The “value of a person” online does not refer to a moral or philosophical quality. The term denotes a set of metrics calculated by algorithms: reputation score, estimated commercial potential based on personal data, or even a fictitious price assigned by playful sites. These calculations rely on real data, and their legal framework has changed profoundly since the implementation of the European Regulation on Artificial Intelligence.
Personal data and unit price: what a digital profile is worth
Every online interaction produces data. A data broker aggregates these fragments to create a usable profile: browsing history, geolocation, shopping habits, interactions on social media. Taken in isolation, a data point has negligible market value.
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The value increases when these fragments are combined. A complete set of personally identifiable information (PII) is worth, according to specialized market analyses, thousands of times more than the sum of its parts. An email address alone is worth a few cents, but the complete profile (identity, health, finances, interests) reaches significantly higher amounts on resale markets, including the dark web.
Some recent projects offer users the chance to regain control by monetizing their own data. The startup Verb, for example, allows users to sell their data directly to AI labs for model training. Payments remain modest, but the principle reverses the usual logic where only brokers profit from this information.
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To explore a more playful angle on this issue, a page details how much I am worth on Les Affaires du Net by simulating a price based on personal criteria.
Online reputation score: calculation methods and limits
The digital reputation scoring works by aggregating several public signals: customer reviews, media mentions, social media activity, consistency of information across platforms. Specialized tools then assign a synthetic score, sometimes out of 100, sometimes in the form of a letter.
For businesses, this score has a direct impact on valuation. Online reputation is now treated as an intangible asset in corporate valuation practices. A leader selling a small business knows that untreated negative reviews can justify a discount during the negotiation of the sale price.
For individuals, the mechanism is less formalized but just as concrete. A well-referenced LinkedIn profile, consistent publications, and a history of recommendations constitute a reputational capital. Recruiters, business partners, and platform algorithms rely on these signals to sort, rank, and select.
Fake reviews and manipulation: noise in the signal
The scoring relies on public data, which makes it vulnerable to manipulation. Fake Google reviews represent a structural problem. A competitor can artificially inflate their rating or degrade that of a rival with a few dozen fake accounts.
Detecting these fake reviews requires cross-referencing several indicators:
- Recent profiles that have only posted one review, often written in a generic style and lacking specific details about the experience
- Sudden spikes of positive or negative reviews concentrated over a few days, with no correlation to a real event (promotion, opening, incident)
- Nearly identical wording across multiple reviews, suggesting automated or coordinated writing
These distortions directly affect the reliability of the score. A scoring tool that does not filter out these noise signals produces a biased estimate, both for a company and for a freelancer.
Prohibition of social scoring in Europe: the framework of the AI Regulation
The European Union has drawn a clear line with the Regulation (EU) 2024/1689 on artificial intelligence. Article 5 prohibits social scoring systems that evaluate or rank individuals based on social behaviors or personal characteristics when the score leads to unfavorable treatment in a context different from that in which the data was collected.
This prohibition has been in effect since February 2, 2025. It concerns both public and private actors. The penalties can reach 35 million euros or 7% of global turnover, whichever is higher.
The regulatory distinction is precise. Evaluating a customer’s creditworthiness in a banking context remains permitted because the score is used in the same domain as the collection. Using a behavioral score derived from social media to deny a job or housing, however, falls under the prohibition.
What this framework changes for estimation sites
Playful platforms that assign a “price” to a profile are not directly targeted, as long as the result remains informative and does not lead to any legal or economic consequences. The risk arises when third parties exploit these scores to discriminate.
Companies developing profile valuation tools must now document the purpose of the processing and prove that the score is not used out of context. This principle of proportionality adds to the GDPR obligations regarding consent and transparency.
Estimating the value of a person: between play, data, and regulation
The concept of “value of a person online” encompasses three distinct realities:
- The market value of personal data, measurable in cents per unit but in tens or even hundreds of euros once aggregated into a complete profile
- Reputational capital, which influences recruitment, partnership, and valuation decisions during a business sale
- The playful or fictitious score, offered by sites that translate personal characteristics into simulated revenue
These three dimensions converge on one point: they all depend on the quality and quantity of available data. The less exposed a profile is, the more its estimation will be lacking. Mastery of one’s digital identity, from the choice of publicly shared information to the active monitoring of reviews and mentions, remains the only real lever to influence what algorithms calculate.