OECD AI’s Policy Areas
One of the organisations steadily working for international collaboration is The Organisation for Economic Co-operation and Development (OECD).
It is an intergovernmental economic organisation with 37 member countries, founded in 1961 to stimulate economic progress and world trade.
It is set up to compare policy experiences and to seek answers to common problems.
I decided I would explore their current platform and the policy areas they have outlined within artificial intelligence. This article is made purely to understand these better.
The OECD AI Policy Observatory (OECD.AI) builds on the momentum of the OECD’s Recommendation on Artificial Intelligence (“OECD AI Principles”) — the first intergovernmental standard on AI — adopted in May 2019 by OECD countries and adhered to by range of partner economies.
They are the following:
The OECD AI Principles provided the basis for the G20 AI Principles endorsed by Leaders in June 2019.
After this they have been increasingly been endorsed around the world.
As such one could argue that OECD.ai is one to follow if one is interested in AI Policy.
OECD.AI combines resources from across the OECD its partners and all stakeholder groups.
OECD.AI facilitates dialogue between stakeholders while providing multidisciplinary, evidence-based policy analysis in the areas where AI has the most impact.
It is oriented around three core attributes:
- “Multidisciplinarity. The Observatory works with policy communities across and beyond the OECD — from the digital economy and science and technology policy, to employment, health, consumer protection, education and transport policy — to consider the opportunities and challenges posed by current and future AI developments in a coherent, holistic manner.
- Evidence-based analysis. The Observatory provides a centre for the collection and sharing of evidence on AI, leveraging the OECD’s reputation for measurement methodologies and evidence-based analysis.
- Global multi-stakeholder partnerships. The Observatory engages governments and a wide spectrum of stakeholders — including partners from the technical community, the private sector, academia, civil society and other international organisations — and provides a hub for dialogue and collaboration.”
As promised here is the list. It can be navigated on the website, however I thought I would list all of them here, with the descriptions available on each page:
- “Agriculture. AI is changing agriculture and the food system by enabling automation, better traceability, and the analysis of large volumes of satellite data, among others.
- Competition. Big data and AI-powered pricing algorithms are changing the competitive context and corporate commercial and strategic decision-making.
- Corporate governance. AI calls for responsible corporate governance to build the environment of trust, transparency and accountability necessary to foster the adoption and growth of trustworthy AI.
- Development. AI can create opportunities for economic development and contribute to addressing social, economic or environmental challenges.
- Digital economy. AI impacts every field of digital economy policy, including data governance, privacy, digital security, communications networks and online consumer protection.
- Economy. Through less expensive and more accurate predictions, recommendations or decisions, AI promises to generate productivity gains, improve well-being and help address complex economic challenges.
- Education. Education policy will need adjusting to AI. At the same time, educational increasingly leverage AI technologies that enable novel ways of teaching, learning and skills development.
- Employment. AI is widely expected to change the nature of work as it diffuses across sectors. It will complement humans in some tasks, replace them in others and also generate new types of work.
- Environment. By analysing the flood of climate data being generated everyday, AI is being used to spot patterns and produce more accurate predictions to better respond to the impact of climate change.
- Finance and insurance. AI is being used in the financial sector to improve customer experience, increase the security of systems, identify smart investment opportunities and produce personalised credit scores.
- Health. In healthcare, AI systems help diagnose disease and prevent outbreaks, discover treatments, tailor interventions and power self-monitoring tools. They can facilitate personalised healthcare and precision medicine.
- Industry and entrepreneurship. AI, big data and the overarching digital revolution are bringing new opportunities for businesses by helping them tackle common customer and productivity pain-points. AI tools and an enabling digital setting could help strengthen entrepreneurship and SME ecosystems.
- Innovation. AI could increase the productivity of science, enable novel forms of discovery and innovation in most fields and enhance the reproducibility of scientific research.
- Investment. AI holds promise to help track and optimise international investment flows and international co-operation, advancing growth and sustainable development. At the same time, companies, governments and private equity firms are stepping up investments in AI.
- Public governance. AI tools can enhance the efficiency and quality of public services, and are already impacting how the public sector works and designs policies to serve citizens and businesses. (See also Observatory on Public Sector Innovation)
- Science and technology. AI is becoming indispensable in science to analyse and generate insights from vast amounts of data, tackle complex computational problems, and improve hypothesis generation and experimental design.
- Social and welfare issues. AI can help advance the Sustainable Development Goals (SDGs) and produce more inclusive societies. At the same time there is concern that is could have a disparate impact on vulnerable and under-represented populations.
- Tax. AI could help increase tax compliance and the efficiency of tax administration. At the same time, AI may accelerate automation and impact the design of tax allocation and transfer pricing rules.
- Trade. AI could help increase tax compliance and the efficiency of tax administration. At the same time, AI may accelerate automation and impact the design of tax allocation and transfer pricing rules.
- Transport. Autonomous vehicles with virtual driver systems, high definition maps and optimised traffic routes all promise cost, safety, quality of life and environmental benefits.”
20 Areas can be a lot.
However, it makes sense that artificial intelligence has a wide variety of areas to be addressed.
They have different news feeds relating to each area (programmed I believe, it does not seem curated).
They also have quantitative stats about research and lists of initiatives.
If one were to map within these areas
This is #500daysofAI and you are reading article 351. I am writing one new article about or related to artificial intelligence every day for 500 days. My focus for day 300–400 is about AI, hardware and the climate crisis.