Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.

The authors argue that it is possible to partly automate the process of abstract control of fairness of clauses in online consumer contracts. The authors present a theoretical and empirical argument for this claim, including a brief presentation of the software they have designed. This type of automation would not replace human lawyers, but would assist them and make their work more effective and efficient. Policy makers should direct their attention to the potential of using algorithmic techniques in enforcing the law regarding unfair contractual terms, and to facilitating research on and ultimately implementing such technologies.

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In the digital economy, consumer vulnerability is not simply a vantage point from which to assess some consumers’ lack of ability to activate their awareness of persuasion. Instead, digital vulnerability describes a universal state of defencelessness and susceptibility to (the exploitation of) power imbalances that are the result of the increasing automation of commerce, datafied consumer–seller relations, and the very architecture of digital marketplaces. Digital vulnerability, we argue, is architectural, relational, and data-driven. Based on our concept of digital vulnerability, we demonstrate how and why using digital technology to render consumers vulnerable is the epitome of an unfair digital commercial practice.

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