Championing Responsible AI with Quantiphi

Enabling AI Governance Responsibly



In a world where ethical concerns with AI are widespread, Quantiphi is committed to delivering AI solutions that prioritize safety, security, and social equity. Explore our conscientious approach to Responsible AI (RAI) and discover how we balance intent and impact.

Quantiphi provides a sentient framework for building ethical AI applications. We empower organizations to innovate, enhance, and deploy solutions while ensuring compliance with Responsible AI principles. Join us in shaping a more ethical AI future.

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What is
Responsible AI?

The practice of building and deploying ethical and socially responsible AI solutions that generate positive impacts on society, through transparency, accountability, fairness, security, and respect for human rights.


Why do we
need it?

By building AI solutions that uphold racial, communal, and individual integrity, we can ensure a safer environment for all.

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How do we
do it?

Our AI development process is built on eight fundamental facets of Responsible AI that enables us to develop human-centric and ethically accountable solutions.

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How does Responsible AI impact Businesses?

Responsible AI impacts businesses positively by building trust, ensuring legal compliance, fostering innovation, and reducing costs.

Principles of Responsible AI

Establish diversity at every step of data handling and maintain similar diversity during data annotation, aiming to include people from all backgrounds, age-groups, and ethnicities.

Adhere to the local and federal/national/territory rules and regulations, and be answerable and accountable to the specific governing councils.


Ensure that the most impactful decisions are taken by people using human-in-the-loop design principles, making the implementation process human-centric.


Evaluate modern AI systems on multiple metrics and achieve high performance for those metrics.


Collaborate with the scientific community to advance state-of-the-art techniques and validate the truthfulness and generalization of the AI systems being built at-scale.

Develop and deploy AI systems in secure and conducive environments, both for data collection and storage. Follow best practices while dealing with the security and privacy of data used by these systems.

Build solutions and tools that deliver a net positive impact on society by safeguarding businesses and communities against potential threats.

Maintain complete transparency of data handling as well as model techniques and achieve an AI system that is self-explanatory, helping the users understand the algorithms and their working principles.


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