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: [
                'AI systems are shaped by economic, regulatory, and market ecosystems – as these in turn are shaped by the technology. Understanding these interactions, including through the analysis enabled by more transparent systems and the collaborative experimentation supported by open models and datasets, fosters more effective development, governance, and commercialization of the technology to ensure more 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: {
            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; raising questions of digital and technological sovereignty for nations and communities aiming to set their own terms for their digital infrastructures.',
                        'A more resilient path forward requires cultivating a distributed ecosystem — one that checks abuses of market power and invests deliberately in open infrastructure. Fostering open AI models, datasets, tools, and a thriving research environment plays a dual role in mitigating concentration: first, by deepening understanding of the tradeoffs in development — including how resource capture threatens competition; second, by lowering barriers to entry, enabling actors of all sizes — from individuals to institutions — to adapt and secure their AI systems without replicating the computational excesses of the largest developers. Together, these efforts enable a more sustainable ecosystem, where adopters retain control over their data and the value of their work, and can drive innovation aligned with diverse needs.',
                    ],
                },
                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'
            },
            economy: {
                id: 'economy',
                name: 'Economic and Labor Impacts of AI',
                description: {
                    short: 'How AI systems affect the economy and labor conditions.',
                    paragraphs: [
                        'AI is reshaping work across industries, from logistics and finance to media and customer service, by embedding automated processing, pattern recognition, and content generation into commercial systems – along with new standards for efficiency, output, and oversight – often without input from those who rely on these systems daily. These changes reconfigure not just job roles but entire production structures, redefining how work is organized; its quality, autonomy, and safety; and where economic value accumulates.',
                        'Open models and transparent data practices empower a broader range of economic actors — including small businesses and specialists within economic domains — to adapt AI to their operational contexts, maintain control over their supply chain, expertise, and value propositions, and conduct independent analysis of its economic and labor impacts. Because those who deploy and live with these systems daily understand their effects — including unintended consequences, workflow disruptions, and hidden costs — better than centralized developers pushing top-down adoption, this openness enables workers and participants in economic production to shape AI to their needs, rather than conforming to standardized, one-size-fits-all solutions. Transparency doesn’t just enable scrutiny; it shifts the basis of economic policy, investment strategies, and AI product development from promotional claims to observable, shared realities.',
                    ],
                },
                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'
            },
            regulation: {
                id: 'regulation',
                name: 'Rights and Regulation',
                description: {
                    short: 'How AI systems are regulated and how they affect rights and regulations.',
                    paragraphs: [
                        'The regulation of AI increasingly grapples with how to apply existing legal frameworks – and design new ones – to systems whose scale, opacity, and dynamism present unprecedented challenges. Effective tools and processes for regulation require direct engagement with the technical characteristics of AI systems; an engagement uniquely enabled by open research and development. This in turn means that compliance requirements must account for the diverse needs of open and collaborative work across smaller institutions to avoid excluding all but the largest commercial actors from meaningfully participating in the technology’s development – undermining the very research needed to sustain meaningful governance.',
                        'By collaborating on tools and norms that operationalize rights and regulations in open settings – allowing direct collaboration with legal and social expertise and rightsholders, providing transparency that allows questioning practices earlier, and facilitating the development of more rights-respecting versions of the technology – open developers and researchers enable continuous alignment between technological evolution and societal expectations. These practices, rooted in the open-source and open science ethos, facilitate integrating foresight and building with more regulatory security for especially small and medium developers; resulting in a system where innovation and accountability are mutually reinforcing, not separate domains.',
                    ],
                },
                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
        }))
    };
}