js/data/areas.js

// areas.js - Centralized areas data - SINGLE SOURCE OF TRUTH
// All content, titles, navigation labels, and section IDs are defined here
export const areasData = {
    sustainability: {
        id: 'sustainability',
        title: 'Sustainability',
        navTitle: 'Sustainability',  // Short title for navigation
        description: {
            short: 'More sustainable AI environmentally and economically is essential to managing its impacts, open development supports reliable measuring and more efficient methods.',
            paragraphs: [
                'Reaching a better understanding of AI’s sustainability is essential to managing the impacts of AI technologies; it shapes who gets to develop AI technologies, use them, and how their externalized costs are borne by people who do not choose or benefit from the technology.',
                'Developers of open models typically have stronger incentives to favor and invest in efficiency, which in terms helps drive more responsible and informed usage of the technologies created. Relatedly, the open development of AI systems also greatly facilitates transparency regarding the costs of training and developing them and open-sourcing models contributes towards reducing wasted deployment costs, since models can be reused and adapted instead of being trained from scratch.',
            ],
        },
        color: 'bg-green-100 text-green-800',
        primaryColor: 'green',
        colors: {
            light: 'bg-green-50 text-green-700',
            medium: 'bg-green-100 text-green-800', 
            dark: 'bg-green-200 text-green-900',
            gradient: 'from-green-50 to-green-100 hover:from-green-100 hover:to-green-200 border-green-200 hover:border-green-300 text-green-900'
        },
        image: 'efficiency.png',
        imageAttribution: 'Hanna Barakat  & Archival Images of AI + AIxDESIGN | BetterImagesOfAI, CC-BY-4.0',
        imageAltText: 'The image shows a surreal landscape with vast green fields extending toward distant mountains under a cloudy sky. Embedded in the fields are digital circuit patterns, resembling an intricate network of blue lines, representing a technological infrastructure. Five large computer monitors with keyboards are placed in a row, each with a Navajo woman sitting in front, weaving the computers. In the far distance, a cluster of teepees is visible.',
        imageSourceUrl: 'https://betterimagesofai.org/images?artist=HannaBarakat&title=WeavingWires2',
        topics: {
            measuring: {
                id: 'measuring',
                name: 'Measuring and standardising costs and impacts',
                description: {
                    short: 'Addressing the environmental and financial costs of AI systems start with reliable and trustworthy measuring and reporting.',
                    paragraphs: [
                        'Systematically evaluating the impacts of deployed AI systems and developing methodologies and standards to compare different systems is a key component of getting more transparency on their energy and financial costs. Open-sourcing systems supports this evaluation by allowing researchers to access open models, training and fine-tuning data, and code.',
                        'Carrying out research to improve the state-of-the-art in terms of evaluation science and disclosure requirements at different levels of granularity – from individual developers to industry organizations, as well as entire countries – can help pave the way towards more standardized evaluation approaches. Building links between AI developers, international standards organizations and policymakers is important to ensure the cohesion between disclosure requirements and technical standards.',
                    ],
                },
                color: 'bg-lime-100 text-lime-800',
                gradient: 'from-lime-50 to-lime-100 hover:from-lime-100 hover:to-lime-200 border-lime-200 hover:border-lime-300 text-lime-900'
            },
            efficiency: {
                id: 'efficiency',
                name: 'Making AI less compute-intensive',
                description: {
                    short: 'Efforts to reduce the compute-intensive nature of AI systems, and ways to make them more compute-efficient.',
                    paragraphs: [
                        'Approaches to improving AI systems’ efficiency include efforts to reduce the compute-intensive nature of AI systems (specifically large language models), and ways to make both their development and usage more compute-efficient. While the true monetary and energy costs of proprietary AI models are rarely made available to users and developers, their increased usage in existing and new systems (e.g. Web search, AI agents, etc.)  makes understanding these important. Optimization approaches such as distillation and quantization can help make models more efficient, but they can only be applied if the models themselves are accessible. This means that adopters of open models have stronger incentives to favor and invest in efficiency, and access to fully open models supports the development of more efficient models and training and inference techniques. ',
                        'While the hardware used to train and deploy models (GPUs and TPUs) has become increasingly compute-efficient, it is still unclear whether this is outpaced by increased usage or not. Given that the current dynamics that incentivize an over-usage of compute-intensive AI models (prioritizing convenience and market capture over using the right AI tool for the right task), it is still unclear to what extent hardware efficiency gains are lost due to their increased usage.',
                    ],
                },
                color: 'bg-teal-100 text-teal-800',
                gradient: 'from-teal-50 to-teal-100 hover:from-teal-100 hover:to-teal-200 border-teal-200 hover:border-teal-300 text-teal-900'
            },
        },
        imagePosition: 'left'
    },
    agency: {
        id: 'agency',
        title: 'Personal and Community Agency',
        navTitle: 'Agency',  // Short name for navigation
        description: {
            short: 'How AI systems affect our personal and collective experiences and how we can in turn shape them.',
            paragraphs: [
                'AI systems mediate how we express ourselves, form relationships, and act within digital environments. From personal interactions to collective practices, consent, privacy and agency define how people engage with and shape AI systems. They determine how individuals navigate attachment, how their data and identities shape them, and how communities organize to reclaim influence over the systems that affect them.',
                'Openness of models, data, and decision-making processes plays a strong role in enabling these forms of agency. It enables individuals and groups to understand how AI affects them, but also to act on that understanding – by choosing how to participate, auditing and adapting technologies, and helping define the norms and safeguards that govern them. Active participation and informed consent turn openness from a principle into a practice, ensuring that users, researchers, and communities have the capacity to steer AI development toward their own needs and values.',
            ],
        },
        color: 'bg-purple-100 text-purple-800',
        primaryColor: 'purple',
        colors: {
            light: 'bg-purple-50 text-purple-700',
            medium: 'bg-purple-100 text-purple-800',
            dark: 'bg-purple-200 text-purple-900',
            gradient: 'from-purple-50 to-purple-100 hover:from-purple-100 hover:to-purple-200 border-purple-200 hover:border-purple-300 text-purple-900'
        },
        image: 'personal.png',
        imageAttribution: 'Kathryn Conrad | BetterImagesOfAI, CC-BY-4.0',
        imageAltText: 'Students at computers with screens that include a representation of a retinal scanner with pixelation and binary data overlays and a brightly coloured datawave heatmap at the top.',
        imageSourceUrl: 'https://betterimagesofai.org/images?artist=KathrynConrad&title=Datafication',
        topics: {
            personal: {
                id: 'personal',
                name: 'Personal Agency and Interactions',
                description: {
                    short: 'Characterizing how AI system design, embedded values, and data flows shape personal experiences.',
                    paragraphs: [
                        'AI systems increasingly participate in people’s daily lives, from conversational agents to creative tools. They mediate emotional expression, companionship, and value formation, and they also shape which forms of identity and speech are amplified or suppressed. The same systems that foster companionship or creativity can also reproduce disparities in performance or moderation with users whose identities, languages, or cultures differ most from those of their developers. Questions of privacy are also central to understanding how data about our lives and activities inform these systems and the decisions they make about us, and keeping control of our digital identities.',
                        'Studying these interactions shows how people experience agency, trust, and influence within AI-mediated environments; and allows us to build tools and shape development in ways that reflect broader needs and interests. Openness and transparency around data and decision-making enable individuals and communities to act: to audit behaviors, retrace decisions, and adapt systems to their own cultural and emotional contexts. Moreover, integrating privacy and consent at the design stage allows non-technical actors to participate in co-design, provide feedback, or govern usage norms, transforming the understanding of AI experiences into shared capacity to shape them.',
                    ],
                },
                color: 'bg-fuchsia-100 text-fuchsia-800',
                gradient: 'from-fuchsia-50 to-fuchsia-100 hover:from-fuchsia-100 hover:to-fuchsia-200 border-fuchsia-200 hover:border-fuchsia-300 text-fuchsia-900'
            },
            community: {
                id: 'community',
                name: 'Collective Agency and Community Governance',
                description: {
                    short: 'How AI systems shape collective practices and influence who can participate in digital spaces.',
                    paragraphs: [
                        'We interact with and are shaped by AI systems not just as individuals but also as members of communities who have different relationships to the technology. As both technical artifacts and social infrastructures, they shape collective practices, redistribute power, and influence who can participate in digital spaces. ',
                        'Transparent, collaborative, and affordable technical systems can support approaches to direct community governance, shaping how decisions about datasets, design, and deployment are made; transparency and open access to support investigation by and for communities also help them assert influence beyond formal participation. Reporting or documenting harms and biases that affect them, developing decentralized or community-moderated alternatives to centralized systems, organizing around the implementation of AI in local or online spaces, or mobilizing against uses that threaten their rights, privacy, and values all have a strong role to play.',
                        'Open access to the inner working and intermediary design decisions of AI systems make this governance meaningful, enabling communities to deliberate on system goals, share oversight, and embed accountability and pluralism into AI development. Embedding consent and privacy norms into collective governance ensures that communities actively interpret, contest, and rebuild technology, shaping the field through both critique and creation.',
                    ],
                },
                color: 'bg-violet-100 text-violet-800',
                gradient: 'from-violet-50 to-violet-100 hover:from-violet-100 hover:to-violet-200 border-violet-200 hover:border-violet-300 text-violet-900'
            },
        },
        imagePosition: 'right'
    },
    ecosystems: {
        id: 'ecosystems',
        title: 'Ecosystems',
        navTitle: 'Ecosystems',  // Short title for navigation
        description: {
            short: 'AI systems are embedded in economic, regulatory, and market ecosystems that shape and are shaped by their development.',
            paragraphs: [
                'Understanding AI systems requires understanding the economic, regulatory, and market ecosystems that shape and are shaped by its development. These ecosystems  determine the effectiveness of different approaches to the development, governance, and commercialization of the technology –  and the most effective strategies to ensure positive outcomes for stakeholders both in and outside of its development settings.',
                'More open and transparent technology enables both a better study of the interactions between these ecosystems and the development of tools and versions of the technologies that can better avoid pitfalls of labor and economic displacement, excessive concentration of resources and market power, or of regulation under strong epistemic asymmetries between policymakers and large developers. In particular, open research and development enables more direct collaboration between diverse developer profiles, legislators, adopters, advocates, and other economic actors – with less dependence on access and information provided by large model developers.',
            ],
        },
        color: 'bg-orange-100 text-orange-800',
        primaryColor: 'orange',
        colors: {
            light: 'bg-orange-50 text-orange-700',
            medium: 'bg-orange-100 text-orange-800',
            dark: 'bg-orange-200 text-orange-900',
            gradient: 'from-orange-50 to-orange-100 hover:from-orange-100 hover:to-orange-200 border-orange-200 hover:border-orange-300 text-orange-900'
        },
        image: 'ecosystems.png',
        imageAttribution: 'Lone Thomasky & Bits&Bäume | BetterImagesOfAI, CC-BY-4.0',
        imageAltText: 'A simplified illustration of urban life near the sea showing groups of people, buildings and bridges, as well as polluting power plants, opencast mining, exploitative work, data centres and wind power stations on a hill. Several small icons indicate destructive processes.',
        imageSourceUrl: 'https://betterimagesofai.org/images?artist=LoneThomasky&title=DigitalSocietyBell',
        topics: {
            economy: {
                id: 'economy',
                name: 'Economic and Labor Impacts of AI',
                description: {
                    short: 'How AI systems affect the economy and labor conditions.',
                    paragraphs: [
                        'AI is transforming work across industries from logistics and finance to media, software development, and customer service. As models are integrated into larger commercial systems, they increasingly shape how tasks are designed, monitored, and valued. The centralization of data and the ensuing integration of automated information processing, pattern recognition, and content generation tools is changing how digital and creative work is supported, organized, and distributed, and who benefits from it. These changes redefine labor and economic sectors not just through job displacement, but through the redesign of entire production systems.',
                        'Understanding where AI systems can support workers and economic outcomes, how different kinds of deployments may favor or harm different parties, and how control of work data flows can either consolidate or displace value creation for adopters of AI systems all contribute to making sure that they are developed and deployed to benefit the workforce and industry sectors that leverage the technology, not just its developers.',
                        'Openness and transparency into the models and datasets that support commercial applications of AI, particularly in “enterprise” settings and for specific domains of activities, supports more positive outcomes and broadly distributed benefits in two major ways. First, by enabling the development of new AI systems to be driven more directly by the economic actors who want to leverage them, maintaining control of their supply chain, expertise, and value propositions. Second, by enabling more scrutiny into the systems themselves, and supporting more robust and independent analysis of the labor and economic impacts of the technology – rather than relying on the framings and promises of developers – to better guide economic policy and strategy.',
                    ],
                },
                color: 'bg-yellow-100 text-yellow-800',
                gradient: 'from-yellow-50 to-yellow-100 hover:from-yellow-100 hover:to-yellow-200 border-yellow-200 hover:border-yellow-300 text-yellow-900'
            },
            power: {
                id: 'power',
                name: 'De-Centralized Markets, Development, and Sovereignty',
                description: {
                    short: 'Market concentration dynamics and technological sovereignty questions.',
                    paragraphs: [
                        'The narratives and development of AI today are disproportionately shaped by a handful of actors who control the largest models, datasets, and compute infrastructure. This concentration of technical and financial power doesn’t just shape (and constrain) innovation – it defines which versions of the technology are given priority, who sets its norms, what values it encodes, and who benefits most from its integration into all aspects of society. As these dependencies deepen, they also raise questions of digital and technological sovereignty for nations and collectivities aiming to set their own terms for their digital infrastructures.',
                        'A more balanced and resilient AI ecosystem depends on distributing the capacity to develop, study, and govern these systems. Maintaining this capacity to develop alternative versions of the technology beyond that of a few actors concentrating the majority of the computational and data resources requires a wide range of strategies, from checking abuses of market power to ensure broad participation in AI development remains incentivized and sustainable to lowering the technical and financial barrier to entry for all categories of actors; benefiting both small start-ups and public institutions and larger economic organizations resources who still may not want to reach the excesses of compute expenses of the largest developers.',
                        'Fostering a broad ecosystem of open AI models, datasets, and tools and a thriving open research environment across universities and independent developers has a dual role to play in mitigating the risks of extreme concentration. First, it enables a better understanding into the tradeoffs involved in the development of the technology, including specifically in characterizing the role of different categories of resources and the risks their capture may pose to a competitive ecosystem. Second, it drastically reduces the cost of developing new AI systems, or of adapting existing AI technology to the needs of various actors; and allows them to control their data flows to ensure their longer-term welfare.',
                    ],
                },
                color: 'bg-red-100 text-red-800',
                gradient: 'from-red-50 to-red-100 hover:from-red-100 hover:to-red-200 border-red-200 hover:border-red-300 text-red-900'
            },
            regulation: {
                id: 'regulation',
                name: 'Rights and Regulation',
                description: {
                    short: 'How AI systems are regulated and how they affect rights and regulations.',
                    paragraphs: [
                        'Regulation of AI systems, both through the design of new technology-specific rules of through the exploration of how existing laws apply to the new technical paradigms it introduces, has drawn increasing attention commensurate with the ubiquity and visibility of the technology. Notably, the unprecedented scales of data and automation can present unique challenges, and different interests and perspectives on the technology from different categories of stakeholder have raised important questions about which risks to prioritize in legislative actions, and how to arbitrate between different tradeoffs – including when considering how to apply proposed rules to open and open-source systems, which often receive significantly less consideration in drafting processes than diversity of development contexts and developer and researcher profiles who participate in it would warrant.',
                        'Effective AI regulation requires a deep understanding of the technology\'s inner workings, including its inherent trade-offs and the feasibility of technical interventions. Open access to AI systems is crucial for this, as it empowers independent legal and domain experts to conduct research and assess the technology without relying solely on the interpretations of a few powerful developers. Furthermore, open research and centralized resources for legal compliance lower barriers to participation, which is vital for the often less-resourced organizations that produce much of the most publicly beneficial work. This openness extends beyond code to include open datasets, models, and transparent decision-making, enabling a broader community to help shape AI. Overall, collaboration on the technical artifacts and legal tools that shape the design and governance of artificial intelligence is essential to ensure the sustainability of regulatory efforts that serve all of the people whose lives it shapes.',
                    ],
                },
                color: 'bg-purple-100 text-purple-800',
                gradient: 'from-purple-50 to-purple-100 hover:from-purple-100 hover:to-purple-200 border-purple-200 hover:border-purple-300 text-purple-900'
            }
        },
        imagePosition: 'right'
    }
};

export const homeBackgroundImage = {
    image: 'ai.png',
    attribution: 'Jamillah Knowles & Digit | BetterImagesOfAI, CC-BY-4.0',
    altText: 'The image is of the exterior of an impression of a building. People and figures can be seen inside and outside of the building. There are clouds of network connections all around the building and inside. It relates to the digital networked workplace.',
    sourceUrl: 'https://betterimagesofai.org/images?artist=JamillahKnowles&title=BuildingCorp'
};

export const overallBackgroundImage = {
    image: 'background_ai.png',
    attribution: 'Jamillah Knowles & Digit | BetterImagesOfAI, CC-BY-4.0',
    altText: 'A pink and yellow abstract image of an office with people working, chatting and walking around. Above their heads are clouds of network connections.  It was painted with guache and drawn with pencils.',
    sourceUrl: 'https://betterimagesofai.org/images?artist=JamillahKnowles&title=PinkOffice'
};

// Helper function to generate navigation structure from areasData
// This is the SINGLE SOURCE OF TRUTH for all navigation
export function getNavigationData() {
    return Object.values(areasData).map(area => ({
        id: area.id,
        navTitle: area.navTitle,
        title: area.title,
        topics: Object.values(area.topics).map(topic => ({
            id: topic.id,
            navName: topic.navName || topic.name,
            name: topic.name
        }))
    }));
}

// Get navigation structure for a specific area
export function getAreaNavigation(areaId) {
    const area = areasData[areaId];
    if (!area) return null;
    
    return {
        id: area.id,
        navTitle: area.navTitle,
        title: area.title,
        topics: Object.values(area.topics).map(topic => ({
            id: topic.id,
            navName: topic.navName || topic.name,
            name: topic.name
        }))
    };
}