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Laws, legal frameworks, and binding policy instruments governing AI development and use.
Also in Legal & Regulatory
1.1 Governments should support and participate in international co-ordination (through bodies such as the OECD and the International Competition Network) to develop best practices and rigorous analysis in understanding the competitive impact of dataset control and AI systems on economic markets. 1.2 Governments should undertake regular reviews to ensure that competition law frameworks and the enforcement tools available to the relevant enforcement authorities are sufficient and effective to ensure sufficient access to necessary inputs, and adequate choice, vibrant rivalry, creative innovation and high quality of output in the development and deployment of AI systems, to the ultimate benefit of consumers.
Reasoning
Governments coordinate internationally and review domestic competition law frameworks to ensure market conditions support AI development and deployment.
Ethical Purpose and Societal Benefit
Organisations that develop, deploy or use AI systems and any national laws that regulate such use should require the purposes of such implementation to be identified and ensure that such purposes are consistent with the overall ethical purposes of beneficence and non-maleficence, as well as the other principles of the Policy Framework for Responsible AI.
3.2.2 Technical StandardsEthical Purpose and Societal Benefit > Overarching principles
2.1.3 Policies & ProceduresEthical Purpose and Societal Benefit > Work and automation
2.2.1 Risk AssessmentEthical Purpose and Societal Benefit > Environmental impact
2.2.1 Risk AssessmentEthical Purpose and Societal Benefit > Weaponised AI
3.1.3 International AgreementsEthical Purpose and Societal Benefit > The weaponisation of false or misleading information
1.2.1 Guardrails & FilteringOther (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management