《WEF&毕马威:2025智能经济蓝图:通过区域合作增强人工智能竞争力(英文版)(21页).pdf》由会员分享,可在线阅读,更多相关《WEF&毕马威:2025智能经济蓝图:通过区域合作增强人工智能竞争力(英文版)(21页).pdf(21页珍藏版)》请在三个皮匠报告上搜索。
1、Blueprint for Intelligent Economies:AI Competitiveness through Regional CollaborationW H I T E P A P E RJ A N U A R Y 2 0 2 5In collaboration with KPMGAI Governance AllianceImages:Getty ImagesDisclaimer This document is published by the World Economic Forum as a contribution to a project,insight are
2、a or interaction.The findings,interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum,nor the entirety of its Members,Partners or
3、 other stakeholders.2025 World Economic Forum.All rights reserved.No part of this publication may be reproduced or transmitted in any form or by any means,including photocopying and recording,or by any information storage and retrieval system.ContentsForeword 3Executive summary 41 A blueprint for in
4、telligent economies 51.1 Unpacking the blueprint 51.2 Key stakeholders collaborating to deliver intelligent economies 61.3 Blueprint layers and strategic objectives 62 Spotlight on three strategic objectives 82.1 Build sustainable AI infrastructure 82.2 Curate diverse,high-quality datasets 112.3 Est
5、ablish guardrails for ethics,safety and security 13Conclusion 16Contributors 17Endnotes 19Blueprint for Intelligent Economies2ForewordArtificial intelligence(AI)is set to fuel the Fourth Industrial Revolution,drive economic growth and spur innovation across all industries and societies.While the pro
6、mise of AI is becoming a reality in certain regions,many nations with limited access to energy-intensive AI infrastructure,advanced computing capability,high-quality data and AI skills risk missing out on the economic and societal benefits promised by the age of intelligence.In 2024,the AI Governanc
7、e Alliance,part of the World Economic Forums Centre for the Fourth Industrial Revolution,introduced the AI Competitiveness through Regional Collaboration Initiative to promote a holistic approach to creating more equitable and responsible AI economies and societies.It aims to facilitate prosperous,i
8、nclusive,secure and sustainable intelligence-driven economies while mitigating the widening gap in access to AI technologies through collaborative action.Our white paper,Blueprint for Intelligent Economies:AI Competitiveness through Regional Collaboration,outlines the opportunities that governments,
9、enterprises,academia and wider civic society can harness to achieve a successful AI revolution.Given that aspirations and resources in the field of AI vary significantly,the blueprint provides guidance for nations and regions at any level of digital and AI maturity.We have chosen to spotlight three
10、strategic objectives building sustainable AI infrastructure,curating diverse and high-quality datasets,and establishing guardrails for ethics,safety and security.Each spotlight illustrates a range of challenges and the potential capabilities required by key actors in the AI ecosystem globally.We inv
11、ite you to explore this blueprint and participate in the next phase of our work,which will centre on regional challenges.We thank those who have contributed to this report and look forward to continuing our collaborative efforts to realize the economic and societal benefits of AIfor all.Adrian Clamp
12、 Global Head,Digital Transformation for Sectors;Member,AI Council,KPMG InternationalCathy Li Head,AI,Data and Metaverse;Deputy Head,Centre for the Fourth Industrial Revolution;Member,Executive Committee,World Economic ForumBlueprint for Intelligent Economies:AI Competitiveness through Regional Colla
13、borationJanuary 2025Blueprint for Intelligent Economies3Executive summaryArtificial intelligence(AI)has the potential to profoundly transform economies and societies for the benefit of all,provided it is developed and implemented in a responsible and equitable manner.It can enhance productivity,faci
14、litate the creation of innovative business models and assist in addressing important development challenges.The integration of AI with quantum computing,biotechnology,robotics,spatial intelligence and other emerging technologies will create the foundations for new intelligent economies.Despite the s
15、ignificant technological advancements in AI,these innovations are not equally accessible to much of the world and risk widening the current digital divide.This white paper,Blueprint for Intelligent Economies:AI Competitiveness through Regional Collaboration,encompasses every stage of the AI journey:
16、innovation,development,deployment and adoption of AI for the benefit of all.The blueprint is organized into three interconnected layers:building the foundations,growing new intelligent economies and putting people at the heart.Beneath each layer is a set of strategic objectives,capabilities and init
17、iatives for delivering growth through inclusive AI.The aim of this report is to assist nations and regions,irrespective of their AI maturity level,in identifying the necessary capabilities to advance their AI development.The blueprint provides practical guidance and examples of initiatives that are
18、already proving successful in addressing common challenges.The successful implementation of an AI strategy does not depend on the simultaneous achievement of all outlined strategic objectives and capabilities.Approaches taken by each country and region will vary depending on unique challenges,existi
19、ng resources(natural,human and financial),current AI capabilities(connectivity,compute,data infrastructure,AI innovation and talent)and the ambition of leaders to prioritize AI as a lever for transformational impact.International consultation and collaboration between stakeholders will be required t
20、o facilitate global trade across the AI value chain and secure cross-border data flows.Approved international or regional frameworks for AI safety,standards,ethical AI guardrails and data governance will contribute to promoting the development of inclusive AI models and applications.Close collaborat
21、ion between national and regional governments,global AI leaders,enterprises,small businesses,academia,civil society and end users is essential.Public-private partnerships(PPPs)and academic collaboration are particularly important ways to accelerate the development of successful and agile national AI
22、 ecosystems.Such cooperation is crucial for creating initiatives and solutions that address the needs of local end users and key industry sectors,as well as encouraging the growth of domestic AI innovators.To aid this collaboration,the blueprint is grounded in observations from existing national str
23、ategies and insights gathered from interviews with a diverse array of stakeholders,including public sector representatives,AI industry leaders,infrastructure providers and non-profit organizations operating across various regions.The findings presented herein will serve as the foundation for strateg
24、ic pilot projects at the regional level during the next phase of the Forums AI Competitiveness through Regional CollaborationInitiative.This blueprint outlines a holistic and collaborative approach to creating inclusive growth through intelligent economies.Blueprint for Intelligent Economies41The pu
25、rpose of the blueprint is to act as a framework for strategic decision-making,providing common terminology and objectives that can be used to shape collaborative action.The layers of the blueprint work together in a logical architecture to harness the power of AI and other emerging technologies to c
26、reate innovative intelligent economies.Beneath each of the three layers is a set of nine strategic objectives and enabling capabilities,which should guide the collaboration required by key stakeholders.1.1 Unpacking the blueprintA blueprint for intelligent economies Creating resilient ecosystems wil
27、l require strategic planning to identify and develop vital AI capabilities.A blueprint for intelligent economiesFIGURE 1Strategic objectives CapabilitiesBuild sustainableAI infrastructureCurate diverse,high-quality datasetsDevelop responsible AI modelsHarness channels of AI investment Sustainable an
28、d responsible green energy Secure networks and resilient AI supply chains General access to high-speed connectivity Access to scalable and affordable compute Access to AI-ready devices Available and accessible data Diverse and inclusive data Data ownership and sharing Data protection and privacy Dat
29、a life cycle management Self-governance Multilingual Inclusive design Transparency and explainability Open innovation AI investment partnerships Public-private partnerships AI infrastructure funds Regional investment pools Multi-fund managementBuilding the foundations for intelligent economiesIgnite
30、 an AI-powered industrial revolutionAccelerate deployment of embedded intelligenceCultivate ecosystemsof entrepreneurshipStrategic objectives Capabilities AI enhanced experiences AI agents in workflows AI in robotics AI in devices AI and emerging technologies AI adoption in key sectors AI adoption i
31、n public services Reimagined value chains Pioneering AI disruptors Continuous business model change Innovation hubs and incubators AI entrepreneurship networks Collaborative R&D Communities of open innovation Cooperation on global challengesGrowing new intelligent economies AI awareness and literacy
32、 Personalized and inclusive education Workforce training and support Talent attraction and retention Lifelong learning Ethical guardrails Responsible use guardrails Safety and security standards AI regulations Legal frameworksEstablish guardrails for ethics,safety and securityElevate human potential
33、Strategic objectives CapabilitiesPutting people at the heart of intelligent economiesBlueprint for Intelligent Economies5Governments play a pivotal role in the AI ecosystem.Success in the AI revolution is closely tied to political intent;higher ambitions are likely to attract both public and private
34、 investment.As custodians of national interests,governments shape markets through policy-setting,regulatory frameworks,incentives,funding initiatives and public-private partnerships(PPPs).Large enterprises are at the forefront of AI advancements across various sectors of the economy,often with limit
35、ed governmental involvement.However,collaboration with the public sector,academia and other stakeholders is essential for inclusive AI growth.AI offers significant opportunities for enterprises to transform products and services,enhance experiences and revamp entire value chains.Large businesses hav
36、e a responsibility to promote AI adoption within their workforce through partnerships with the government.Small-and medium-sized enterprises(SMEs)form the backbone of every economy,and their rapid adoption of AI can significantly boost growth and prosperity.However,many SMEs tend to be slow technolo
37、gy adopters and often lack early insight into the benefits and return on investment of AI.Conversely,tech start-ups are often the first adopters of AI and play a crucial role in shaping national AI inclusivity and data diversity,stimulating entrepreneurial talent and local innovation.Academia plays
38、a crucial role in AI development and adoption.This spans from pioneering research in AI ethics to the development of AI education programmes created in partnership with government and industry partners.Its function is essential in bridging the gap between academic research and large-scale practical
39、application.Civic society is an essential ally in ensuring AI development aligns with societal values and wider societal benefits.Organizations can identify potential inequalities exacerbated by AI,push for mitigating measures,hold governments and businesses to account,and encourage grassroots innov
40、ation in AI applications that address local challenges.1.2 Key stakeholders collaborating to deliver intelligent economies1.3 Blueprint layers and strategic objectivesThe blueprint is organized in layers,each containing a set of strategic objectives.The purpose of each strategic objective is to high
41、light five capabilities that support AI initiatives at a national,regional andglobal level.Building the foundations for intelligent economies This layer of the blueprint covers the foundations upon which the new economic activities will be built.These include access to sufficient and sustainable ene
42、rgy resources to support the significant expansion of compute infrastructure,access to high-quality data,responsible AI models and efficient capital investment channels needed by intelligent economies.Build sustainable AI infrastructure:This objective is focused on building and maintaining the found
43、ational infrastructure for modern digital networks,ensuring that AI technology is powered in an environmentally responsible and sustainable manner while being accessible and inclusive.Curate diverse,high-quality datasets:This objective is focused on the curation of available,accessible,diverse and i
44、nclusive high-quality data that considers the needs,characteristics,and cultures of a population and their languages.It also covers the creation of data governance,data protection and privacy frameworks.Develop responsible AI models:This objective encompasses the development of open-source or closed
45、 models,foundational large language,or smaller and tailored domain-specific models.It also explores the creation of AI model self-governance and regulation.Harness channels of AI investment:This objective is the key enabler of the foundational layer.Significant amounts of capital will need to be rai
46、sed and efficiently channelled into impactful AI projects.New capabilities,such as public-private investment partnerships and the creation of specific AI infrastructure development funds,willbe needed.Blueprint for Intelligent Economies6 Growing new intelligent economiesThis layer of the blueprint b
47、uilds upon the capabilities of the foundational layer to reimagine the core activities undertaken in every sector of new AI economies.Core value chains in all sectors will be transformed by embedding intelligence within applications,workflows,devices and robotics fundamentally changing how work is d
48、one.Accelerate deployment of embedded intelligence:This objective encourages the automation of manual workflows through the accelerated deployment of AI in application workflows,physical devices,and robotics and the integration of AI with other advanced technologies.AI agents with embedded intellige
49、nce will offer the possibility of orchestrating complex activities.Ignite an AI-powered industrial revolution:This objective refers to the need to prioritize the adoption of AI in sectors and public services.Sectors like healthcare,finance,advanced manufacturing,food and agriculture,and sustainable
50、and responsible energy can enable downstream benefits to other sectors of the economy.Cultivate ecosystems of entrpreneurship:This objective promotes the creation of AI innovation hubs,incubators and international entrepreneurial ecosystems,supported by venture funding and scale-up capital,all of wh
51、ich are key to nurturing new networks of innovators.Putting people at the heart of intelligent economies This layer of the blueprint shapes the direction of new intelligent economies.It encompasses the introduction of essential guidelines,policies and regulations that will shape and govern all the a
52、ctivities of the new intelligent economies,as well as the skills and workforce training required to drive human potential.Elevate human potential:This objective focuses on empowering individuals through accessible high-quality education,skill development and workforce training.These initiatives ensu
53、re that communities and societies have the lifelong learning opportunities necessary to thrive amid the significant opportunities and disruptive changes brought about by AI.Establish guardrails for ethics,safety and security:This objective refers to the importance of designing a future where the imp
54、act of AI on economies and wider society is guided by strong ethical guardrails,and safety and security controls.Blueprint for Intelligent Economies7Spotlight on three strategic objectivesThis section explores the three strategic objectives commonly prioritized in all geographic regions.2While all n
55、ine strategic objectives must be considered holistically to drive an effective AI strategy,feedback from government,enterprises and academic stakeholders involved in the development of this white paper identified three that are the most often prioritized within national AI programmes:building sustai
56、nable AI infrastructure,curating diverse and high-quality datasets,and establishing guardrails for ethics,safety and security.This section provides a comprehensive analysis of these three objectives,including an outline of the key challenges,necessary enablers and examples of practical decisions tha
57、t stakeholders can make.2.1 Build sustainable AI infrastructureDelivering sustainable and resilient AI infrastructure will require significant investment and cross-sector collaboration to create scalable,secure and environmentally responsible systems.The challenges are complex and multifaceted,requi
58、ring new capabilities involving the coordination of efforts at global,regional and national levels.Key challenges in building sustainable AI infrastructureTABLE 1Key challengesExamples of successful initiativesHigh energy consumption and environmental impactAdvanced energy programmes to power data c
59、entres:Collaborative agreements between data centre firms and energy providers can support the deployment of sustainable energy such as wind,solar or nuclear to power the massive demands for compute.Energy efficient AI model development:Organizations are complementing large models with smaller secto
60、r-specific models trained on narrow datasets to optimize energy consumption during large language model(LLM)training.AI optimized energy consumption:Use AI to optimize energy management by predicting consumption patterns,forecasting demand requirements and automating distribution.Significant scale o
61、f investments requiredRegional sharing of AI infrastructure:Data centre capacity is being shared between countries via regional clusters and arrays.Incentives for private sector investment:Governments are introducing tax incentives and financial grants and creating the wider enabling environment(thr
62、ough AI national strategies,regulatory reforms,capacity building,etc).Non-secure and non-resilient AI supply chainsInternational trade corridors:Trade agreements between international partners,bilateral or multilateral agreements,and lists of trusted suppliers are enabling resilient and diverse AI s
63、upply chains.National AI clusters:Governments are facilitating the development of clusters that enable the research,design and manufacturing of AI critical hardware to stay onshore.AI cloud resources:Collaboration with home-grown technology firms and global leaders of cloud infrastructure are contri
64、buting to creating trusted national sovereign clouds.A growing digital gapPartnerships with network providers:Some markets are now providing access to emerging high-speed and full coverage internet networks such as satellite internet constellation or collaborating with telecommunication providers fo
65、r mobile internet deployment.High cost of current generation of internet devicesDevice subsidy programmes:Subsidized low-cost devices and network connectivity are supporting low-income and digitally disadvantaged groups.Low-cost AI optimized devices:Collaboration with technology providers and non-pr
66、ofit organizations is providing widespread access to devices for impactful AI use cases.Blueprint for Intelligent Economies8Building sustainable AI infrastructure requires a coordinated action plan involving many stakeholders.A preliminary set of five capabilities frames how this strategic objective
67、 can be delivered:Sustainable and responsible green energy To minimize the environmental impact of AI,it is crucial that the rapid expansion in data centres is powered by sustainable and responsible energy sources.The substantial financial investment in this infrastructure in some regions will not b
68、e affordable for most countries.Therefore,viable alternatives will be necessary,alongside strong collaboration among energy providers,environmental organizations,technology firms and governments.Illustrating the substantial scale of the commitments that global tech firms have begun to make in the mo
69、st mature AI markets,Microsoft recently signed a power purchasing agreement in the US to buy carbon-free energy using only nuclear power.1 This agreement will reopen the Three Mile Island plant,shut down since 2019,solely to supply the Microsoft data centre with green energy.For the benefit of emerg
70、ing and developing economies,the World Bank has created a$2 billion,10-year,multi-phased initiative to add 15 gigawatts(GW)of renewable energy capacity(enough to charge 650 million electric cars every year).The plan aims to eliminate around 240 million tonnes of carbon emissions(equivalent to avoidi
71、ng the combustion of 100 billion litres of gasoline).The first initiative of the programme involves a$657 million financing facility for Turkey and an enabling framework to attract private capital to scale up renewable energy.2As AI use cases expand,extensive decarbonization opportunities are emergi
72、ng to support global climate and energy conservation goals.AI is already being embedded into building and network management,predictive maintenance,grid optimization and fleet management.Some use cases demonstrate conservation rates of up to 60%,with potential for further optimization.3Secure networ
73、ks and resilient AI supply chainsResilience means that national critical infrastructure and enterprise AI systems are protected against disruption and can withstand cyberattacks and other risks.Mitigating these risks necessitates cooperation among cybersecurity firms,infrastructure providers and gov
74、ernments to develop robust AI infrastructure and associated regulatory guardrails.Foreign investment plays a significant role here.It can accelerate development and provide essential resources but may also create geopolitical complexities.Without a strategy to ensure the diversity of AI hardware,cou
75、ntries may find themselves grappling with strategic and political considerations,particularly related to national security and economic sovereignty.Governments are thus compelled to assess the implications of being reliant on other countries or tech firms for critical infrastructure.Governments can
76、guide industry to deploy resilient,scalable and secure foundations for AI by establishing a national AI security framework,facilitating public-public or PPPs,or directly leading AI infrastructure development.These strategies offer various balances between government control,private sector involvemen
77、t and international cooperation.The development of new international trade corridors presents another viable route to building resilient AI supply chains,offering flexibility,reduced risk,and improved resilience through diversified sourcing and distribution networks.Trade corridors can alleviate sup
78、ply chain issues related to AI hardware(such as microchips and critical resources like cobalt used in semiconductors)by improving material access and streamlining transport.The NY SMART I-Corridor illustrates the potential of trade corridors;it aims to establish a world-leading semiconductor cluster
79、 by joining more than 100 regional semiconductor suppliers together to provide local industries with expansive growth opportunities.4Access to high-speed connectivityAccess to high-speed networks is crucial for the effective functioning of AI systems and to encourage inclusive digital participation.
80、Expanding this infrastructure,especially in underserved areas,can represent a significant opportunity for telecommunication providers and technology firms.Currently,approximately 2.6 billion people(one-third of the global population)remain offline.5Digitalpublic infrastructure(DPI)is a set of secure
81、 and interoperable digital systems built on open standards.They enable a community of competitive market players to provide innovative digital services to deliver public service objectives.DPI is another way to bridge the digital divide and aid inclusion by enabling businesses and citizens to gain a
82、ccess to online resources like digital payments,healthcare services and technology.6AI is being added to DPI to personalize user interactions and translate languages in real time.Additionally,DPI can be used as a catalyst for AI To minimize the environmental impact of AI,it is crucial that the rapid
83、 expansion in data centres is powered by sustainable and responsible energy sources.Blueprint for Intelligent Economies9innovation through the development and sharing of open AI models that can be replicated,modified and shared within the constraints of secure environments.Yet the integration of AI
84、with DPI can also bring new challenges,such as the risk of large-scale data breaches.The United Nations DPI Safeguard Framework outlines an approach for mitigating risks at both individual and societal levels.Regionally,India has been a pioneer in DPI.The widely successful deployment of the Unified
85、Payments Interface(UPI)for small payments,used for 11.7 billion transactions in 2023,demonstrates how DPI can provide inclusive access to high-speed networks for enabling innovative and impactful solutions at a low cost.7Access to scalable and affordable computeScalable and affordable compute capaci
86、ty is also essential for supporting large-scale AI applications.The global AI infrastructure market was valued at$35.42 billion in 2023 and is projected to grow at a compound annual growth rate(CAGR)of 30.4%,reaching$223.45 billion by 2030.8 Governments are significant investors.The US Federal Gover
87、nment,for example,spent$3.3 billion on AI in fiscal year 2023,more than double the$1.38 billion spent in 2018.9 A recent report by the Tony Blair Institute for Global Change notes that care should be taken as broad investment in high technology will not translate to compute capability,meaning a spec
88、ific AI compute strategy is needed.Tech companies and private investors(including private equity,asset finance funds and infrastructure funds)can play a significant role in developing large-scale data centres.One alternative to training large language models(LLMs)in large data centres is to use smal
89、l,domain-specific models that provide narrow-focused expert intelligence for a fraction of the cost of training a large language model.This can be an effective strategy for smaller countries and enterprises.Country collaborations can also help provide access to compute.In one recent successful examp
90、le,Rwanda and Qatar signed a memorandum of understanding(MoU)to enhance collaboration in R&D in AI,building digital public infrastructure for innovation and transformation.10The International Computation and AI Network(ICAIN)is a Swiss initiative with a global focus on broadening access to AI resour
91、ces for sustainable development research.It connects AI capabilities,such as computing power,data and expertise with research projects aligned with the UN Sustainable Development Goals.ICAIN uses two supercomputers and draws on the expertise of experienced policy-makers and AI research experts in Eu
92、rope and Africa to develop AI models that benefit societal needs.Access to AI-ready devicesThe use ofsmartphones and computers has increased significantly in the past decade,with low-cost devices(such as the affordable Reliance Jio smartphone)driving adoption within the Global South.Within the conte
93、xt of an inclusive national AI ecosystem,low-cost devices can be used to run AI-enabled applications that have been optimized to work with limited compute capability,weak internet connectivity and low levels of battery storage.An alternate way to increase access to AI-ready devices is to subsidize t
94、he distribution of devices.Singapores Digital for Life initiative under the Smart Nation Singapore programme,in partnership with the Edison Alliance,provides subsided devices to low-income families through PPPs with telecom and tech providers.11 Additionally,there are numerous low-cost devices that
95、can be used to facilitate inclusive AI access,such as the previously mentioned Jio smartphone and Computer Aid.The latter,in partnership with large technology companies,provides access to computers and mobile devices.10Blueprint for Intelligent EconomiesData is crucial for developing equitable,accur
96、ate and fair AI models.Various data-related challenges exist,including data accessibility,imbalance and ownership.Different methodologies are being implemented globally to address these issues.2.2 Curate diverse,high-quality datasetsKey challenges in curating high-quality and diverse datasetsTABLE 2
97、Key challengesExamples of successful initiativesAccess to high-quality dataOpen data platforms:Government programmes are encouraging the development of open data sharing through the mutual sharing of public and private datasets.Synthetic data:Synthetic data is being used when there is a lack of dive
98、rse dataset availability,specifically for model training requirements.Transparent multi-sided data markets:Developing marketplaces that allow for the structured exchange of data are helping to free data currently locked away within large platforms.Addressing current data inequityDiverse and inclusiv
99、e regional datasets:Capturing and curating datasets that represent local communities ensures that regional knowledge and insight are represented within AI model development.Digital language banks:Governments,the private sector and non-governmental organizations(NGOs)are collaborating to capture diff
100、erences in idioms,cultural norms and religious considerations to build diverse language training datasets.Data equity approach:The adoption of a data equity approach across industries is helping to ensure that data represents all parts of the population.Increasing data ownership considerationsIntern
101、ational data sharing agreements:Cross-border data flows within bilateral or multilateral trade agreements are accelerating the pace of innovation and AI product deployment while protecting national interests.Data residency requirements:National security and data protections are being governed throug
102、h data residency requirements,which are also impacting AI regional infrastructure investment.Keeping pace with advancements in AIData governance frameworks:Frameworks and data protection rules are providing robust guidelines to ensure data accuracy,reliability,consistency,licencing and compliance ac
103、ross all stages of the AI development life cycle.Consensus on data quality:National and regional collaboration can help build parameters for collecting high-quality data,including the timeliness,accuracy,completeness,representativeness and consistency of metadata.Lack of trust in AIRobust AI disclos
104、ure requirements:These are being developed to ensure that individuals and organizations understand when their outputs are AI-derived,while providing greater transparency on sources.New guidance for the thresholds of data collection:Refreshed guidelines on data privacy are being adopted to address ri
105、sks related to personal data collected by AI.Opt-in/out approaches:Organizations are exploring the possibility of providing an opt-in/opt-out approach for individuals to make informed choices on the benefits of AI usage versus their choice not to engage.Curating diverse and high-quality datasets req
106、uires a coordinated action plan involving many stakeholders.A preliminary set of five capabilities frames how this strategic objective can be delivered:Available and accessible dataTo realize the transformative nature of AI,data must be available and accessible for AI model development so that AI ca
107、n truthfully and accurately represent the spectrum of communities it aims to empower.In the context of inclusive AI,it is important to consider the sensitivity,relationship,originality and value of data for model development.Globally,governments have committed to the United Nations Global Digital Co
108、mpact,which emphasizes the need for multistakeholder cooperation for the development and deployment of open data,software and AI models.Fugaku LLM,for example,is a Japanese-based open-source LLM developed by a public-private and academic partnership.12 Trained on over 380 billion tokens of data,sign
109、ificant effort was made to ensure that at least 60%of the training data originated in Japan for a Japanese audience.When data is not available,artificially generated data(known as“synthetic data”)can bridge the gap.13 However,while synthetic data can be helpful,training a model purely on synthetic d
110、ata can result in narrow model outputs,leading to the erosion of the diversity that the synthetic data is aiming to address.14Blueprint for Intelligent Economies11Diverse and inclusive dataEquitable data is not a luxury;diverse and inclusive datasets are essential for creating AI that reflects and s
111、erves all of humanity.The World Economic Forums Global Future Council on Data Equity defines data equity as the shared responsibility for fair data practices that respect and promote human rights,opportunity and dignity.Data equity is a fundamental responsibility that requires strategic,participativ
112、e,inclusive,proactive and coordinated action.It aims to create a world where data-based systems promote fair,just and beneficial outcomes for all individuals,groups and communities.15National language models are an important new way to support data equity.A PPP between the United Arab Emirates gover
113、nment and G42 has developed one of the worlds first LLMs based specifically on modern standard Arabic(understood across the Middle East)and regional diverse spoken dialects.16 Known as“Jais”,the LLM draws on local media reports and social media posts to ensure that locally spoken languages are inclu
114、ded within the LLM development while also considering cultural norms.Taking inspiration from Google Researchs language inclusion work and the concept of digital language banks,Jais should act as a catalyst to enabling region-specific model requirements.Additionally,Cohere has developed Aya,a dataset
115、(more specifically,a digital language bank)that represents one of the largest collections of multilingual models covering 114 languages,including rare and local dialects.17 The Aya models and datasets have been released publicly with the intention of safely advancing the R&D of multilingual capabili
116、ties.Data ownership and sharingThe controlled ownership of data enables governments to regulate how data is shared internationally,thereby reducing misuse and promoting trust in AI applications.This complexity of data ownership is now increasing with the emergence of the agent economy and multi-agen
117、t interactions,where data is modified many times during use.The past few years have seen a shift to data residency restrictions,often justified as essential to national security.These restrictions are now shaping data centre investment as tech companies look to comply with data residency requirement
118、s,operational compliance and,in some cases,the need for individual consent.Microsofts recent announcement of significant investment into cloud services in Saudi Arabia,18 for example,is partly driven by market demand and partly by evolving regional data residency requirements.Data protection and pri
119、vacyEmerging privacy challenges such as deepfakes,AI-generated misinformation and high-profile data breaches are increasing mistrust in AI.Tools such as the World Economic Forums Digital Trust Framework can support regulators and industry leaders in considering shared goals and values in the develop
120、ment,use and application of AI.19Disclosure requirements mandate organizations to share information about their data practices,including how data is collected,used and protected.Broadening these requirements to include AI-derived data enhances data protection.It requires companies to clarify how the
121、y use AI to process and generate insights from personal information.20 Expanding these requirements in this way may mean that companies need to offer their users an opt-in/out option to consent to expanding the purpose for which their data is used.Data life cycle managementRegulatory tools remain ke
122、y to safeguarding the privacy and security of data.Existing national data governance frameworks can be adapted and employed to ensure data is managed responsibly in the context of AI development,deployment and use.International agreements on cross-border data flows are becoming increasingly vital to
123、ols to minimizing regulatory obstacles,enhancing collaborative research and knowledge sharing related to AI,and building trust in data sharing.Collaboration with stakeholders at regional and global levels can lead to the development of shared terminology of concepts relating to privacy and data prot
124、ection,thereby promoting clarity and effective communication between all stakeholders.Data intermediaries and stewards,along with leadership from chief data officers,have an important role to play in guiding the data strategy for collecting,sharing and using data.Data free flow with trust(DFFT)polic
125、ies enforce the need to govern the flow of data,both within the data type and how it is used,21 however more comprehensive data governance is required to ensure that AI is developed responsibly and ethically.This does not happen organically,and,given the recent advancement of AI,governments must add
126、ress their wider data governance approach to ensure that data is managed responsibly,with safeguards to protect privacy,security and ownership.Emerging privacy challenges such as deepfakes,AI-generated misinformation and high-profile data breaches are increasing mistrustin AI.Blueprint for Intellige
127、nt Economies12Robust ethical and regulatory frameworks for AI are essential to ensuring that technology benefits society while reducing risks.Establishing standards prevents misuse,bias and ethical breaches,strengthening trust in AI and promoting responsible development and use.2.3 Establish guardra
128、ils for ethics,safety and securityKey challenges in establishing guardrails for responsible AITABLE 3Establishing guardrails for ethics,safety and security requires a coordinated action plan involving many stakeholders.A preliminary set of five capabilities frames how this strategic objective can be
129、 delivered:Ethical guardrailsEthical guardrails are essential for building societal trust and ensuring AI systems align with both global and local values.AI systems predominantly trained on Western-centric data risk perpetuating cultural biases when deployed globally,thereby creating ethical dilemma
130、s in culturally diverse settings.Efforts like the Organisation for Economic Co-operation and Developments(OECD)International Standards and regional initiatives such as the African Unions Continental AI Strategy play crucial roles in reflecting diverse values.However,the lack of universally accepted
131、ethical standards significantly complicates the implementation of ethical principles in AI systems.As AI ethics governance evolves,there is a growing recognition of the need for culturally sensitive approaches.In 2024,eight global tech companies announced their intention to align with the United Nat
132、ions Educational,Scientific and Cultural Organizations(UNESCO)Recommendation on the Ethics of AI,22 which emphasizes cultural sensitivity in AI development and deployment.23 Initiatives in Australia,Canada and New Zealand focus on integrating indigenous knowledge and perspectives,such as those of th
133、e Mori,into AI systems.24 Similarly,frameworks developed by the Council of Europe25 and Singapore reflect their unique societal values and risk tolerances.However,the UNs 2024 report,Governing AI for Humanity,highlights a critical current gap:“whole parts of the world have been left out of internati
134、onal AI governance conversations primarily in the Global South”.26 This homogenization of AI ethics,dominated by perspectives from the Global North,risks excluding diverse cultural philosophies and interests worldwide.Addressing this issue requires stakeholders to commit to comprehensive approaches
135、to ethical AI development and deployment.Key challengesExamples of successful initiativesMitigating bias,ensuring equity and inclusionConsensus on ethical AI:Consensus can be reached through collaborations between international and regional bodies,along with industry and civil society engagement.Awa
136、reness campaigns:Initiatives that solicit cultural and regional feedback to inform policy development are being prioritized within historically underrepresented groups and communities that do not fully trust AI.Navigating evolving regulatory landscapesEnhanced data and technology regulations:These a
137、re helping organizations to consider the changing landscape within the context of AI while assigning responsibilities and ownership of AIs regulatory challenges.Risk-based regulatory approaches:These are being used to ensure regulation remains in line with the fast-paced advancement of AI,addressing
138、 the balance between supporting innovation within defined AI safety and security considerations.Securing AI against emerging risksAI safety bodies:These bodies are contributing to the development of global and regional AI safety standards.Agreement to“red lines”:Defining the highest-risk use cases t
139、hrough the continuous dialogue between all stakeholders in the AI value chain Implementing accountable AI practicesAdaptation of existing AI regulatory frameworks:Governments and industry are using regulation and self-regulation to encourage operationalization of self-governance.AI intellectual prop
140、erty(IP)rights and legal uncertaintyAlignment on global IP standards:Collaboration between international IP boards and industry groups is ensuring that emerging AI technology definitions are common within rights and legal frameworks.International AI IP sharing platforms:These are being developed to
141、enable cross-boundary commercial partnerships and alignment around R&D outcomes.Blueprint for Intelligent Economies13Multiple international AI governance initiatives are being put in place,including UNESCOs Global AI Ethics and Governance Observatory,27 the Readiness Assessment Methodology(RAM)tool,
142、28 the Hiroshima Principles29 and the OECDs ethical AI governance framework.30 While these efforts lay a strong foundation,there remains a critical need for further action and broader agreement on global ethics frameworks for AI.Responsible use guardrails Responsible use guardrails promote the ethic
143、al and responsible management and use of AI across various sectors,helping to prevent harmful applications while maintaining public trust and accountability.The recently published USAID AI Action Plan report notes the need for significant stakeholder engagement,with governments providing strategic v
144、ision,and academia addressing complex challenges with civil society.Establishing responsible AI practices requires a thoughtful approach to ethical standards,comprehensive transparency initiatives and a continuous dedication to societal improvement in various technological domains.The challenge enco
145、mpasses not only the creation of these standards but also the cultivation of public trust and the maintenance of accountability amid the swift progression of AI technologies.Self-governance tools have been widely adopted by the largest developers of AI models,such as Microsofts Responsible AI Princi
146、ples,31 Googles AI Principles32 and Salesforces Office of Ethical and Humane Use.33The promotion of self-governance processes should be encouraged within the small and medium-sized technology business community.However,self-regulation presents a host of challenges such as limited oversight and accou
147、ntability.Self-regulation alone can sometimes be insufficient and necessitates a degree of governmental intervention to ensure consistency in the implementation of responsible and ethical AI principles.Safety and security standards AI poses risks that are both known and still emerging,particularly a
148、s researchers progress towards advanced AI development,such as artificial general intelligence(AGI).To mitigate these AI safety and security risks,it is crucial to first establish clear policy“red lines”and safety guardrails.The EU Artificial Intelligence Act34 categorizes AI applications into risk
149、levels,setting requirements for high-risk areas like critical infrastructure while promoting innovation in low-risk sectors.This approach defines“red line”areas where AI poses unacceptable risks.In another example,the collaboration between the US and UK,through their AI Safety Institutes,35 focuses
150、on developing shared frameworks for testing advanced AI models,emphasizing international collaboration.The NIST AI risk framework36 is an example of a voluntary structure for managing AI risks,emphasizing trustworthiness and alignment with international standards but still lacking in enforceability
151、and global consensus.A global framework and international body for AI could set boundaries on high-risk technologies,such as autonomous weapons and mass surveillance systems.The recent collaboration between the UK,US and Canada on AI in the nuclear sector37 highlights the importance of international
152、 cooperation,emphasizing risk management and balancing human oversight with AI autonomy.AI regulationsThe rapidly changing regulatory landscape for AI presents significant challenges for industries delivering technological advancements at regional or global scale.Companies must adapt to complex and
153、evolving AI-specific regulations across various jurisdictions while ensuring compliance with data protection laws and industry standards.Regulatory approaches vary widely,from hands-off to hands-on,and can differ even within regions.A hands-off approach to regulation minimizes government interventio
154、n,allowing for rapid innovation and market-driven growth by reducing barriers to entry.While this creates an environment conducive to experimentation,it has led to public concerns over privacy violations and the misuse of technologies like facial recognition.The alternative is a hands-on approach th
155、at promotes government intervention with clear guidelines and accountability mechanisms.The EU Artificial Intelligence Act is one example,setting regulated requirements for high-risk AI applications,aiming to protect public interests while encouraging innovation through structured oversight.Narrowly
156、 targeted regulation by governments can also be a valuable policy lever and can proactively prevent emerging AI risks while supporting innovation.The World Economic Forums Governance in the Age of Generative AI report38 suggests that governments should enhance existing regulations,clarify authoritie
157、s and assign responsibilities to adapt to AIs regulatory challenges.This includes addressing privacy,consumer protection,product liability and competition issues.Establishing responsible AI practices requires a thoughtful approach to ethical standards,comprehensive transparency initiatives and a con
158、tinuous dedication to societal improvement.Blueprint for Intelligent Economies14Legal frameworks The advancement of AI has introduced new challenges related to IP,primarily owing to legal uncertainties surrounding AI-generated works and the unauthorized use of copyrighted materials for training AI m
159、odels.Traditional copyright laws require meaningful human contribution,which generative AI frequently lacks,thereby complicating matters of authorship and ownership.Although this thinking is evolving within legal jurisprudence,ambiguities persist.Existing laws addressing these challenges are intrica
160、te and continue to evolve,consisting of a patchwork of national regulations and ongoing legal debates.Prominent lawsuits underscore disagreements regarding whether AI-generated outputs that closely mimic copyrighted works constitute infringement or fair use.Interpretations differ significantly acros
161、s jurisdictions.For instance,the EUs copyright directive includes exceptions for text and data mining,39 while Japan has expanded fair use to cover certain AI training activities.40To address these challenges,regional legal frameworks are necessary to manage the borderless nature of AI technologies
162、effectively.Developing global IP standards,led by organizations like the World Intellectual Property Organization(WIPO),41 will help ensure that AI intellectual property is safeguarded internationally.Blueprint for Intelligent Economies15ConclusionDesigning national and regional AI strategiesNationa
163、l or regional AI strategies can be effective tools for designing and implementing AI initiatives.They should also be endorsed at the highest level to demonstrate a commitment to long-term success.Achieving equitable access to AI necessitates a hybrid approach,combining a top-down approach to develop
164、ing national and regional strategies with bottom-up initiatives that actively involve end users,individuals,communities,entrepreneurs,businesses and government administrations to uncover their needs.Strategies must tackle the most pressing local challenges.Their long-term impact must be considered f
165、rom the outset.For example,while responsible AI and data governance frameworks are essential,their development must consider the potential local impact these may have on innovation and investment decisions.Adapting innovative solutions to the market While there is no one-size-fits-all approach to ta
166、ckling these challenges,it can be effective to replicate existing successful solutions developed elsewhere.For example,regional frameworks for sharing AI infrastructure and energy can overcome national resource limitations,while DPI can improve the reach of a countrys digital and AI ecosystem for pa
167、yments or for publishing open AI models.Centralized databanks offer an innovative way to create multilingual and inclusive local datasets to feed AI applications tailored to users needs.Public-private subsidies can widen access to low-cost AI-ready devices and encourage small businesses to implement
168、 AI-powered applications.Multistakeholder action at the global and national levelTechnological advancements are projected to remain driven by the private sector.As such,it will become increasingly important for innovative AI solutions to be more inclusive of their expanding global user base.The adop
169、tion of AI remains a significant challenge in many regions globally,especially within the small business sector.Collaboration between the private and public sectors can encourage entrepreneurs and small businesses to implement AI-powered applications through specific incentives and educational progr
170、ams.Governments can prioritize strategic sectors such as health,education,finance,agriculture and energy by implementing supportive policies aimed at advancing AI innovation and enabling these sectors to act as engines of growth.Additionally,governments,educational institutions and industries can co
171、llaborate to offer skills training,workforce development,and continuous learning opportunities.This will ensure that individuals are equipped to adapt to the evolving impact of AI on the workplace.Academia and the education sectors are critical in cultivating the talent pool required to sustain the
172、pace of innovation.Collaborations between the AI industry and various industrial sectors have the potential to uncover solutions to global challenges.Towards a regional dialogueThe subsequent phase of this work will focus on implementing these recommended actions with active participation from regio
173、nal stakeholders.Collective efforts will focus on assessing common challenges and proposing solutions aimed at developing or enhancing AI ecosystems.Blueprint for Intelligent Economies16ContributorsAcknowledgementsSincere appreciation is extended to the following working group members,who took part
174、in dedicated interviews or provided critical input and feedback on the draft.Their diverse insights were fundamental to the success of this work.Abdulaziz AlAliHead,Centre for the Fourth Industrial Revolution,QatarBasma AlBuhairanManaging Director,Centre for the Fourth Industrial Revolution Saudi Ar
175、abiaUthman AliGlobal Responsible AI Officer,BPDena AlmansooriGroup Chief Artifical Intelligence and Data Officer,e&Khalid AlNuaimiProject Manager,Ministry of Cabinet Affairs,UnitedArab EmiratesManail Anis AhmedLecturer,Princeton UniversityZiyaad BhoratSenior Advisor,AI Ecosystem Strategy,Mozilla Fou
176、ndation Daniela BragaFounder and Chief Executive Officer,Defined.aiLead authorsWilliam CouperAI Enablement and Tech Transformation,KPMGSamira GazzaneAI Policy Lead,AI and Machine Learning,World Economic ForumJames HeatonAI Researcher,KPMGJoao Adams VieiraAI Lead,Switzerland,KPMGNichole WilliamsAI in
177、 Government,KPMGWorld Economic ForumConnie KuangLead,Generative AI and Metaverse Value CreationBenjamin Larsen Lead,AI and Machine LearningHannah Rosenfeld Specialist,AI and Machine LearningStephanie TeeuwenSpecialist,Data and AIKarla Yee AmezagaLead,Data Policy and AIHesham ZafarLead,Digital Trust
178、Francesca ZanollaLead,Strategic Integration,Artificial IntelligenceKPMGLeanne AllenHead of AI,Advisory,KPMG UKRegina MayorGlobal Head of Clients and MarketsChristy MitchellWorld Economic Forum LeadDavid RowlandsGlobal Head of AIBrenda WalkerGlobal Government Sector HeadThis paper is a combined effor
179、t based on numerous interviews,discussions,workshops and research.The opinions expressed herein do not necessarily reflect the views of the individuals or organizations involved in the project or listed below.Sincere thanks are extended to those who contributed to the drafting or review of the repor
180、t,as well as those not captured below.Blueprint for Intelligent Economies17Nick CainDirector,Strategic Grants;Head of Climate Portfolio,The Patrick J.McGovern FoundationFrincy ClementHead,North America Region,Women in AIShanna CrumleyDirector,Impact Data Science,Mastercard Centre for Inclusive Growt
181、hHeather DominGlobal Leader,Responsible AI Initiatives,IBMKatharina FreyDeputy Head,Digitalisation Division,Federal Department of Foreign Affairs(FDFA)of SwitzerlandOlaf GrothProfessional Faculty,Haas School of Business,University of CaliforniaAbdulrahman HabibDeputy Chief Strategy Officer,Saudi Dat
182、a and AI Authority(SDAIA)Pradipt KapoorChief Digital and Information Officer,Bharti Airtel;Chief Executive Officer,Xtelify Faisal KazimHead,Centre for the Fourth Industrial Revolution,United Arab EmiratesAshok Kumar PandaVice-President and Global Head,AI&Automation Services,InfosysRafi LazersonAssoc
183、iate Manager,Responsible AI,AccentureTze Yun LeongProfessor,Computer Science,National University of SingaporeHarrison LungGroup Chief Strategy Officer,e&Florian MuellerSenior Partner,Head of Artificial Intelligence,Europe,Middle East and Africa,Bain&CompanyLama NachmanFellow;Director,Human&AI System
184、s Research Lab,IntelAlain NdayishimiyeHead,AI,Centre for the Fourth Industrial Revolution RwandaAidan PeppinLead,Policy and Responsible AI,CohereGarima RathoreDirector,Government Affairs and Public Policy,Microsoft Corporation IndiaKelly RichdaleSenior Advisor,SandboxAQCrystal RugegeManaging Directo
185、r,Centre for the Fourth Industrial Revolution RwandaAli Schmidt-FellnerVice-President,Insights,Mastercard Centre for Inclusive GrowthNavin Shankar PatelAssociate Vice-President and Head,AI Academy,InfosysManal SiddiquiManager,Responsible AI,AccentureLandry SignSenior Fellow,Global Economy and Develo
186、pment Program,Brookings InstitutionProductionLaurence DenmarkCreative Director,Studio MikoXander HarperDesigner,Studio MikoWill LileyEditor,Studio MikoBlueprint for Intelligent Economies18Endnotes1.Constellation.(2024).Constellation to Launch Crane Clean Energy Center,Restoring Jobs and Carbon-Free
187、Power to the Grid.https:/ Bank Group.(2024).World Bank Group Launches Renewable Energy Initiative to Enhance Energy Security and Affordability in Europe and Central Asia.https:/www.worldbank.org/en/news/press-release/2024/03/28/world-bank-group-launches-renewable-energy-initiative-to-enhance-energy-
188、security-and-affordability-in-europe-and-central.3.Learn more in the Forums 2024 white paper,Artificial Intelligences Energy Paradox:Balancing Challenges and Opportunities.4.U.S.Economic Development Administration(EDA).(n.d.).New York SMART I-Corridor Tech Hub.https:/www.eda.gov/funding/programs/reg
189、ional-technology-and-innovation-hubs/2023/NY-SMART-I-Corridor-Tech-Hub.5.International Telecommunication Union(ITU).(2023).Statistics.https:/www.itu.int/en/ITU-D/Statistics/pages/stat/default.aspx.6.Global Digital Public Infrastructure Repository(GDPIR).(n.d.).UPI.https:/www.dpi.global/globaldpi/upi
190、.7.India Ministry of External Affairs.(2023).G20 New Delhi Leaders Declaration.https:/www.mea.gov.in/Images/CPV/G20-New-Delhi-Leaders-Declaration.pdf.8.Grand View Research.(2023).AI Infrastructure Market Size,Share&Trends Analysis Report By Component(Hardware,Software,Services),By Technology(Machine
191、 Learning,Deep Learning),By Application,By Deployment,By End-user,By Region,And Segment Forecasts,2024-2030.https:/ University.(2024).Artificial Intelligence Index Report 2024.https:/aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf.10.Ministry of Communications and Inform
192、ation Technology,State of Qatar.(2023).Qatar signs MoU in the field of Communication and Information technology with Rwanda.https:/www.mcit.gov.qa/en/media-center/news/qatar-signs-mou-field-communication-and-information-technology-rwanda.11.Digital For Life.(n.d.).Home.https:/www.digitalforlife.gov.
193、sg/.12.Fujitsu Limited.(2024).Release of“Fugaku-LLM”a large language model trained on the supercomputer“Fugaku”.https:/ K.Martineau.(2023).What is synthetic data?IBM.https:/ A.I.s Output Is a Threat to A.I.Itself.The New York Times.https:/ Economic Forum.(2024).Advancing Data Equity:An Action-Orient
194、ed Framework.https:/www.weforum.org/publications/advancing-data-equity-an-action-oriented-framework/.16.Cherney,M.A.(2023).UAEs G42 launches open source Arabic language AI model.Reuters.https:/ Primer The AI Language Gap.https:/ Announces Intent to Expand Cloud Regions through Saudi Arabia Datacente
195、r.https:/ Economic Forum.(n.d.).The Digital Trust Framework.https:/initiatives.weforum.org/digital-trust/framework.20.World Economic Forum.(2024).Governance in the Age of Generative AI:A 360 Approach for Resilient Policy and Regulation.https:/www.weforum.org/publications/governance-in-the-age-of-gen
196、erative-ai/.21.World Economic Forum.(2023).Data Free Flow with Trust:Overcoming Barriers to Cross-Border Data Flows.https:/www.weforum.org/publications/data-free-flow-with-trust-overcoming-barriers-to-cross-border-data-flows/.22.United Nations Educational,Scientific and Cultural Organization(UNESCO)
197、.(2023).Recommendation on the Ethics of Artificial Intelligence.https:/www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence.23.Paule,L.G.(2024).UNESCO launches Global AI Ethics and Governance Observatory at the 2024 Global Forum on the Ethics of Artificial Intelligence.EU Digital
198、 Skills&Jobs Platform.https:/digital-skills-jobs.europa.eu/en/latest/news/unesco-launches-global-ai-ethics-and-governance-observatory-2024-global-forum-ethics.24.Brown,P.T.,D.Wilson,K.West,K.Escott,et al.(2024).Mori Algorithmic Sovereignty:Idea,Principles,and Use.Data Science Journal,vol.23,pp.1-16.
199、https:/datascience.codata.org/articles/1639/files/660bde3010db2.pdf.25.Council of Europe.(2024).Council of Europe Treaty Series No.225:Council of Europe Framework Convention on Artificial Intelligence and Human Rights,Democracy and the Rule of Law.https:/rm.coe.int/1680afae3c.26.United Nations.(2024
200、).Governing AI For Humanity.https:/www.un.org/sites/un2.un.org/files/governing_ai_for_humanity_final_report_en.pdf.Blueprint for Intelligent Economies1927.Paule,L.G.(2024).UNESCO launches Global AI Ethics and Governance Observatory at the 2024 Global Forum on the Ethics of Artificial Intelligence.EU
201、 Digital Skills&Jobs Platform.https:/digital-skills-jobs.europa.eu/en/latest/news/unesco-launches-global-ai-ethics-and-governance-observatory-2024-global-forum-ethics.28.United Nations Educational,Scientific and Cultural Organization(UNESCO).(n.d.).Readiness Assessment Methodology.https:/www.unesco.
202、org/ethics-ai/en/ram.29.Ministry of Foreign Affairs of Japan.(2023).Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems.https:/www.mofa.go.jp/files/100573473.pdf.30.OECD.AI.(2023).Catalogue of Tools&Metrics for Trustworthy AI:Framework for Ethical AI Gove
203、rnance.https:/oecd.ai/en/catalogue/tools/framework-for-ethical-ai-governance.31.Microsoft.(n.d.).Responsible AI:Principles and approach.https:/ Principles.https:/ai.google/responsibility/principles/.33.Salesforce.(n.d.).Ethical&Humane Use of Technology:Intentional Innovation.https:/ Artificial Intel
204、ligence Act.(n.d.).Home.https:/artificialintelligenceact.eu/.35.UK Government.(2024).Notice:Collaboration on the safety of AI:UK-US memorandum of understanding.https:/www.gov.uk/government/publications/collaboration-on-the-safety-of-ai-uk-us-memorandum-of-understanding/collaboration-on-the-safety-of
205、-ai-uk-us-memorandum-of-understanding#:text=In%20November%202023%2C%20the%20UK,and%20use%20of%20advanced%20AI%20.36.National Institute of Standards and Technology(NIST).(2024).Artificial Intelligence Risk Management Framework:Generative Artificial Intelligence Profile.https:/nvlpubs.nist.gov/nistpub
206、s/ai/NIST.AI.600-1.pdf.37.United Nations Office for Disarmament Affairs(UNODA).(n.d.).Treaty on the Non-Proliferation of Nuclear Weapons(NPT).https:/disarmament.unoda.org/wmd/nuclear/npt/#:text=The%20NPT%20is%20a%20landmark,and%20general%20and%20complete%20disarmament.38.World Economic Forum.(2024).
207、Governance in the Age of Generative AI:A 360 Approach for Resilient Policy and Regulation.https:/www.weforum.org/publications/governance-in-the-age-of-generative-ai/.39.European Union.(2019).Directive(EU)2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related
208、 rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC.https:/eur-lex.europa.eu/eli/dir/2019/790/oj.40.Agency for Cultural Affairs,Government of Japan.(2024).General Understanding on AI and Copyright in Japan.https:/www.bunka.go.jp/english/policy/copyright/pdf/94055801_0
209、1.pdf.41.World Intellectual Property Organization(WIPO).(n.d.).Home.https:/www.wipo.int/portal/en/index.html.20Blueprint for Intelligent EconomiesWorld Economic Forum9193 route de la CapiteCH-1223 Cologny/GenevaSwitzerland Tel.:+41(0)22 869 1212Fax:+41(0)22 786 2744contactweforum.orgwww.weforum.orgThe World Economic Forum,committed to improving the state of the world,is the International Organization for Public-Private Cooperation.The Forum engages the foremost political,business and other leaders of society to shape global,regional and industry agendas.