitigation

Responsible AI can be a force for good in the world, and yet many AI/ML models rely on biased and skewed datasets for their development and training. Learn how Synthesized can help you develop fit-for-purpose, fair and bias-free models.

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With Synthesized ML-powered Fairness and Bias Remediation platform You Can:

Check Out Our Full Suite of Features
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Understand sensitive attributes within your datasets (gender, ethnicity, sexual orientation, etc.) within seconds

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Quantify how different the target variable distribution is for each of these sensitive groups with respect to the rest of the population

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Preserve the privacy of your production data while optimising the quality of your test data

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Automatically mitigate the biases present within the dataset with data rebalancing and generation

Case Study

Ensure Fairness and Transparency in All Data-Driven Applications

The Challenge

With limited, poor-quality or skewed datasets; data-driven applications often fail to achieve their intended purpose as they are inherently biased. Algorithmic bias often results in poor predictive capability, and functional failure with legal and reputational consequences.

The Solution

We believe privacy, fairness and ethical use of data should be key elements of any data-driven company. With the Synthesized platform, the customers can solve the issue of biases in data and hence algorithmic biases.

The customer benefits:

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Correct interpretations of models behavior

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Improve predictive capabilities and efficiencies resulting in emerging business opportunities

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Reduce the risk of non compliance with regulations

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Protect your brand reputation

Learn More about Fairness and Bias Mitigation

Synthesized's DataOps Blog
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