AI can make online platforms more relevant, help create or summarize content, and process moderation decisions at scale. It does not automatically make a service safer or more personal: outcomes depend on what data systems use, how they rank and moderate content, and whether people can understand or challenge their decisions.
How is AI changing online platforms?
AI affects more than visible chatbots or generated images. It can help determine what appears in a feed, tailor features to a person, create content, and detect or act on material that may violate platform rules. Those uses can make services more useful and help them handle large volumes of content, but they also influence what people see and how their data is used.
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The same system can have different effects depending on its design and context. A recommendation that helps someone find a useful lesson may be unwelcome if it repeatedly surfaces distressing material. Automated moderation may help process reports at scale, but a high volume of automated actions does not by itself show that decisions are accurate or fair.
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Recommender systems select, filter, and order content, contacts, or other items. Their ranking choices shape exposure, not just convenience: deciding what to show prominently also affects what is less likely to be seen. The Australian eSafety Commissioner notes that platforms often prioritize engagement, and that engagement can be positive or negative. Repeated exposure may have cumulative effects beyond the effect of any one post.
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Personalization can also be useful. The UK Information Commissioner’s Office (ICO) identifies potential benefits for workplace productivity and educational outcomes. But adapting an experience to a person requires decisions about personal data: what is collected, how it is used, and what protections apply. The ICO also identifies model-inversion attacks as a risk that can affect more highly personalized models. The OECD likewise treats privacy and AI data governance as connected concerns.
Does AI make recommendations safer or more personal?
Not on its own. A system optimized for interaction may surface material that draws attention without distinguishing whether that attention reflects a good experience. Repeated recommendations can compound exposure, although the effects depend on the system, the content, and the user’s circumstances. It would be too broad to conclude that every algorithmic recommendation is harmful—or that personalization necessarily benefits the person receiving it.
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Safety and personalization can pull in different directions. A system may use more information to tailor results, while that same use raises privacy questions. For younger users, the European Commission’s 2025 guidance on protecting minors under the Digital Services Act (DSA) highlights possible effects of recommender systems on privacy, safety, and security, including the need to address repeated exposure to certain harmful recommendations. This is EU guidance; applicable duties depend on the service category and law that applies.
How do platforms use AI to create content?
Generative AI produces content such as text, images, audio, or video in response to a prompt, according to the U.S. Government Accountability Office (GAO). A platform might make such capabilities available to users or use generative systems in internal workflows. What a particular service does can change, so claims about its current features or data practices should be checked against that service’s own current documentation.
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Generated material can be useful, but it can also be inaccurate or misleading. The presence of a generative feature is therefore not enough to judge a platform’s quality or safety. The relevant questions include what the feature is intended to do, how people are told that content was generated, and what safeguards apply to its use.
How do platforms use AI to moderate content?
Automated systems can help detect content, apply platform rules, and process moderation actions. A European Commission Joint Research Centre analysis of Digital Services Act transparency-database records for very large online platforms (VLOPs) found that a majority of registered moderation actions from 1 April 2024 to 1 April 2025 involved at least partial automation. The analysis described automation initially most often in detection and increasingly in fully automated removal.
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That finding is limited to the reported actions in that dataset, for that group of services and period. It is not a rate for every online platform, and it does not establish whether each action was correct. For users, the quality of moderation also depends on whether decisions can be explained, reviewed, and appealed, and on how a service handles errors.
What safeguards help make AI-mediated services accountable?
Useful evaluation looks at how a service handles specific risks, rather than treating “uses AI” as a safety rating. GAO describes practices among commercial generative-AI developers that include benchmark testing, multidisciplinary review, and red-teaming. Such practices can help identify problems, but their existence is not a guarantee of safety; GAO also discusses limits to testing.
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The European Commission’s 2025 overview of systemic risks for VLOPs and very large online search engines considers risks to minors and the impact of emerging technologies such as generative AI. It draws on platform risk assessments, audits and transparency reports, independent research, and civil-society contributions. This is a developing evidence base, not a final verdict on every service.
- Control: Can people adjust, reset, or opt out of personalization where the service allows it?
- Transparency: Does the service explain why content is recommended and how AI features use data?
- Privacy: Does its documentation explain what data is collected, retained, or used for personalization, and what controls are available?
- Safety: Does it address repeated exposure to harmful material and risks to minors or other vulnerable groups?
- Recourse: Can people report a problem and seek review of an automated decision?
- Evidence: Are safety claims supported by clearly scoped transparency data, audits, or regulator findings?
No single feature answers all of these questions. A control may offer choice without explaining how ranking works; a transparency report may describe actions without establishing their accuracy. Looking at several dimensions gives a more grounded picture than a platform’s AI claims alone.
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