Why AI Inferences Matter More Than the Data You Provide

AI inferences and predictions impact us constantly.

🧭Executive Summary

Artificial intelligence (AI) systems increasingly influence people's lives through inferred profiles rather than explicit data disclosures. These inferences can be sensitive, continuously updated, and reused across systems. Additionally, they can be difficult to detect, address, and correct, while remaining poorly governed and inconsistently disclosed. Most data privacy and data protection laws and regulations were designed for a world focused on data collection, storage, and transfer. They were not designed to govern systems and technologies that continuously generate probabilistic judgments about individuals.​

Legal and regulatory guidance recognizes automated decision-making and profiling as heightened-risk processing, particularly where inferred judgments produce legal or similarly significant effects (Article 29 Data Protection Working Party, 2018; Bygrave, 2020). Academic and policy work increasingly recognizes that individuals need practical means to understand and contest consequential inferences, not merely access to input data (Buchi et al., 2020; Custers & Vrabec, 2024; Dickey, 2025).​

The primary audience for this article includes AI governance, compliance, data privacy, data protection, product development, and risk professionals who must translate high-level AI risk discussions into concrete controls. It focuses on inferences in high-impact domains, including credit, employment, health, and online platforms. These are areas where errors or bias in automated judgments can lead to human rights, legal, and reputational consequences. As AI moves onto personal devices and into everyday decisions, governing outcomes rather than data flows becomes central to responsible AI governance, data privacy, and data protection.

This article argues that inferences must be treated as first-class objects of governance. For individuals, this means asking which systems are making decisions about them and how those decisions are used. For organizations, this means cataloguing key insights, understanding how they propagate, and assessing their risks. It also entails providing meaningful mechanisms for understanding and challenging consequential algorithmic judgments (Custers & Vrabec, 2024; Dickey, 2025).​

Additionally, it addresses a growing mismatch between the structure of data privacy and data protection frameworks and the operation of modern AI systems.   Today, governance regimes remain focused on data collection and transfer. Moreover, many of today’s most consequential harms arise from inferred judgments that are generated, reused, and amplified across systems with limited visibility or accountability. This gap is becoming more acute as automated decision-making expands into high-impact domains. It widens when AI increasingly operates on personal devices and at the network edge.

The analysis is written for AI governance, compliance, data privacy, data protection, enterprise risk, product development, and policy professionals. They must translate abstract regulatory principles into operational controls. Its purpose is not to criticize data privacy and data protection laws and regulations. It is to show why governing inference is now central to the effectiveness of those frameworks in practice.

🔍Why Inferences Matter More Than the Information You Gave

You apply for a credit card and are approved, but the credit limit is far lower than you expected. Nothing in your credit history explains it. Unknown to you, an automated system inferred that people with similar browsing habits, device usage patterns, and purchasing behavior are more likely to miss payments. That inference, not the information you provided, shaped the outcome.​

Scenarios like this are increasingly common. AI systems now shape people's access to credit, work, healthcare, and information. It occurs not primarily through the data they collect directly, but through the conclusions they draw about individuals. These conclusions are inferences derived from routine signals, such as device metadata, clickstreams, or location traces. They often occur without any additional information that individuals consciously provide.​ Often, those inferences matter far more than the raw data that produced them.​

🧠A Simple Way to Think About AI Decisions

Most AI-driven decisions operate across three layers:​

1. Data you provide, such as forms, messages, or uploads.

2. Data observed about you, including clicks, device metadata, and usage patterns.

3. Inferences drawn about you, including predictions, risk scores, and inferred traits.

Data privacy and data protection tools and notices tend to focus on the first two layers. The third layer is where many of the most impactful and least visible decisions occur (Montagnani & Verstraete, 2023).​

Consider a hiring system. The application form and curriculum vitae/resume sit in the "data you provide" layer. Clickstream data on the careers site, device details, and time‑of‑day usage patterns fall into the "data observed" layer. Fit scores, attrition risk estimates, and productivity predictions live in the "inferences" layer. Most governance artifacts (e.g., records of processing, retention schedules, access controls) describe the first two layers. The third layer is captured only implicitly in model documentation, if at all.​ The same structure appears in credit scoring, insurance underwriting, targeted advertising, and content ranking. Input fields and logs are documented. Inferred risk or relevance scores are often treated as internal technical details, even when they determine who gets what opportunity.​

📘Key Terms

Before examining how AI inferences shape decisions and create data privacy and data protection risks, it is important to clarify the core concepts used throughout this article. These terms describe not only technical processes. They also describe the legal and governance categories that determine how automated judgments are evaluated, challenged, and regulated. Together, they form the conceptual foundation for understanding why inferences deserve closer scrutiny than they have traditionally received in data privacy and data protection frameworks. Table 1 outlines the article’s key terms.

Table 1: Key Terms

Term	Definition	Why It Matters
Inference	A conclusion or prediction about a person derived from one or more data points, often through machine learning or statistical models, rather than information the person explicitly provided (Dickey, 2025; Kamarinou et al., 2016).	Inferences transform raw or observed data into judgments about identity, risk, preference, or behavior, often driving outcomes more powerfully than the data individuals knowingly share.
Profiling	An automated form of processing that evaluates personal aspects of an individual, particularly to analyze or predict behavior, preferences, interests, or performance (Article 29 Data Protection Working Party, 2018).	Profiling operationalizes inferences at scale, enabling ranking, segmentation, and categorization that can shape access to opportunities without individual visibility.
Automated decision making	Decisions about individuals made solely by automated means, without meaningful human involvement, which produce legal or similarly significant effects (Bygrave, 2020).	When inferences feed directly into automated decisions, they move from background analytics to determinative judgments affecting credit, employment, insurance, healthcare, or essential services.
Sensitive inference	An inference that reveals or approximates protected characteristics such as health status, racial or ethnic origin, political opinions, religious beliefs, sexual orientation, or trade union membership, even if those attributes were never directly collected (Kröger, 2022).	Sensitive inferences challenge traditional data-protection boundaries by recreating special-category data through proxies and correlations, often without explicit safeguards.
On-device or edge inference	AI processing that occurs locally on a user’s device or near the network edge reduces centralized data transfers while still enabling rich inferences and decisions about the user.	While often framed as privacy-enhancing, on-device inference can reduce auditability, fragment accountability, and complicate the explanation and oversight of automated decisions.
Outcome-centric governance	An approach to privacy and AI oversight that prioritizes the real-world effects of automated systems on people rather than focusing only on technical data flows, storage locations, or model architecture (Buchi et al., 2020).	Outcome-centric governance reframes compliance and accountability around what systems do to people, aligning oversight with emerging regulatory and ethical expectations.

Source: Adapted from Article 29 Data Protection Working Party (2018), Buchi (2020). Bygrave (2020); Dickey (2025), Kamarinou et al. (2016); Kröger (2022), and Montagnani & Verstraete (2023). Definitions and explanatory language are synthesized for clarity and consistency across privacy, data protection, and AI governance contexts.

⚙️What Are AI Inferences?

AI inferences are conclusions drawn from patterns rather than facts that an individual explicitly provides. A system may infer mood, financial stability, health risk, or political inclination from combinations of seemingly mundane signals, such as browsing behavior, location traces, or interaction timing (Kamarinou et al., 2016).​ These inferences are often sensitive, continually updated, and opaque to the individuals they describe.

For example, a recommender system might infer that a user is experiencing financial distress based on changes in shopping patterns and time spent on certain content. That conclusion may then influence offers, prices, or eligibility without ever being surfaced as a discrete datapoint. Importantly, inferences are rarely presented as objects that people can easily view, correct, or challenge, even though they may drive high-stakes decisions (Custers & Vrabec, 2024).​ Understanding what inferences are is only the first step; governing their impact requires examining how inferred judgments are generated, stored, reused, and propagated across systems over time.

🔁The Inference Lifecycle

Inferences are rarely ephemeral. They persist, propagate, and accumulate influence over time. Understanding this lifecycle is essential to governance because each stage introduces distinct risks and control points (Kröger, 2022). The inference lifecycle typically involves:

1. Inference Data: Information derived about individuals through analysis, correlation, or prediction rather than directly provided (PrivacyForge, 2025).

2. Inference Persistence: Storage of inferred judgments in profiles, feature stores, databases, or logs, often outlasting the data that produced them (Kröger, 2022).

3. Inference Reuse: Use of stored inferences across multiple downstream systems for purposes beyond their original context (Kröger, 2022).​

4. Inference Special Category Data: Combination of non-sensitive inputs to approximate special-category attributes as defined under the European Union’s General Data Protection Regulation’s Article 9 (Hewson, 2023).

5. Signal Aggregation: Merging data from multiple contexts or vendors to create richer and less predictable profiles (Kröger, 2022).​​

This lifecycle explains why deleting raw data frequently fails to eliminate the effects of prior inferences. Because inferences persist and are reused across systems, deleting original data inputs often fails to prevent continued downstream effects on access to products, services, and opportunities (Kröger, 2022).​​ Once inferences persist and circulate across systems, their effects rarely remain isolated. Individual judgments interact, reinforce one another, and shape future decisions in ways that are difficult to observe in isolation.

📈When One Inference Becomes Many

Inferences rarely operate as isolated judgments. Instead, they are often chained, amplified, and reinforced through feedback loops that span multiple systems and decision points. What begins as a single probabilistic assessment can quickly evolve into a cascade of interrelated inferences. They can each shape the conditions under which subsequent data is generated and interpreted. A common example is customer churn prediction (Kröger, 2022).​​ 

An initial churn score may be used to assign a customer to a particular tier. That tiering decision can affect pricing, marketing offers, service responsiveness, or access to premium features. Changes in service quality then influence customer behavior (e.g., reduced engagement or increased complaints), which are subsequently captured as new behavioral data. That new data feeds back into the model, reinforcing the original churn inference. Additionally, it makes the interpretation appear increasingly “accurate,” even when the initial signal was weak or context-specific (Hewson, 2023).

This dynamic illustrates a broader pattern: inferences do not merely describe behavior; they actively shape it. Once an inference is operationalized, it alters the environment in which future data is produced. Over time, systems begin to learn from behavior that they themselves helped create. These feedback loops can entrench disadvantages by systematically steering individuals or groups toward outcomes that confirm prior assumptions (Privacy International, 2017).

Inference amplification is particularly concerning in high-impact domains, including credit, employment, insurance, and content moderation (Privacy International, 2017). A low initial creditworthiness score may limit access to favorable terms, thereby affecting financial behavior and repayment patterns (Van Drunen et al., 2019). In hiring systems, an inferred “low fit” score may reduce interview opportunities. Moreover, it can limit the chance for new evidence that could contradict the model’s expectations (Privacy International, 201). In content ranking, downranking reduces visibility and engagement, producing signals that justify further downranking (Van Drunen et al., 2019).

From a governance perspective, these feedback loops are difficult to detect because no single decision appears determinative. Each step may be individually defensible, documented, and statistically justified. The harm emerges only when decisions are viewed collectively, over time, and across systems (Barocas & Selbst, 2016; Wachter et al., 2021). Contesting a single inference or decision is often ineffective because downstream judgments have already been shaped by earlier ones (Privacy International, 2017).​

This chaining effect also complicates accountability. Responsibility for outcomes becomes distributed across models, teams, and business functions. Records of processing may capture the original data collection. However, they may fail to reflect how inferred outputs propagate and compound across decision pathways (Privacy International, 2017). As a result, bias and exclusion can become structurally embedded without any explicit intent or single point of failure (Barocas & Selbst, 2016; Wachter et al., 2021).

Understanding how one inference gives rise to many is therefore essential for meaningful oversight. Governance mechanisms that focus only on individual models or discrete decisions will miss the systemic effects of inference amplification. Effective control requires examining how inferences interact and how they reshape behavior. They can also determine how feedback loops can magnify initial errors into durable patterns of disadvantage (Buchi et al., 2020). These compounding dynamics explain why inferences pose distinct and often greater risks than raw data itself. These risks occur particularly when judgments are decisive, opaque, and difficult to correct once operationalized.

​⚠️Why Inferences Can Be More Dangerous Than Raw Data

First, raw data describe what happened; inferences predict what might happen next or provide insights into the person. This distinction shapes how decisions about people are made. First, inferences can be incorrect yet still decisive: inaccurate predictions about risk, productivity, or reliability can shape credit terms, employment screening, or access to services even when the underlying data are accurate (Barocas & Selbst, 2016; Privacy International, 2017). A model may infer that a candidate will “churn” based on proxies such as job-hopping in a particular region. They can lead to the systematic exclusion of certain groups without an explicit statement to that effect (Barocas & Selbst, 2016).​

Second, inferences can recreate sensitive information. By combining non-sensitive data points such as location, app usage, and time patterns, AI systems can effectively approximate special category attributes without directly collecting them. These inferences can raise significant governance concerns (Kröger, 2022; Privacy International, 2017). For instance, regular visits to specific clinics, places of worship, or political events can yield high confidence estimates of health status, religion, or political views (Privacy International, 2017).​

Third, inferences are harder to see, correct, or delete. Many data privacy and data protection tools expose collected data by allowing people to download a profile or view their search history. Unfortunately, they rarely reveal derived scores, segments, or probabilistic judgments, even though those judgments may have the greatest real-world impact (Kröger, 2022; Privacy International, 2017). In practice, subject access requests often return records of inputs and transactions, but not the risk scores or segments used to rank or filter an individual (Privacy International, 2017).​ When these three features, decisiveness, sensitivity, and opacity, combine, inferences can produce harm. These harms can become more severe and harder to remedy than those arising from a single leaked dataset (Barocas & Selbst, 2016; Kröger, 2022; Privacy International, 2017).

📱On‑Device AI Does Not Eliminate Inference Risk

On-device AI can reduce data transfer and breach exposure, but it does not eliminate the power or consequences of inference. Systems running locally on phones, browsers, vehicles, or smart devices can still rank, filter, recommend, and decide, often without centralized logging or oversight. As more models are deployed at the edge, critical judgments about people are increasingly made in environments that were not designed to support traditional compliance, data privacy, and data protection controls.​

This distribution can improve security and data minimization, but it also introduces new governance blind spots. Edge systems can generate inferences outside existing logging and monitoring frameworks, making it harder for organizations or regulators to reconstruct how a decision was made or to audit its fairness. Responsibility for outcomes becomes fragmented across operating system providers, app developers, and model vendors. Consequently, there is no single entity that is clearly accountable for the full inference lifecycle. Users, meanwhile, may struggle to access, understand, or challenge inferences generated locally on their devices, especially when those inferences are not surfaced as explicit scores or labels (Velasco & Lareo, 2025).​

Different versions of a model may run in different environments. Logs may be partial or absent, and controls designed for centralized databases may no longer capture the location of decision-making (Velasco & Lareo, 2025). As regulators increasingly emphasize human oversight, contestability, and demonstrable fairness, this shift to on-device inference raises new governance challenges rather than resolving existing ones. They include how to ensure meaningful accountability and explanation when critical judgments are distributed across devices and organizations (Article 29 Data Protection Working Party, 2018; Velasco & Lareo, 2025).​ Given the decisive, sensitive, and often opaque nature of inference-driven decisions, the next question is whether existing data privacy and data protection laws and regulations meaningfully govern inferred judgments. Another question would examine whether critical gaps remain.

⚖️How Data Privacy and Data Protection Laws and Regulations Treat Inferences, and Where They Fall Short

European-style data protection regimes treat certain forms of profiling and automated decision-making as particularly risky when they have legal or otherwise significant effects on individuals. Guidance under the European Union’s (EU) General Data Protection Regulation states that such decisions require safeguards, including transparency and, in many cases, meaningful human involvement. The United Kingdom’s (UK) Information Commissioner (ICO) and other global regulators similarly stress the need for clear explanations and meaningful human review of significant automated decisions. They include those based on profiling (Article 29 Data Protection Working Party, 2018).​

Despite this, inferred data remains poorly specified in doctrine and practice. Many legal texts focus on data that is directly collected or stored. They leave scores, risk ratings, and inferred attributes in a grey zone where their status and protection are uncertain. Scholarly analysis supports the application of access, rectification, erasure, and portability rights to inferred and ascribed data, even though enforcement remains contested and uneven in practice (Custers & Vrabec, 2024; Kröger, 2022). Controllers also frequently rely on trade secrets and others' rights to resist the disclosure or deletion of inferences, even when those inferences drive important outcomes (Fischer, 2020).​

Past case law of the Court of Justice of the EU has confirmed that processing may fall within the EU GDPR’s special category regime when the controller is indirectly liable to disclose sensitive information (Maynard et al., 2022). Courts and supervisory authorities have held that profiling, which may reveal health status, sexual orientation, or political opinions, constitutes special-category processing. It may occur, even when those attributes are inferred rather than directly collected (Maynard et al., 2022). Consequently, data controllers must assess what their profiling and scoring reveal about data subjects, not just whether the raw inputs are sensitive in themselves (Article 29 Data Protection Working Party, 2018).​

Regulatory guidance and supervisory practice emphasize outcome-focused scrutiny, directing attention to the real-world effects of profiling and automated decision-making rather than formal compliance with notice-and-consent mechanisms alone (Article 29 Data Protection Working Party, 2018; Information Commissioner’s Office, 2025). Moreover, authorities are examining the real-world effects of profiling and automated decision-making rather than formal compliance with notice-and-consent rules. At the same time, protection for inferences remains fragmented and unevenly enforced. The author proposes establishing a distinct “right to reasonable inferences” to allow individuals to challenge high-risk, weakly justified inferential practices (Viljoen, 2021).​

A practical reading of current law or regulation can be sketched along three lines.

High‑impact inferences that significantly affect people can trigger rights to explanation, human review, and the ability to contest solely automated decisions when the conditions of the EU GDPR’s Article 22 and related rights are met. Inferences that reveal or closely approximate special‑category attributes, such as health, political views, or sexual orientation, may require the same level of protection as directly collected special‑category data because the EU GDPR’s Article 9 applies whenever processing is liable to reveal such information.

Even when texts do not explicitly mention “inferences,” duties of fairness, transparency, and data protection by design are increasingly interpreted to encompass the generation and use of inferred data. This is particularly pertinent when they share access to important opportunities or services (Information Commissioner’s Office, 2025).​ Across jurisdictions, emerging AI laws and frameworks increasingly converge on a common concern. They address that when automated systems are used to assess, rank, or predict individuals in consequential contexts, additional safeguards are required. While the legal mechanisms differ, the regulatory logic is consistent. Consequently, AI governance is shifting from technical inputs toward real-world effects.

🧩Common Organizational Failure Patterns

Across sectors and jurisdictions, organizations tend to repeat a small number of predictable failure patterns when managing AI-generated inferences. These patterns are rarely the result of bad faith. Instead, they emerge from structural gaps between technical development, business decision-making, and privacy governance (OECD, 2024).

1.    Allowing vendors to define the scope of inference: Organizations frequently rely on third-party platforms, analytics providers, or AI vendors whose models generate inferences as part of a “black box” service. In these arrangements, vendors often determine which attributes are inferred, how long those inferences persist, and how they are reused across products or clients. Contracts and due diligence processes focus primarily on data inputs and security controls, rather than on inferred outputs and decision logic. Consequently, organizations outsource decisions about what will be concluded about individuals, without meaningful oversight or accountability (Dickey, 2025).

2.    Assuming consent to data collection covers downstream inference: Another recurring assumption is that user consent or notice at the point of data collection implicitly authorizes all subsequent inference. In practice, the risks and purposes associated with inference often differ materially from those associated with raw data collection. An individual may understand why data are collected. However, he or she has no expectation that it will be used to infer sensitive traits, long-term risk profiles, or eligibility judgments. Treating consent as a blanket authorization for all derived conclusions erodes purpose limitation and weakens the legitimacy of automated decision-making (Custers & Vrabec, 2024).

3.    Failing to define retention, refresh, or decay rules for inferences: Many organizations apply retention policies to raw data but fail to establish comparable rules for inferred data. As a result, inferences may persist indefinitely in profiles, feature stores, or downstream systems, even when the circumstances that produced them have changed. Without defined mechanisms to refresh, contextualize, or retire inferences, outdated judgments can continue to shape decisions long after they are no longer accurate or fair (LeapXpert, 2025).

4.    Fragmented ownership and unclear accountability: Underlying these failures is a broader governance issue: no single function clearly owns inference risk. Data science teams focus on performance, product teams on outcomes, legal teams on compliance, and data privacy and data protection teams on data flows. Inferences fall between these domains. When accountability is diffused, risks accumulate quietly, and harmful patterns can become embedded before they are recognized (Kodakandla, 2024).

5.    Treating inferences as technical artifacts rather than personal data: A common failure is treating inferences as internal technical output rather than as personal data. When framed this way, inferred judgments often fall outside the scope of records of processing, retention rules, and impact assessments. Moreover, they allow consequential decisions to be driven by outputs that remain loosely governed (Fischer, 2020).

These organizational failure patterns are best understood as symptoms of misalignment rather than intent. Most organizations did not design their governance frameworks for systems that continuously generate, reuse, and compound judgments about people. Addressing inference risk, therefore, requires not just better policies but clearer ownership, explicit governance of inferred data, and closer integration between technical and compliance functions.

🧠Common Myths About Inferences

Inferences are easy to misinterpret because they are less visible than the underlying data and are often couched in technical language (Montagnani & Verstraete, 2023).​ A short orientation before introducing myths and practical steps can help readers see why inferences matter and how they differ from familiar privacy risks.​ Inferences are the conclusions that systems draw about people (e.g., risk scores, predicted behaviors, or inferred traits) based on observed or collected data. They often remain invisible to individuals, yet can strongly influence access to credit, work, services, and information. These myths include:

1.    Myth #1: If data is anonymized, inferences are safe: Reality: Inferences can still affect individuals even when inputs are obscured. An advertising or risk‑scoring system may use aggregated or pseudonymized data to generate group-level propensities and then apply those propensities back to individuals, creating concrete effects despite the absence of directly identifiable inputs (Montagnani & Verstraete, 2023).​

2.    Myth #2: On-device processing eliminates privacy risk: Reality: Decisions and impacts still occur locally, often outside traditional oversight mechanisms. A model that denies a loan or downgrades an application on a device can still discriminate or misjudge; retaining raw data on the device does not diminish the significance of the decision or the need for effective human oversight (Velasco & Lareo, 2025).​

3.    Myth #3: Probabilistic judgments do not count: Reality: Probabilities can still drive exclusion, disadvantage, and discrimination. A “70% risk of churn” or “low engagement probability” score can be enough to withhold offers, downgrade service, or reduce support, even though it is only an estimate (Custers & Vrabec, 2024).​

Because inferred judgments persist and are reused across systems, deleting raw data frequently fails to eliminate their downstream effects on access to products, services, and opportunities (Kröger, 2022). Taken together, these myths can make inferences seem less consequential and less subject to effective oversight than they are in practice. It is necessary to clarify that anonymization, on-device processing, and probabilistic labels can still produce real-world consequences. It must precede consideration of what individuals and organizations can concretely do in response.

👤What Can Individuals Do Today?

Legal and regulatory tools are imperfect, but individuals are not powerless. People can use existing rights and practical strategies to surface and challenge inferences that shape important outcomes.​

1. When facing an unexpected or adverse decision that appears automated (for example, a denied loan, account closure, or algorithmic downgrade), explicitly ask whether the decision was based solely on automated processing, including profiling, and request an explanation of the main factors and inferences used (Custers & Vrabec, 2024).​

2. Use data‑subject rights, where available, to seek access not only to raw data but also to the categories of profiling and automated decision‑making that apply to you; even partial disclosure can pressure organizations to document their inferences more thoroughly (Custers & Vrabec, 2024).​

3. Limit unnecessary cross-context data sharing (e.g., linking identities across platforms or granting broad app permissions), especially where combined signals could reveal or approximate sensitive traits (Montagnani & Verstraete, 2023).​

4. Challenge unexplained or disproportionate outcomes and ask organizations to justify the inferences that shaped decisions, creating records that regulators and advocacy groups can later review (Custers & Vrabec, 2024).​

These steps will not eliminate inference risk, but they can help individuals identify patterns, prompt internal reviews, and build an evidentiary record to support broader regulatory and policy change.​

🏛️What Organizations Should Be Doing Differently

Organizations that seek to maintain trust, regulatory readiness, and long-term legitimacy must treat inferences as governed assets rather than incidental byproducts of analytics or model performance. Inference-driven systems increasingly determine access to credit, employment, services, pricing, visibility, and opportunities. However, many AI governance, data privacy, and data protection frameworks remain anchored to an input-centric model that documents what data is collected while leaving derived judgments weakly controlled.

Effective governance, therefore, requires explicit capabilities to identify, assess, monitor, explain, and retire inferences across the full lifecycle of AI systems. The following framework translates legal, regulatory, and scholarly expectations into concrete organizational practices.

1.    Identify Key Inferences:

·         Rationale: Organizations cannot govern what they cannot see. AI systems routinely generate scores and classifications that shape outcomes yet remain undocumented or treated as transient technical artifacts (Article 29 Data Protection Working Party, 2018; Kröger, 2022).

·         Governance Expectation: Organizations should maintain an inventory of key inferences used across products and services, documenting the type of inference, decision context, contribution to legal or similarly significant effects, and where the inference is generated, stored, and reused across systems. This inventory should document:

o   The type of inference (e.g., credit risk score, churn propensity, suitability ranking, fraud likelihood, engagement prediction)

o   The decision contexts in which it is used.

o   Whether it contributes to legal or similarly significant effects

o   Where the inference is generated, stored, and reused, including downstream systems and vendors.

Without this inventory, later governance steps (e.g., risk assessment, explanation,

or contestation), become speculative rather than operational.

2.    Classify High-Impact and Sensitive Inferences:

·         Rationale: Not all inferences carry the same level of risk. Governance must therefore be proportionate to impact and sensitivity. EU data protection doctrine distinguishes decisions with legal or similarly significant effects from low-stakes personalization, triggering heightened safeguards when consequences are meaningful (Article 29 Data Protection Working Party, 2018).

At the same time, scholarship and case law demonstrate that inferences can approximate special-category data through proxies, even when no sensitive data is directly collected (Kröger, 2022; Maynard et al., 2022).

·         Governance Expectation: Organizations should classify inferences along at least three dimensions:

o    Impact level: legal or similarly significant effects versus low-stakes personalization

o    Sensitivity: explicit special-category data versus inferred proxies for protected attributes

o    Scope of use: single-purpose application versus reuse across multiple systems or contexts

This classification enables prioritization, supports risk-proportionate controls, and aligns governance effort with real-world consequences.

3.    Assess Risk and Define “No-Go” Inference Categories:

·         Rationale: Risk assessments that focus solely on input data fail to capture how harm arises in inference-driven systems. Research on data subject rights demonstrates that deleting or minimizing raw data does not neutralize the consequences of inferred profiles once they are generated and operationalized (Custers & Vrabec, 2024).

Moreover, legality alone does not exhaust governance responsibility. Certain

inferences may be technically feasible yet socially unacceptable,

disproportionate, or harmful when used in commercial or public-sector

contexts (Kröger, 2022).

·         Governance Expectation: Organizations should:

o    Explicitly incorporate inferences into data protection impact assessments and AI risk assessments

o    Evaluate risks arising from error, bias, reuse, persistence, and feedback loops.

o    Define “no-go” categories of inference that will not be generated or used, even if technically possible.

In commercial settings, no-go categories commonly include inferences about

sexual orientation, religious belief, political ideology, or certain health conditions.

These boundaries support purpose limitation, harm prevention, and trust

preservation, rather than relying solely on post-hoc compliance defenses.

4.    Define Retention, Refresh, and Decay Rules

·         Rationale: Many organizations apply retention limits to raw data but fail to establish comparable rules for inferred data. As a result, inferences can persist indefinitely in profiles, feature stores, or downstream systems, even when the circumstances that produced them have changed.

Scholarship on inferred data highlights that such persistence undermines fairness, accuracy, and the effectiveness of data subject rights (Custers & Vrabec, 2024; Kröger, 2022).

·         Governance expectationOrganizations should define clear policies governing:

o    How long do inferences persist?

o    When must they be refreshed or recalculated?

o    Under what conditions are they overwritten or deleted?

Retention logic should be tied to purpose, impact, and sensitivity, not operational

convenience. Without refresh and decay rules, inference stagnation can allow

outdated or unjustified judgments to continue to shape decisions.

5.    Enable Contestation and Explanation:

·         Rationale: Transparency and contestability are central to the legitimacy of automated decision-making. Regulatory guidance emphasizes that individuals must receive meaningful information about automated decisions, particularly where those decisions have significant effects (Article 29 Data Protection Working Party, 2018; Information Commissioner’s Office, 2025).

Research on inferred data shows that contestation fails in practice when

organizations cannot explain which inferences were used or how they

influenced outcomes (Custers & Vrabec, 2024).

·         Governance expectation: Organizations should provide accessible and practical channels for individuals to:

o    Ask whether automated decisions and inferences affect them.

o    Receive high-level explanations of the main factors and types of inference involved.

o    Seek review, correction, or escalation through existing complaint or appeal mechanisms.

Explanation templates should communicate substance without requiring

disclosure of proprietary model details. Where explanation is absent, trust

degrades and regulatory exposure increases.

6.    Monitor Outcomes and Ensure Accountability Over Time:

·         Rationale: Inference-driven harm often emerges over time through repeated use, feedback loops, and cumulative disadvantage, even when individual decisions appear defensible in isolation. Empirical and legal scholarship demonstrates that profiling systems can entrench inequality and produce chilling effects even in the absence of explicit intent (Barocas & Selbst, 2016; Büchi et al., 2020).

Regulators increasingly emphasize outcome-focused oversight rather than

purely procedural compliance (Velasco & Lare, 2025).

·         Governance Expectation: Organizations should:

o    Monitor real-world outcomes and disparities, not only model accuracy, or performance metrics.

o    Examine how inferences interact across systems and over time.

o    Ensure governance follows the model, whether processing occurs centrally or on-device.

Where inference generation is distributed, accountability-oriented practices such

as version tracking, periodic sampling, and scoped logging may be necessary to

support oversight (Velasco & Lareo, 2025).

Clear ownership should be assigned across privacy, risk, legal, data science, and product teams, with escalation paths for high-risk inferences. Boards and senior leadership should receive regular reporting on key inferences, identified harms, and mitigation status.

Treating inferences as governed assets allows organizations to demonstrate that they are managing not only data flows, but the judgments and outcomes those flows enable. This shift aligns governance with emerging regulatory expectations and addresses the real locus of risk in modern AI systems.

🕰️Why This Conversation Matters Now

As AI systems increasingly operate through inference rather than explicit instruction, the center of gravity of data privacy and data protection risks is shifting. The most consequential harm no longer arises primarily from the collection or transfer of personal data. They arise from automated judgments that classify, predict, and rank individuals, thereby shaping access to opportunities, services, and rights.

For individuals, this shift changes the core privacy question. It is no longer sufficient to ask what data was collected or shared. The more pressing concern is what conclusions systems draw about them, how durable those conclusions are, and how they are used across decisions that may be difficult to see, challenge, or correct. Inference-driven systems can silently shape outcomes even when data collection appears minimal, localized, or consent-based.

For organizations, this evolution exposes a growing mismatch between traditional governance models and operational reality. Many AI governance, data privacy, and data protection programs remain anchored to an input-centric paradigm. They focus on static datasets, data flows, and storage locations. That model does not adequately capture how risk emerges in systems that continuously generate, reuse, and compound inferred judgments. Legal and regulatory guidance on profiling and automated decision-making increasingly reflects this shift. They emphasize the effects on individuals and meaningful contestability, rather than formal compliance with notice-and-consent mechanisms alone (Article 29 Data Protection Working Party, 2018).

This matters now because regulatory scrutiny, litigation risk, and public expectations are converging around outcome-focused accountability. Scholarly and policy analyses show that unmanaged inference practices can lead to discrimination, exclusion, and structural disadvantage, even in the absence of any single unlawful act or malicious intent (Barocas & Selbst, 2016; Büchi et al., 2020). At the same time, regulators are signaling that controllers must be able to explain and justify how automated judgments affect individuals. Specifically, they seek to determine whether decisions have legal or otherwise significant effects on individuals (Information Commissioner’s Office, 2025).

Organizations that fail to adapt their governance frameworks accordingly face compounding risk. Legal exposure may arise from unfair or unlawful automated decision-making. Reputational damage can follow when inference-driven harms become visible through investigative journalism, advocacy, or enforcement actions. Most significantly, structural harms created by feedback loops and persistent inferred profiles can become entrenched. More importantly, they can become too difficult to unwind once they are embedded across systems and processes. In this context, governing inferences is no longer a theoretical or future-facing concern. It is a practical necessity for the deployment of responsible AI, credible data privacy, and data protection compliance, and sustained public trust.

📝Key Takeaways

The following takeaways synthesize the article’s core findings and governance implications. They are not abstract principles, but practical conclusions drawn from current regulatory guidance, legal scholarship, and observed patterns in how AI systems operate in practice. Together, they highlight why inference-driven systems pose distinct privacy and data protection risks, and why traditional, input-focused compliance approaches are no longer sufficient. Readers should view these takeaways as a checklist for reassessing both individual rights protections and organizational governance models in the age of inference.

1.    Inferences Often Matter More Than Raw Data: In modern AI systems, decisions are increasingly driven by inferred scores, classifications, and predictions rather than by the data individuals provide. Legal and scholarly analysis shows that these inferred judgments often persist, propagate, and produce effects that are more consequential than the underlying inputs. This is particularly relevant in profiling and automated decision-making contexts (Article 29 Data Protection Working Party, 2018; Custers & Vrabec, 2024).

2.    Sensitive Traits Can Be Approximated Without Direct Collection: AI systems can infer health status, political opinions, religious beliefs, or other protected characteristics by combining non-sensitive data points. Courts and scholars have recognized that such inferred attributes may trigger the same legal and ethical concerns as directly collected special-category data. This requires heightened safeguards and, in some contexts, outright prohibitions (Kröger, 2022; Maynard et al., 2022).

3.    Most Transparency Tools Expose Inputs, Not Conclusions: Access rights and privacy dashboards typically reveal what data was collected but not what conclusions were drawn. Research on data subject rights demonstrates that this gap undermines meaningful contestation when individuals cannot see, correct, or challenge the inferences that drive decisions (Custers & Vrabec, 2024).

4.    On-device AI changes where risk appears, not whether it exists.Local or edge-based processing can reduce certain data transfer and breach risks, but it does not eliminate inference-driven harms. Distributed models can still make consequential decisions while reducing auditability and visibility, requiring adapted oversight mechanisms rather than relaxed governance expectations (European Data Protection Supervisor, 2025).

5.    Effective governance requires managing inferences and outcomes, not just data flows: Regulatory guidance increasingly emphasizes the real-world effects of profiling and automated decision-making rather than formal compliance with notice-and-consent models. Governing AI systems, therefore, requires inventories, limits, monitoring, and accountability mechanisms centered on what systems decide about people and how those decisions affect access and opportunity (Article 29 Data Protection Working Party, 2018).

6.    Inference chains and feedback loops can amplify bias and entrench disadvantage. Inference-driven systems often operate through feedback loops in which initial judgments shape behavior, generate new data, and reinforce earlier conclusions. Empirical and legal scholarship demonstrates that such dynamics can produce structural discrimination and chilling effects, making single-point contestation insufficient without systemic governance (Barocas & Selbst, 2016; Büchi et al., 2020).

7.    Privacy in the age of AI is about governing interpretation, not just information. As automated systems increasingly decide who people are believed to be, meaningful data protection depends on making inferences visible, contestable, and governable. Without control over inferred judgments and their downstream effects, individuals may comply with every data-sharing rule and still experience unaccountable harm (Custers & Vrabec, 2024; Kröger, 2022).

Taken together, these findings underscore a fundamental shift in how privacy, data protection, and AI governance risks arise in practice. The analysis above shows that inference-driven systems do not merely process information; they actively shape behavior, opportunity, and access through judgments that are often invisible, persistent, and difficult to contest. Traditional, input-focused governance models are therefore no longer sufficient to address the locus of harm. The following conclusion draws these threads together to explain why governing inference is now central to meaningful privacy protection and to the responsible and trustworthy deployment of AI.

📜Conclusion: Privacy in the Age of Inference

As AI systems increasingly operate through inference rather than explicit instruction, the center of gravity of privacy, data protection, and AI governance has shifted. The most consequential risks no longer arise primarily from how much data is collected or where it is stored, but from how automated systems interpret that data, generate judgments about individuals, and apply those judgments across decisions that shape access to opportunity, services, and rights.

This shift exposes a growing mismatch between traditional, input-focused governance models and the operational reality of inference-driven systems. Frameworks designed around data minimization, notice, and consent remain necessary but are no longer sufficient in themselves. Where inferred judgments persist, propagate, and compound across systems, accountability must extend to what systems conclude about people, not only to what data they ingest.

Governing inference is therefore not a future-facing aspiration or an abstract ethical concern. It is a present operational requirement for organizations that deploy AI in consequential contexts. Mature governance programs will be distinguished not by the absence of automation, but by their ability to identify, constrain, explain, and monitor the inferred judgments that shape real-world outcomes. In the age of inference, responsible decision-makers are those who govern conclusions with the same rigor once reserved for data itself.

❓Key Questions for Stakeholders

The following questions are designed to help different stakeholder groups assess whether their organizations are governing inference-driven systems in ways that are legally defensible, ethically responsible, and operationally realistic. They are not intended as abstract prompts. Each question reflects documented governance gaps identified by regulators, courts, and scholars, and should be used to test whether inference risks are being actively managed rather than implicitly tolerated.

1.    For Boards and Senior Leadership Teams:

·         Which inferences are most central to our mission or business model, such as risk scores, eligibility ratings, suitability rankings, or churn and fraud propensities, and are these inferences explicitly documented, owned, and reviewed at an appropriate governance level?

·         What are the plausible harms if a key inference is wrong, biased, or reused outside its intended context, and have we consciously decided whether those risks are acceptable, mitigated, or prohibited, rather than allowing them to persist by default?

·         Do our oversight and reporting mechanisms track real-world outcomes, including how automated judgments affect people’s access to products, services, employment, or opportunities over time, rather than focusing only on incidents, data flows, or technical performance metrics?

2.    For Engineers, Data Scientists, and Product Teams:

·         When designing or deploying a model, do we explicitly map which attributes and inferences it generates, and test how errors or biases in those inferences could lead to unfair, exclusionary, or harmful outcomes for different groups, rather than relying solely on aggregate accuracy metrics?

·         How are inference behaviors documented, monitored, and versioned over time, particularly for models running on devices or at the edge, so that governance, legal, and privacy teams can understand what judgments are being made and under what constraints?

·         Have we implemented technical and procedural guardrails, such as explicit “no-go” inference categories, review checklists, and escalation triggers, to prevent systems from generating or acting on certain high-risk judgments even when the underlying data would technically allow it?

3.    For Compliance, Data Privacy, Data Protection, and Risk Teams:

·         Do records of processing activities, DPIAs, and AI risk assessments explicitly describe inferred outputs, including scores, classifications, and profiles, or do they focus primarily on input data collection and sharing while leaving derived judgments undocumented?

·         Where in our systems are sensitive inferences being generated indirectly, such as proxies for health status, political views, religious beliefs, or other protected characteristics, and have we defined clear policy limits or prohibitions on their use consistent with special-category data protections?

·         Do individuals have practical, usable avenues to access, understand, and contest high-impact inferences or automated decisions, or do existing processes satisfy formal legal requirements while remaining ineffective in practice?

4.    For Regulators and Policymakers:

·         Are existing rules on profiling, automated decision-making, and sensitive data being interpreted in ways that meaningfully cover inferred information, or do gaps remain that allow high-impact inferences to escape effective oversight?

·         How can regulatory frameworks require meaningful explanation and contestability of inference-driven decisions without mandating full disclosure of proprietary models, balancing accountability with legitimate intellectual property?

·         What minimum standards should apply to the accuracy, fairness, persistence, and contestability of high-impact inferences in domains such as credit, employment, insurance, healthcare, and public services, and how should those standards be enforced over time to address cumulative and systemic harm?

📍References

1.    Article 29 Data Protection Working Party. (2018). Guidelines on automated individual decision-making and profiling for the purposes of Regulation 2016/679 (WP251 rev.01). https://ec.europa.eu/newsroom/article29/redirection/item/612053 

2.    Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.2139/ssrn.2477899​

3.    Buchi, M., Fosch-Villaronga, E., Lutz, C., Tamo-Larrieux, A., Velidi, S., & Voljoen, S. (2020). The chilling effects of algorithmic profiling: Mapping the issues. Computer Law & Security Review, 36, 105367. https://doi.org/10.1016/j.clsr.2019.105367 

4.    Bygrave, L. A. (2020). Article 22 Automated individual decision‑making, including profiling. In C. Kuner, L. A. Bygrave, & C. Docksey (Eds.), The EU General Data Protection Regulation (GDPR): A commentary. ​https://doi.org/10.1093/oso/9780198826491.003.0055

5.    Custers, B., & Vrabec, H. (2024). Tell me something new: data subject rights applied to inferred data and profiles. Computer Law & Security Review, 52, 105956. https://doi.org/10.1016/j.clsr.2024.105956 

6.    Dickey, J. (2025). Privacy by Proxy: Regulating inferred identities in AI systems. IAPP. https://iapp.org/news/a/privacy-by-proxy-regulating-inferred-identities-in-ai-systems 

7.    European Commission. (2018). Guidelines on automated individual decision-making and profiling for the purposes of Regulation 2016/679 (wp251rev.01).​ https://ec.europa.eu/newsroom/article29/items/612053/en

8.    European Data Protection Supervisor. (2025). TechDispatch 2/2025: Human oversight of automated decision-making systems.​ https://www.edps.europa.eu/data-protection/our-work/publications/techdispatch/2025-09-23-techdispatch-22025-human-oversight-automated-making_en

9.    Fischer, C. (2020). The legal protection against inferences drawn by AI under the GDPR. Tillberg University Law School. https://arno.uvt.nl/show.cgi?fid=151926

10. Hewson, K. (2023). Personal data or inferred special category of data? Stephenson Harwood Data Protection Hub. https://www.dataprotectionlawhub.com/blog/personal-data-or-inferred-special-category-data

11. Information Commissioner's Office. (2025). Automated decision‑making and profiling.​ https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/individual-rights/automated-decision-making-and-profiling/

12. Kamarinou, D., Millard, C., & Singh, J. (2016). Machine learning with personal data: Profiling, decisions and the EU General Data Protection Regulation.​ Queen Mary University of London School of Law. https://www.mlandthelaw.org/papers/kamarinou.pdf

13. Kodakandla, P. (2024). Privacy-by-design in AI data pipelines: A unified governance approach. Global Journal of Engineering and Technology Advances, 21(1), 215 – 224. https://doi.org/10.30574/gjeta.2024.21.1.0187

14. Kröger, J. L. (2022). Recognizing information inferred about individuals as personal data. SSRN. https://doi.org/10.2139/ssrn.4349200 

15. LeapXpert. (2025). AI and data privacy: Risks, compliance, and protection strategies. https://www.leapxpert.com/ai-and-data-privacy/

16. Maynard, P., Cooper, D., O’Shea, S. (2022). Special category of data by inference: CJEU significantly expands the scope of Article 9 GDPR. Inside Privacy – Covington. https://www.insideprivacy.com/eu-data-protection/special-category-data-by-inference-cjeu-significantly-expands-the-scope-of-article-9-gdpr/https://www.insideprivacy.com/eu-data-protection/special-category-data-by-inference-cjeu-significantly-expands-the-scope-of-article-9-gdpr/

17. Montagnani, M. L., & Verstraete, M. (2023). What makes data personal? UC Davis Law Review, 56(3), 1165 – 1232.​ https://lawreview.law.ucdavis.edu/sites/g/files/dgvnsk15026/files/media/documents/56-3_Montagnani_Verstraete.pdf

18. Organization for Economic Cooperation and Development (OECD). (2024). AI, data governance and privacy: Synergies and areas of international co-operation. OECD Artificial Intelligence Papers, 22. https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/06/ai-data-governance-and-privacy_2ac13a42/2476b1a4-en.pdf

19. PrivacyForge. (2025). Inferred data. https://www.privacyforge.ai/glossary/inferred-data

20. Privacy International. (2017). Data is power: Profiling and automated decision‑making in GDPR. https://privacyinternational.org/sites/default/files/2018-04/Data%20Is%20Power-Profiling%20and%20Automated%20Decision-Making%20in%20GDPR.pdf

21. Saunders, D. P., Pimentel, A. C., Linsky, K., Sawyer, J. M., Roberts, M., Southwell, A. H., Schreiber, M. E., Barcelo, R., Karniyevich, N., Huba, M., & Golding, E. R. (2025). Data, privacy, and cybersecurity developments we are watching in 2026. McDermott Will & Schulte. https://www.mwe.com/insights/data-privacy-and-cybersecurity-developments-we-are-watching-in-2026/ 

22. Velasco, L., & Lareo, X. (2025). Human oversight of automated decision-making. TechDispatch-European Data Protection Supervisor. https://www.edps.europa.eu/system/files/2025-09/25-09-15_techdispatch-human-oversight_en.pdf (Use in lieu of EDPS 2025 article).

23. Viljoen, S. (2021). A relational theory of data governance. The Yale Law Journal, 131​, 573 – 654. https://yalelawjournal.org/pdf/131.2_Viljoen_1n12myx5.pdf

24. Wachter, S., Mittelstadt, B., & Russell, C. (2021). Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review, 41, 105567. https://doi.org/10.1016/j.clsr.2021.105567