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Our Responsible AI Principles

Enabling AI Governance with Responsibility

Our Responsible AI Principles
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Overview

Today, about 90% of organizations battle ethical issues with AI usage. At Quantiphi, we believe in delivering AI solutions that ensure safety, security, and social equity for all stakeholders. Learn how we conscientiously balance intent and impact in our value-driven Responsible AI approach.

Quantiphi offers a sentient framework for building AI applications that addresses ethical concerns across the AI adoption process. We enable organizations to innovate, improve, and successfully deploy solutions while staying compliant with the key facets of Responsible AI.

Facets of Responsible AI

  • Fairness
  • Governance & Accountability
  • Human-Centric
  • Safety, Robustness & Reliability
  • Scientific Rigor
  • Security & Privacy
  • Socially-Beneficial
  • Transparency & Explainability
Fairness

Fairness

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.

Governance & Accountability

Governance & Accountability

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

Human-Centric

Human-Centric

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

Safety, Robustness & Reliability

Safety, Robustness & Reliability

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

Scientific Rigor

Scientific Rigor

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.

Security & Privacy

Security & Privacy

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.

Socially-Beneficial

Socially-Beneficial

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

Transparency & Explainability

Transparency & Explainability

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.

Fairness

Fairness

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.

Governance & Accountability

Governance & Accountability

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

Human-Centric

Human-Centric

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

Safety, Robustness & Reliability

Safety, Robustness & Reliability

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

Scientific Rigor

Scientific Rigor

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.

Security & Privacy

Security & Privacy

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.

Socially-Beneficial

Socially-Beneficial

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

Transparency & Explainability

Transparency & Explainability

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.

Build AI Solutions of tomorrow, responsibly and confidently

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