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Big data and algorithmic systems can affect what people are observed, what can be inferred about them, and how they are treated. Evaluating a system responsibly means looking beyond whether it hides names or reports one fairness score: examine its data practices, effects on different groups, accessibility, transparency, and accountability throughout its lifecycle.
How does big data affect privacy?
Privacy is broader than keeping information secret. It also concerns people’s autonomy, identity, dignity, and ability to exercise agency over disclosure and aspects of their identity. Large-scale data use can affect privacy through what is collected, who can access it, how long it is retained, how it is combined or reused, and what can be inferred. NIST notes that AI can infer identity or information that was previously private, so removing names or other direct identifiers does not by itself establish that people cannot be identified or profiled in a particular context. NIST’s AI risks and trustworthiness material discusses these privacy concerns.
Useful questions include whether people know what data is being collected and why, whether they can meaningfully limit or correct its use, and whether a system’s inferences expose sensitive aspects of their lives. These questions apply to data practices and inferences, not only to the contents of an original dataset.
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Algorithmic bias is not limited to a developer deliberately encoding prejudice. NIST groups relevant sources into three categories: systemic bias, computational and statistical bias, and human-cognitive bias. These can arise at different points, including organizational practices, decisions about how data is collected or measured, system design, and how people interpret and act on an output. NIST’s AI RMF 1.0 material describes these categories.
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- Systemic bias: Existing institutional or societal practices can shape which people are represented, which outcomes are treated as normal, and how a system is used.
- Computational and statistical bias: Data that do not represent the deployment population, imperfect measurements, modeling choices, or evaluation methods can produce uneven errors or outcomes.
- Human-cognitive bias: People can misread a prediction, over-trust an automated recommendation, or apply it inconsistently in decisions.
For example, a model may use a variable that looks neutral but is correlated with a protected or otherwise sensitive characteristic in a particular setting. The OECD notes that postal codes can act as proxies for ethnic origin in some contexts. That does not mean every use of postal codes is discriminatory; it means the variable’s relationship to the people and decision at hand needs to be examined. The OECD’s June 2024 report on AI, data governance, and privacy discusses proxies and differing uses of fairness terminology.
Can an algorithm be fair if its training data is biased?
Not reliably just because a model passes a single test. Data limitations can carry into predictions, but fairness also depends on system design, the way an organization uses outputs, and who can access or contest the resulting decisions. NIST states: “Fairness in AI includes concerns for equality and equity by addressing issues such as harmful bias and discrimination.” It also cautions that mitigating harmful bias does not, by itself, make a system fair. An apparently balanced system can still exclude people with disabilities, reflect the digital divide, or worsen broader disparities. NIST’s AI RMF 1.0 material explains these limits.
Fairness criteria can conflict, and what people consider fair can depend on the application and cultural context. A system’s evaluation should therefore ask which groups and outcomes matter in its specific setting, rather than treating one aggregate score as a complete verdict. Privacy policy discussions also use fairness to address reasonable and transparent data practices, while AI discussions often focus on predictions, recommendations, decisions, and bias affecting particular groups. The OECD report describes this difference in emphasis.
How can organizations balance privacy and fairness?
Data minimization, de-identification, aggregation, and other privacy-enhancing technologies can help protect people. They do not remove the need to evaluate a system’s accuracy and effects: under some conditions, privacy safeguards can reduce the information available for analysis and affect accuracy or fairness assessments. NIST discusses these potential trade-offs in its AI risks and trustworthiness material.
Organizations should assess safeguards and outcomes together in the intended setting. Relevant checks include:
- Privacy: Is each data element necessary? Who can access it, how long is it retained, and what new information could be inferred from it?
- Reliability: Does the system perform adequately for its stated purpose and the population where it will be used?
- Group-level effects: Which errors and outcomes occur across relevant groups, and are the measures appropriate to the decision?
- Accessibility: Can people with disabilities and people affected by the digital divide use or respond to the process?
- Recourse: Can an affected person understand and challenge a consequential result?
- Accountability: Who monitors the system, investigates problems, and changes or stops its use when it causes harm?
What should responsible governance cover?
Governance should begin before deployment and continue as a system, its data, and its uses change. The OECD’s AI principles call for human-centred values, transparency, traceability, accountability, and ongoing risk management that addresses concerns including privacy, security, safety, and bias. Traceability includes documenting datasets, processes, and decisions across the AI lifecycle. The OECD Recommendation of the Council on Artificial Intelligence sets out these principles.
Practical governance questions for a system review include:
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- What is the intended use, and who may be affected directly or indirectly?
- What information is collected, inferred, shared, and retained, and for what purpose?
- Are the data and evaluation samples representative of the population in the actual deployment context?
- Which performance measures and error types are examined across relevant groups?
- Can people understand a consequential result and challenge it through an accessible process?
- Who is responsible for monitoring after deployment, and what action follows when the system fails or its context changes?
Frameworks can help organize this work, but they are not universal legal certifications. NIST describes its AI Risk Management Framework as voluntary; its current framework page says AI RMF 1.0 is being revised and lists a generative-AI profile released July 26, 2024, and a critical-infrastructure profile concept note released April 7, 2026. NIST’s AI RMF page provides the status and profile information. The NIST Privacy Framework 1.0 is dated January 2020, and NIST says it is not binding and does not have the force and effect of law. The NIST Privacy Framework page explains its status. OECD principles are policy guidance, not a universal statute; applicable privacy, discrimination, consumer-protection, employment, education, and financial laws vary by jurisdiction and use case.
The scale of policy activity should not be mistaken for proof of results: the OECD reports that, by May 2023, governments had reported over 1,000 initiatives across more than 70 jurisdictions in its OECD.AI national policy database that follow the OECD AI Principles. That is a count of reported initiatives, not evidence that they were implemented effectively or achieved particular outcomes. The OECD AI Principles page reports the figure.
What did the FTC find about platform data and automated decisions?
In its September 11, 2024 report, A Look Behind the Screens: Examining the Data Practices of Social Media and Video Streaming Services, the U.S. Federal Trade Commission described potential risks associated with examined social media and video-streaming companies’ use of personal information in algorithms, data analytics, or AI. The report discussed concerns including skewed or unrepresentative data, opaque systems, automated decisions people might not know about or understand, and limited recourse when data or decisions were biased or inaccurate. These findings concern the companies and practices within the FTC’s examination; they should not be generalized to every service. Read the FTC report.
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