Our Principles
We are an AI research and development startup focused on creating cutting-edge an AI platform for businesses. Our commitment to safety and ethics drives our innovation, ensuring that our technologies are both advanced and responsible.
Transparency is essential in our AI development. We ensure our systems are understandable and accessible, providing clear information on decision-making processes to build trust with stakeholders.
We develop AI that is fair and impartial, actively working to identify and mitigate biases to prevent discrimination and ensure equitable outcomes.
Responsible AI Practices
We employ a human-centered design approach to ensure our AI systems meet business needs and provide positive impacts. This involves:
- Designing features with appropriate disclosures.
- Engaging diverse user groups for feedback.
- Modeling potential adverse feedback early and iterating based on real-world testing.
We conduct a thorough validation phase in all developments to ensure our AI systems meet the highest standards of performance and reliability.
Our AI systems are rigorously tested and continuously monitored post-deployment to ensure they work as intended and can be trusted. This includes:
- Unit and integration tests.
- Iterative user testing.
- Post-deployment monitoring and issue resolution.
We proactively identify potential threats to our AI systems and continuously learn to stay ahead of the curve, bringing the latest advancements to our business clients.
International Best Practices: Asilomar Principles
We adhere to the Asilomar AI Principles, coordinated by FLI and developed at the Beneficial AI 2017 conference. These principles are among the earliest and most influential sets of AI governance principles, guiding the ethical development and deployment of AI. For more details, visit the Asilomar AI Principles.
Asilomar AI Principles:
- Research Goals: AI should benefit society and humanity.
- Research Funding: Funding should prioritize beneficial use.
- Science-Policy Link: Encourage a constructive exchange between AI research and policy.
- Research Culture: Promote a cooperative research culture.
- Avoiding Race: Avoid competition that reduces safety standards.
- Safety: AI systems should be safe and secure.
- Failure Transparency: Share knowledge of AI failures.
- Judicial Transparency: Understandable legal reasoning by AI.
- Responsibility: Developers are responsible for AI misuse.
- Value Alignment: AI should align with human values.
- Human Values: Respect human dignity and rights.
- Personal Privacy: AI should protect personal privacy.
- Liberty and Privacy: Ensure AI does not restrict freedoms.
- Shared Benefit: AI should benefit and empower humanity.
- Shared Prosperity: Economic prosperity from AI should be shared.
- Human Control: Humans should control AI systems.
- Non-subversion: AI should respect human authority.
- AI Arms Race: Avoid an arms race in lethal autonomous weapons.
- Capability Caution: Avoid undue assumptions about AI capabilities.
- Importance: Importance should match the potential impact of AI.
- Risks: Risks should be mitigated proactively.
- Recursive Self-Improvement: Ensure AI self-improvement aligns with human values.
- Common Good: Superintelligence should benefit humanity.
Adherence to Standards
We comply with comprehensive regulatory frameworks to ensure the safety and rights of citizens. Our approach includes:
- Risk-Based Categorization: Classifying AI systems by risk levels.
- Clear Labeling: Providing transparency in AI-generated content.
- High-Risk AI Management: Implementing strict measures such as risk management systems, data governance, human oversight, and robust documentation.
- Prohibition of Harmful Practices: Avoiding AI applications that pose unacceptable risks to fundamental rights.
Our principles emphasize:
- Accountability: Regular audits and compliance assessments.
- Privacy and Security: Robust measures to protect user data.
- Inclusivity: Promoting diversity in AI development to reflect a wide range of perspectives.