Advancing Data Equity in Urban Planning: Balancing AI Innovation with Community Needs

How equitable data practices can help shape urban planning

The future of equitable and sustainable cities could be accelerated by advancing data equity in practice when it comes to urban planning. Digital technologies have become ubiquitous in today's urban environments. Facial recognition systems are used at sports and entertainment venues and public events. Vehicle mounted, camera-based artificial intelligence systems count potholes and signs of homelessness. Licence plate recognition systems enforce traffic rules and charging tolls. As AI systems are increasingly part of the new urban infrastructure that cities use to provide services and operate shared spaces, it is critical that these applications are considered and reflected in broader conversations about the benefits and potential harms of this technology and the need for regulation. These new forms of digitalized urban infrastructure are critical to society's ability to achieve sustainability and climate goals - for example, helping manage energy demand to reduce greenhouse gas emissions, or using cameras to regulate and manage vehicle traffic to minimize congestion and improve air quality. At the same time, these technologies raise pressing issues and considerations in their application to ensure that the digital age enables a vision of shared prosperity. These technologies have transformed what were once static spaces, roads and plazas into dynamic AI systems that make decisions affecting all of us. We live in an era where automated decision-making systems based on algorithms and data are no longer limited to software that runs on our computers and screens, but into the built environment as infrastructure. History has shown that the way places are built has the potential to divide, marginalize and exploit people in their communities. The permanence of built infrastructure - like a highway, park, or school - makes it tangible to the people it has impacted. As a result of this prior history, people and communities now demand and expect participation in the decisions that shape their urban environments. Massive infrastructures are visible and, as such, affected communities can respond to, adapt and co-opt them to mitigate their negative effects. For example, it's possible for people to see and discuss the impacts of a highway or a major road running through their neighbourhood; but much harder to realize that there is an algorithm that adjusts the timing of traffic lights to prioritize the speed of cars over how long people need to wait to cross the street. Digitalized infrastructure is different. It is often invisible. Data-collecting technologies deployed in the public realm are often installed to be as invisible as possible. This invisibility hides the impact digital infrastructure has on communities - the decisions that are made with the data collected and how it's processed, the changes to policy made by changes in code, and the deployment of AI models and algorithms. A security camera in a corner of a physical space could be reviewed and used only when there is a reported incident; or, it could be providing data to a computer vision system using facial recognition to identify specific individuals. Unlike large physical infrastructure, the impacts of digital infrastructure are hidden, and ever-evolving. As the world is increasingly data driven, it is critical to recognize that digitalized forms of infrastructure have the potential to perpetuate existing, inequitable power dynamics through issues such as infringing on privacy, automating decisions and perpetuating sources of bias. Even if a technology's designers are careful to account for these risks in the design of these technologies, inequities can persist through decisions of where, and when, those technologies are utilized. Every technology can be used to benefit cities, and the same technology has the potential to cause harm to the communities we seek to benefit. The Global Future Council on Data Equity has defined and created a "framework of inquiry" to foster action in addressing historical and current imbalances in the use and access of datasets in various domains, and for decision-making in algorithmic and AI systems and their societal impact. Data equity is defined as the shared responsibility for fair data practices that respect and promote human rights, opportunity and dignity - and these concepts are applicable and extensible to digitalized forms of urban infrastructure. Just like it's clear people have a right to participate in the design of these digital layers and urban technologies, it's important to actively involve communities in the deployment and governance of new technologies so that potential benefits and tradeoffs can be discussed. Most processes for public transparency and consent for technology use are centered on consumer technologies and utilize individual "opt-in" processes. However, this individualistic approach is not well suited for urban technologies that impact diverse groups over large geographies and long time-scales. The action oriented Advancing Data Equity framework offers a way to get started with addressing these challenges. Designed to be flexible and a starting point for inquiry, the data equity considerations in the framework are intended to be applicable to a broad range of issues and industries. Just as equity considerations can be brought into the design and development of public spaces and streets, so can data equity for urban technology. In their 2023 Trend Report, the American Planning Association suggested that with the increasing digitalization of public spaces, planners should start incorporating and thinking about technology and how it is being used. Ethical, democratic and effective use of these AI-enabled smart city technologies requires foresight and planning. Proactive actions that can be taken to advance data equity in urban technology have many parallels with practices in urban planning. Here's how: Planners and urban innovators, with processes and expertise in bringing multiple stakeholders together to align on place-based outcomes, are well positioned to advance data equity in practice. They should collaborate with actors designing, deploying and regulating these technologies to ensure that equity considerations are taken into account, and to ensure the inclusion of communities and key stakeholder groups. Opportunities exist for local governments to enable foresight and planning for technology in our cities and public spaces, through existing land use and development planning processes, and tools such as zoning bylaws and design review panels. It's about recognizing parallels in how equity is considered across the many stages of planning and urban development with how data equity is addressed in similar stages in the data lifecycle - and using these considerations as a starting point for inquiry, collaboration and shared learning. In this way, the future of equitable, sustainable cities could be accelerated by bringing the design and deployment of digital systems closer together with the practice of urban planning.

A framework for advancing data equity in a digital world

The Global Future Council on the Future of Data Equity has developed a framework to encourage reflection, guide research and prompt corrective actions for responsible data practices. Data equity is a shared responsibility that requires collective action to create data practices and systems that promote fair and just outcomes for all. Continuously considering the human impact of data is crucial given the ever-expanding role of data-driven systems in today's digital societies. As technologies like machine learning and generative artificial intelligence (AI) further evolve, data equity issues may be exacerbated if not addressed from the foundational stages. By assessing data equity throughout the data lifecycle, practices can be improved to promote fair, just and beneficial outcomes for all individuals, groups and communities. We live in a digital society that leverages algorithmically automated, data-based systems increasingly used for decision-making. But our data-driven world was not designed in a manner that drives equitable outcomes, simply because it was not designed with equity in mind. Instead, it was created with all our societal varieties, historical inequities, biases and differences. Which is why data equity is essential in this AI-driven digital age, to recognize the material impact of these systems on people and society, and on their ability to exercise their human rights. By incorporating data equity into all design, deployment and use efforts, we ensure that the voices of diverse communities are represented, thus embedding trust in the application of technologies within society as our digital landscape continues to evolve. But first and foremost, there must be a harmonized definition of data equity that fosters alignment and collaboration to put this concept into practice and helps measure tangible progress in this area. And so the World Economic Forum's Global Future Council on the Future of Data Equity, a multistakeholder expert group, through various rounds of research and consultations, put together a definition that encompasses the comprehensive nature of this concept and how it impacts all sectors, industries and regions across the data lifecycle. According to the Council, "data equity is 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, participative, inclusive and proactive collective and coordinated action to create a world where data-based systems promote fair, just and beneficial outcomes for all individuals, groups and communities. It recognizes that data practices - including collection, curation, processing, retention, analysis, stewardship and responsible application of resulting insights - significantly impact human rights and the resulting access to social, economic, natural and cultural resources and opportunities." In order to move from a theoretical definition towards actionable impact, the Global Future Council on Data Equity developed a framework to encourage reflection, guide research and prompt corrective actions for responsible data practices in different contexts, while ensuring consistency and compliance with global regulations. This framework is designed to be a crucial foundation for transforming data practices to fully embrace inclusivity and fairness. It is meant to be used as a framework of inquiry, a guide to help spur conversation and evaluation inside organizations as they seek to use AI more broadly. It takes inspiration from existing data principles, like FAIR, CARE and TRUST and is rooted in Maori Indigenous data sovereignty, specifically the Te Mana o te Raraunga Model, that describes the internal logic that traditional knowledge-keepers use when deciding to share knowledge with others. The clear focus on equity and its inspiration derived from an indigenous Maori data model makes this framework unique among other existing data standards. The framework consists of three categories: data, people and purpose, and 10 corresponding characteristics with key issues and inquiry questions. In addition to inquiry questions, the framework also includes suggested actions to consider, depending on the specific context and stakeholders involved. The intention of the framework is to surface issues and identify possible actions that address the issues, to forge a more equitable world. Practical use cases can demonstrate the utility of the data equity framework. For example, data equity considerations in climate data collection and monitoring can lead to more robust climate data for more effective mitigation strategies. Currently, significant gaps exist in climate data collection, especially in rural and remote areas in the Global South. Following data equity practices, such as investing in data collection and (community) capacity-building, can improve the granularity of climate data and in turn enable and incentivize more effective climate monitoring. Data equity transcends technical processes and is fundamentally about the impact of data on people and communities; its foundation is about the shared responsibility for fair data practices that respect and promote human rights, opportunity and dignity. Thus, as technical capabilities advance, it is imperative that the awareness of their social implications does too. The Global Future Council on Data Equity is dedicated to forging a future where cutting-edge technologies empower all, and to ensuring that fairness and inclusivity drive both technological advancements and their real-world applications. In the pursuit of a more equitable world, the proposed data equity definition and framework seek to serve not merely as a set of guidelines but as dynamic tools, urging all stakeholders across sectors involved in the realms of data and technology to prioritize and operationalize equity at every stage of their work. By achieving this, the aim is to ensure that the era of digital transformation is characterized not only by technological breakthroughs, but also by significant social advancements.