Responsible AI Policy

1. Purpose 

  • This policy outlines Integra’s commitment to adhering to Responsible AI Principles and guidelines, ensuring compliance with privacy, data protection, and security standards across all AI systems and services for our customers and for internal operational purposes. 

2. Scope 

This policy applies to: 

  • All AI systems and services developed, deployed, acquired or managed by Integra. 
  • All employees, contractors, and third-party collaborators are involved in AI-related projects. 
  • All organizational activities related to data collection, processing, and utilization within AI systems. 
  • All phases of the AI lifecycle, including design, development, testing, deployment, maintenance, and decommissioning. 

3. Objectives 

  • Ensure AI systems are resilient against unauthorized access, data manipulation, and cybersecurity threats.   
  • Prohibit the use of AI for malicious purposes, such as surveillance without consent or spreading misinformation. 
  • Comply with applicable data protection laws and regulations when processing personal data using AI. 
  • Establish clear responsibility and mechanisms for oversight. 

4. Responsible AI Principles 

Integra is committed to ensuring that its AI solutions are responsible, ethical, and transparent. The following principles guide our AI practices: 

4.1. Transparency and Explainability 

  • AI systems shall be designed and implemented in a manner that stakeholders can understand the purpose, functionality, and outcomes of the AI. 
  • Appropriate documentation will be maintained to ensure auditability and clarity. 
  • Integra will ensure transparency with clients regarding the use of AI systems in their projects. For projects involving AI systems, and in line with contractual obligations, Integra will seek prior written consent from clients before deploying AI systems. Such consent will be documented and retained to demonstrate compliance and accountability. 

4.2. Fairness and Non-Discrimination 

  • AI systems are developed to avoid bias and ensure equitable treatment across diverse demographics. 
  • Clients will have a channel to raise concerns or queries about the use of AI in their projects. These grievances will be addressed promptly in alignment with applicable legal and contractual obligations. 
  • Bias Awareness and Training:  
  • Ensure that all team members involved in AI development, deployment, and decision-making receive training on recognizing and mitigating bias. 
  • Conduct regular internal knowledge-sharing sessions on responsible AI practices. 
  • Data Review and Preprocessing: 
  • Prior to fine-tuning AI models or generating AI outputs, review datasets for potential biases related to race, gender, culture, language, or other factors. 
  • Apply techniques such as anonymization, diversification, or rebalancing of training data to reduce bias where feasible. 
  • Prompt and Output Review: 
  • Review and test AI outputs for potential biases or inappropriate content during development and deployment phases. 
  • Implement systematic prompt-testing to identify cases where the model may produce biased or undesirable responses and refine prompts to mitigate such issues. 

4.3. Accountability 

  • All AI projects have designated accountability structures, ensuring clear ownership and responsibility throughout the lifecycle. 
  • A governance committee oversees AI practices to enforce adherence to this policy. 

4.4. Reliability and Safety 

  • AI systems undergo rigorous testing to ensure reliability and safety in their intended use cases. 
  • We design and deploy reliable, secure, and resilient AI systems that are protected against misuse and adversarial attacks. 
  • Contingency measures are in place to address system failures or inaccuracies. 

4.5. Human Oversight 

  • Human intervention is ensured at critical decision points in AI systems. 
  • Employees are trained to effectively supervise AI outputs and rectify anomalies. 

5. Compliance with Privacy, Data Protection, and Security Standards 

5.1. Privacy Framework Compliance 

  • All AI systems are assessed for compliance with applicable privacy laws, including but not limited to GDPR, CCPA & DPDPA and standard data protection framework and regulations. 

5.2. Data Protection Measures 

  • AI systems adhere to Integra’s Data Protection Policy, which mandates data encryption at rest and transit, secure storage, and access control. 
  • Implement strict access controls to limit who can process personal data based on their roles and responsibilities 
  • Personal data is processed only for defined purposes and retained for no longer than necessary. 

5.3. Incident Response 

  • A dedicated Incident Response Team addresses any breaches or non-compliance events promptly. 
  • All incidents are logged, reviewed, categorized based upon their impact and severity, and reported to the appropriate regulatory bodies as per legal requirements. 

6. Governance and Monitoring 

6.1. Regular Audits and Assessments 

  • Quarterly audits will be conducted starting the first quarter of 2025 to ensure ongoing compliance with this policy. 

6.2. Training and Awareness 

  • All employees involved in AI development and management undergo mandatory training on an ongoing basis on Responsible AI and compliance with privacy and security frameworks. 
  • Employees are trained to report suspected violations or any potential non-compliances 

6.3. Adherence to Client-Specific Contractual Guidelines 

  • Integra will ensure that all AI systems, processes, and practices comply with any AI-related guidelines, standards, or requirements explicitly agreed by the parties in client contracts or communicated by clients in advance and in writing. 

7. Non-Compliance and Disciplinary Action 

Failure to comply with this policy may result in: 

  • Project suspension or termination. 
  • Disciplinary action against responsible personnel, including termination of employment or contracts. 
  • Legal or financial consequences in cases of regulatory non-compliance. 

8. Document Control and Revision 

This policy is maintained by the Central Quality team, with reviews conducted annually or as necessitated by regulatory changes. Updates will be communicated to all relevant stakeholders.