The Recommendation on Artificial Intelligence (AI) is held out as the first intergovernmental standard on AI. The Recommendation was adopted by the OECD Council at Ministerial level on 22 May 2019 on the proposal of the Committee on Digital Economy Policy.
The recommendations are the outcome of OECD research and discussions carried out over a period of 3 years. OECD found that their work had demonstrated a need to shape a policy environment at the international level to ‘foster trust in and adoption of AI in society.’ The recommendations on AI complement existing OECD standards on privacy and data protection, digital security risk management, and responsible business conduct.
The Recommendation on AI contains five high-level values-based principles and five recommendations for national policies and international co-operation. It also proposes a common understanding of key terms, such as “AI system” and “AI actors”, for the purposes of the Recommendation. The following terms have been defined as shown below.
· AI system: An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.
· AI system lifecycle: AI system lifecycle phases involve:
i) ‘design, data and models’; which is a context-dependent sequence encompassing planning and design, data collection and processing, as well as model building;
ii) ‘verification and validation’;
iii) ‘deployment’; and
iv) ‘operation and monitoring’. These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.
· AI knowledge: AI knowledge refers to the skills and resources, such as data, code, algorithms, models, research, know-how, training programmes, governance, processes and best practices, required to understand and participate in the AI system lifecycle.
· AI actors: AI actors are those who play an active role in the AI system lifecycle, including organisations and individuals that deploy or operate AI.
· Stakeholders: Stakeholders encompass all organisations and individuals involved in, or affected by, AI systems, directly or indirectly. AI actors are a subset of stakeholders.
Five high-level values-based principles
1. Inclusive growth, sustainable development and well-being
a. Stakeholders should proactively engage in responsible stewardship of trustworthy AI in pursuit of beneficial outcomes for people and the planet, such as augmenting human capabilities and enhancing creativity, advancing inclusion of underrepresented populations, reducing economic, social, gender and other inequalities, and protecting natural environments, thus invigorating inclusive growth, sustainable development and well-being.
2. Human-centered values and fairness
a. AI actors should respect the rule of law, human rights and democratic values, throughout the AI system lifecycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality, diversity, fairness, social justice, and internationally recognized labor rights.
b. To this end, AI actors should implement mechanisms and safeguards, such as capacity for human determination, that are appropriate to the context and consistent with the state of art.
3. Transparency and explainability
a. AI Actors should commit to transparency and responsible disclosure regarding AI systems. To this end, they should provide meaningful information, appropriate to the context, and consistent with the state of art:
i. to foster a general understanding of AI systems;
ii. to make stakeholders aware of their interactions with AI systems, including in the workplace;
iii. to enable those affected by an AI system to understand the outcome; and
iv. to enable those adversely affected by an AI system to challenge its outcome based on plain and easy-to-understand information on the factors, and the logic that served as the basis for the prediction, recommendation or decision.
4. Robustness, security and safety
a. AI systems should be robust, secure and safe throughout their entire lifecycle so that, in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety risk.
b. To this end, AI actors should ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle, to enable analysis of the AI system’s outcomes and responses to inquiry, appropriate to the context and consistent with the state of art.
c. AI actors should, based on their roles, the context, and their ability to act, apply a systematic risk management approach to each phase of the AI system lifecycle on a continuous basis to address risks related to AI systems, including privacy, digital security, safety and bias.
AI actors should be accountable for the proper functioning of AI systems and for the respect of the above principles, based on their roles, the context, and consistent with the state of art.
Five Recommendations for Policy Makers
6. Investing in AI research and development
a) Governments should consider long-term public investment, and encourage private investment, in research and development, including interdisciplinary efforts, to spur innovation in trustworthy AI that focus on challenging technical issues and on AI issues. AI research related social, legal and ethical implications and policy
b) Governments should also consider public investment and encourage private investment in open datasets that are representative and respect privacy and data protection to support an environment for and development that is free of inappropriate bias and to improve interoperability and use of standards.
7. Fostering a digital ecosystem for AI
Governments should foster the development of, and access to, a digital ecosystem for trustworthy AI. Such an ecosystem includes in particular digital technologies and infrastructure, and mechanisms for sharing AI knowledge, as appropriate. In this regard, governments should consider promoting mechanisms, such as data trusts, to support the safe, fair, legal and ethical sharing of data.
8. Shaping an enabling policy environment for AI
a) Governments should promote a policy environment that tested, and scaled up, as appropriate. supports an agile transition from the research and development stage to the deployment and operation stage for trustworthy AI systems. To this effect, they should consider using experimentation to provide a controlled environment in which AI systems can be tested, and scaled-up, as appropriate
b) Governments should review and adapt, as appropriate, their policy and regulatory frameworks and assessment mechanisms as they apply to AI systems to encourage innovation and competition for trustworthy AI.
9. Building human capacity and preparing for labour market transformation
a) Governments should work closely with stakeholders to prepare for the transformation of the world of work and of society. They should empower people to effectively use and interact with AI systems across the breadth of applications, including by equipping them with the necessary skills.
b) Governments should take step c s, including through social dialogue, to ensure a fair transition for workers as AI is deployed, such as through training programmes along the working life, support for those affected by displacement, and access to new opportunities in the labour market.
c) Governments should also work closely with stakeholders to promote the responsible use of AI at work, to enhance the safety of workers and the quality of jobs, to foster entrepreneurship and productivity, and aim to ensure that the benefits of AI are broadly and fairly shared.
10. International cooperation for trustworthy AI
a) Governments, including developing countries and with stakeholders should actively cooperate to advance these principles and to progress on responsible stewardship of trustworthy AI.
b) Governments should work together in the OECD and other global and regional fora to foster the sharing of AI knowledge, as appropriate. They should encourage international, cross-sectoral and open multi-stakeholder initiatives to garner long-term expertise on AI.
c) Governments should promote the development of multi-stakeholder, consensus-driven technical standards for interoperable and trustworthy AI.
d) Governments should also encourage the development, and their own use, of internationally comparable metrics to measure AI research, development and deployment, and gather the evidence base to assess progress in the implementation of these principles