A framework for advancing data equity in a digital world

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.