Data has increasingly become a condition for participation in modern society. Access to financial services, healthcare, education, employment, transportation, and even political recognition is now frequently mediated through digital systems that rely on data collection, verification, and classification. Governments use administrative databases to determine eligibility for public services, while corporations depend on behavioral data to assess consumers, workers, and users.
In many discussions about digital transformation, inclusion is presented as a positive and inevitable outcome of technological progress. Expanding connectivity, digital identity systems, and data infrastructures are often framed as pathways toward greater efficiency and social participation.
Yet inclusion in contemporary digital society is rarely unconditional.
Participation increasingly depends on the ability to produce acceptable forms of data, maintain institutional visibility, and conform to the categories established by digital systems. Individuals who cannot generate sufficient data, who fail verification systems, or whose lives do not fit standardized classifications may experience exclusion even while formally existing within society.
In this context, inclusion becomes conditional through data.
Data as a Requirement for Recognition
Modern institutions increasingly depend on data to recognize individuals.
Governments require identification numbers, biometric verification, financial histories, and administrative records to determine access to services. Financial institutions evaluate individuals through credit scores, transaction histories, and risk assessments. Digital platforms personalize interaction and visibility through behavioral data continuously generated by users.
To participate fully in institutional life increasingly means to become legible within data systems.
James C. Scott (1998), in Seeing Like a State, explains that modern governance relies on simplifying and standardizing populations in order to make them administratively manageable. Through categories, measurements, and records, institutions transform complex human realities into forms that can be processed bureaucratically.
This process creates a significant shift in the meaning of inclusion.
Recognition no longer depends solely on legal status or social membership. It increasingly depends on compatibility with digital infrastructures that determine whether individuals can be identified, categorized, and verified.
Inclusion therefore becomes tied to data visibility.
The Expansion of Conditional Access
Digital systems increasingly mediate access to essential services.
Opening a bank account may require digital identity verification. Applying for employment may depend on algorithmic screening systems. Accessing government assistance may involve automated eligibility assessments. Even mobility within cities can depend on smartphone applications and digital payment infrastructures.
These systems often promise greater efficiency and accessibility.
However, they also establish conditions for participation that are unevenly distributed across society.
Individuals with stable internet access, formal documentation, digital literacy, and consistent institutional records are more likely to navigate digital systems successfully. Meanwhile, populations operating within informal economies, rural regions, unstable employment conditions, or undocumented circumstances may struggle to meet the requirements embedded within these infrastructures.
According to the World Bank’s Identification for Development initiative (2021), hundreds of millions of people globally still lack formal identification systems necessary for accessing financial and public services.
Inclusion therefore becomes dependent upon the ability to satisfy institutional data requirements.
Data, Classification, and Institutional Categories
Digital systems function through classification.
Algorithms and databases organize individuals into categories that influence how they are evaluated and treated. These classifications may include categories related to financial risk, consumer behavior, employment suitability, health vulnerability, migration status, or security assessment.
Bowker and Star (1999) argue that classification systems are never neutral because they reflect institutional priorities and assumptions about social order. Categories determine what becomes visible, measurable, and administratively relevant.
This creates important consequences for inclusion.
Individuals whose experiences fit institutional categories are more easily recognized within digital systems. Those whose realities are more complex or inconsistent may encounter friction, invisibility, or exclusion. Informal workers, migrants, transgender individuals, unhoused populations, and people with unstable documentation often face difficulties because their lives do not align neatly with standardized systems of verification.
The problem is not only technological.
It is also political and social because digital systems define the conditions under which recognition becomes possible.
Algorithmic Decision Making and Unequal Participation
Algorithmic systems increasingly influence decisions affecting everyday life.
Automated systems are used to assess loan eligibility, prioritize welfare distribution, evaluate job applications, detect fraud, and predict risk. These systems rely on historical data and measurable indicators to produce decisions at large scale.
Cathy O’Neil (2016) argues that algorithmic models frequently reinforce inequality because they reproduce patterns embedded within historical data while maintaining an appearance of objectivity. Individuals with incomplete or unconventional data histories may therefore be disadvantaged within automated systems.
This creates forms of conditional inclusion.
Access to opportunities increasingly depends not only on legal rights or social membership, but also on producing acceptable forms of data that align with institutional expectations. People with limited financial histories may struggle to access credit. Workers lacking formal employment records may become invisible within labor systems. Individuals with inconsistent digital identities may encounter barriers to services.
Inclusion becomes conditional upon data compatibility.
The Unequal Burden of Visibility
Conditional inclusion also produces unequal forms of visibility.
Some populations remain partially invisible because they generate insufficient institutional data. Others become intensely visible through systems of surveillance, monitoring, and behavioral tracking.
David Lyon (2018) describes contemporary surveillance systems as mechanisms of social sorting, where populations are categorized and managed according to institutional priorities. Welfare recipients, migrants, low income communities, and racial minorities are often subjected to disproportionate levels of monitoring compared to more privileged populations.
This creates a paradox.
Individuals may be excluded because they lack sufficient visibility within data systems, while others experience overexposure through continuous institutional scrutiny. Both conditions reflect unequal relationships to power within digital society.
Visibility itself becomes conditional and unevenly distributed.
Digital Identity and the Limits of Standardization
Digital identity systems are frequently promoted as tools of inclusion.
Biometric identification, digital citizenship programs, and integrated databases are often designed to improve administrative efficiency and expand access to public services. In many contexts, these systems provide important benefits by enabling individuals to access banking, healthcare, education, and legal recognition.
However, standardized identity systems also create limitations.
Human lives are often more complex than the categories embedded within administrative infrastructures. Individuals with changing legal status, uncertain documentation, informal living arrangements, or nonstandard identities may struggle to navigate systems designed around rigid forms of classification.
Amartya Sen (2009) argues that justice requires attention to human capabilities and freedoms rather than reducing individuals to simplified categories. From this perspective, systems that demand strict conformity to institutional classifications may undermine meaningful inclusion even while expanding administrative efficiency.
Inclusion becomes conditional when recognition depends on conformity rather than human complexity.
Data Extraction Without Equal Power
Conditional inclusion is also shaped by economic asymmetries within digital systems.
Many individuals participate extensively in digital environments, generating large amounts of behavioral data through smartphones, social media, online transactions, and platform economies. Yet participation does not necessarily produce equal control or benefit.
Shoshana Zuboff (2019) describes this condition as surveillance capitalism, where human experience is transformed into data for commercial extraction and predictive analysis.
This means that inclusion within digital systems may involve participation without power.
Users contribute data continuously while corporations and institutions maintain authority over how information is collected, interpreted, and monetized. Inclusion therefore occurs within systems where control remains highly unequal.
The ability to participate digitally does not automatically guarantee autonomy, representation, or justice.
A Data Justice Perspective
The concept of data justice provides an important framework for understanding conditional inclusion.
Linnet Taylor (2017) argues that data justice concerns fairness in visibility, representation, and treatment within digital systems. This perspective emphasizes that digital infrastructures shape social power rather than functioning as neutral technological tools.
Representation concerns whose experiences are recognized within datasets and institutional systems.
Distribution examines how the benefits and harms of digital participation are allocated across populations.
Governance focuses on who controls the systems that define categories, collect data, and establish the conditions for inclusion.
From this perspective, conditional inclusion reflects broader inequalities embedded within digital governance structures.
The issue is therefore not simply technological access. It concerns the power to define who belongs, who qualifies, and under what conditions recognition becomes possible.
Toward More Inclusive Digital Systems
Addressing conditional inclusion requires moving beyond narrow assumptions that technological expansion automatically produces equality.
At the institutional level, systems must recognize that human lives cannot always be reduced to standardized categories and predictive indicators. Mechanisms for appeal, flexibility, and contextual understanding remain essential.
At the technical level, algorithmic systems should be evaluated for exclusionary outcomes affecting populations with limited documentation, unstable data histories, or nonstandard identities.
At the political level, democratic oversight is necessary to ensure that digital infrastructures serve public interests rather than reinforcing institutional inequalities.
Most importantly, inclusion should not depend entirely on the ability to produce measurable data.
Human dignity and social participation cannot be fully conditioned upon algorithmic legibility.
Conclusion
Data driven systems increasingly shape the conditions of participation within contemporary society.
Access to services, opportunities, and institutional recognition now depends heavily on visibility within digital infrastructures. While these systems promise efficiency and expanded inclusion, they also establish new conditions that determine who can participate fully and who remains excluded.
Inclusion therefore becomes conditional through data.
Individuals who align with institutional categories and generate acceptable forms of data are more likely to receive recognition and access. Those who exist outside standardized systems may encounter invisibility, exclusion, or heightened scrutiny.
The challenge is not simply technological.
It is fundamentally political and ethical because it concerns who is recognized as fully belonging within digital society and under what conditions that recognition becomes possible.
Building more just digital futures requires ensuring that inclusion remains grounded in human dignity and democratic accountability rather than solely in data compatibility.
References
Bowker, G. C., & Star, S. L. (1999). Sorting Things Out: Classification and Its Consequences. MIT Press.
Lyon, D. (2018). The Culture of Surveillance: Watching as a Way of Life. Polity Press.
O’Neil, C. (2016). Weapons of Math Destruction. Crown Publishing.
Scott, J. C. (1998). Seeing Like a State. Yale University Press.
Sen, A. (2009). The Idea of Justice. Harvard University Press.
Taylor, L. (2017). “What Is Data Justice? The Case for Connecting Digital Rights and Freedoms Globally.” Big Data & Society, 4(2).
World Bank. (2021). Identification for Development (ID4D) Global Dataset.
Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.
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