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Here are Artificial Intelligence (AI) Compliance Requirements, structured with
headers for clarity and comprehensive coverage:
1. Data Privacy and Protection
Global Data Privacy Law Compliance: Adherence to major global data
protection regulations (e.g., GDPR, CCPA, LGPD, India's Digital Personal
Data Protection Act - DPDPA 2023, where applicable).
Privacy-by-Design: Embedding privacy and data protection principles into
the entire AI system development lifecycle from conception.
Data Minimization: Ensuring that AI systems collect, store, and process
only the strictly necessary data for their stated purpose.
Data Anonymization & Pseudonymization: Implementing robust techniques
to render personal data anonymous or pseudonymous, reducing re-
identification risks.
Explicit and Informed Consent: Obtaining clear, unambiguous, and
granular consent from data subjects for data collection and its specific use
within AI systems.
Data Retention and Disposal Policies: Establishing and enforcing clear
policies for how long data used by AI is stored and ensuring secure
disposal when no longer needed.
Data Subject Rights: Providing mechanisms for individuals to exercise
their rights (e.g., access, rectification, erasure, portability, objection to
automated decision-making).
Cross-Border Data Transfer Compliance: Adhering to regulations
governing international data transfers (e.g., Standard Contractual Clauses,
adequacy decisions) for AI data.
Data Lineage and Provenance: Maintaining detailed records of data origin,
transformations, and usage throughout the AI lifecycle to ensure
traceability.
2. Algorithmic Fairness and Bias Mitigation
Systematic Bias Assessment: Regularly identifying, measuring, and
documenting biases within AI algorithms and their training data across
different demographic groups.
Proactive Bias Mitigation: Implementing diverse techniques to reduce and
prevent algorithmic discrimination (e.g., re-sampling, re-weighting, adversarial
debiasing, post-processing methods).
Representative Training Data: Ensuring that training and validation datasets
are diverse, accurately reflect the target population, and minimize under-
representation of specific groups.
Fairness Metrics Monitoring: Defining and continuously tracking quantitative
metrics for fairness to ensure equitable performance and outcomes.
Disparate Impact Analysis: Assessing whether an AI system's output leads to
disproportionately negative outcomes for certain protected characteristics,
even if not intentionally biased.
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3. Transparency and Explainability (XAI)
Model Interpretability: Adopting techniques to enable understanding of
how AI models make decisions (e.g., SHAP, LIME, feature importance).
Comprehensive AI System Documentation: Maintaining detailed records of
AI system architecture, datasets, training parameters, evaluation metrics,
and intended use cases.
Transparent User Communication: Clearly informing users when they are
interacting with an AI system and explaining its purpose, capabilities, and
limitations.
Justification for AI Decisions: Providing clear, understandable, and
actionable explanations for AI-driven decisions, especially in high-stakes
scenarios (e.g., credit decisions, employment).
AI-Generated Content Disclosure: Implementing clear labeling and
provenance metadata for AI-generated text, images, audio, and video
(e.g., deepfakes) to ensure authenticity and prevent misinformation.
Black-Box Explanation Requirements: For complex "black-box" models,
implementing methods to provide post-hoc explanations of their behavior.
4. AI Ethics and Governance
Formal AI Ethics Policy: Developing and embedding a comprehensive AI
ethics policy grounded in principles like human agency, societal well-
being, accountability, and sustainability.
Dedicated AI Governance Body: Establishing a multi-disciplinary AI ethics
committee, board, or working group responsible for oversight, risk
management, and policy implementation.
Regular Policy Review and Update: Continuously reviewing and updating
AI policies and guidelines to adapt to technological advancements,
evolving societal norms, and new regulations.
Responsible AI Culture: Fostering a pervasive culture of responsible AI
within the organization through ongoing training, awareness campaigns,
and incentive structures.
Ethical AI Procurement: Ensuring that third-party AI solutions and vendors
adhere to the organization's ethical AI principles and compliance
standards.
5. Security and Robustness
AI Cybersecurity Measures: Implementing robust security controls for AI
infrastructure, data pipelines, models, and outputs to protect against cyber
threats.
Adversarial Attack Resilience: Designing AI systems to be robust against
adversarial attacks (e.g., data poisoning, model evasion, model inversion)
that could compromise integrity or privacy.
Vulnerability Assessments & Penetration Testing: Regularly conducting
security testing specifically for AI components and integrated systems.
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Incident Response Plan for AI Failures: Developing and testing a specific
incident response plan for AI-related security breaches, critical failures, or
unintended harmful outcomes.
Model Drift and Degradation Monitoring: Continuously monitoring AI model
performance in production to detect and address model drift or
degradation that could lead to non-compliance or inaccurate results.
Data Integrity and Quality Checks: Implementing rigorous data validation,
cleansing, and integrity checks to ensure the reliability of data feeding AI
systems.
6. Legal and Regulatory Adherence
Proactive Regulatory Monitoring: Establishing a mechanism to
continuously track and analyze new and emerging AI-specific laws,
industry standards, and regulatory guidance globally and regionally.
Legal and Compliance Audits: Conducting regular internal and external
audits to verify AI system compliance with all relevant laws, regulations,
and internal policies.
Intellectual Property (IP) Compliance: Ensuring adherence to IP laws
(copyright, patents, trade secrets) concerning training data, AI models, and
AI-generated content; securing necessary licenses.
Product Liability for AI: Understanding and addressing potential product
liability for defects or harms caused by AI-enabled products or services.
Sector-Specific Regulations: Complying with AI regulations tailored to
specific industries (e.g., FDA for medical AI, financial regulations, aviation
safety standards).
Competition Law Compliance: Ensuring AI systems do not facilitate anti-
competitive practices (e.g., algorithmic collusion).
Consumer Protection Laws: Adhering to consumer protection acts
regarding fair dealing, non-deceptive practices, and appropriate
disclosures in AI-driven services.
7. Accountability and Human Oversight
Clear Accountability Framework: Defining clear roles, responsibilities, and
accountability mechanisms for all stages of the AI lifecycle.
Meaningful Human Oversight: Designing AI systems to allow for effective
human supervision, intervention, and the ability to override AI decisions,
especially in high-risk applications.
Human-in-the-Loop Protocols: Implementing clear protocols for human
review and validation of AI outputs before critical decisions are finalized.
Remediation and Redress Mechanisms: Establishing clear channels and
processes for individuals to challenge AI decisions and seek effective
remedies.
8. Risk Management and Impact Assessments
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Comprehensive AI Risk Management Framework: Implementing a
systematic approach (e.g., based on NIST AI RMF) to identify, assess,
prioritize, and mitigate technical, ethical, societal, and operational risks
throughout the AI lifecycle.
Artificial Intelligence Impact Assessments (AIIAs): Conducting structured
assessments to evaluate potential benefits and harms (e.g., on
fundamental rights, environment, social equity) before deploying AI
systems.
Continuous Risk Monitoring: Regularly reviewing and updating risk
assessments as AI systems evolve or operational contexts change.
Scenario Planning & Stress Testing: Conducting simulations and stress
tests to understand how AI systems behave under various conditions,
including adverse ones.
9. Development and Lifecycle Best Practices
Secure Development Lifecycle (SDL) for AI: Integrating security and
compliance considerations into every phase of AI development, from
design to deployment.
Robust Testing and Validation: Implementing rigorous testing protocols,
including unit tests, integration tests, adversarial tests, and real-world
simulations, for AI systems.
Model Versioning and Change Management: Maintaining strict version
control for AI models, training data, and codebases, along with formal
change management processes.
Continuous Monitoring and Improvement: Establishing mechanisms for
ongoing monitoring of AI system performance, bias, and compliance in
production, with processes for continuous improvement and retraining.
