{"id":7062095,"date":"2026-09-20T12:54:16","date_gmt":"2026-09-20T12:54:16","guid":{"rendered":"https:\/\/som2nypost.com\/uncategorized\/the-filter-problem-conferences-that-work\/"},"modified":"2026-09-20T12:54:16","modified_gmt":"2026-09-20T12:54:16","slug":"the-filter-problem-conferences-that-work","status":"publish","type":"post","link":"https:\/\/som2nypost.com\/?p=7062095","title":{"rendered":"The filter problem : Conferences That Work"},"content":{"rendered":"


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\"The
The filter problem: deciding what to let through when the information torrent becomes overwhelming.<\/figcaption><\/figure>\n

In 2009, I invited the author, technologist, and speaker, David Weinberger<\/a> to keynote the edACCESS<\/a> annual conference, where he spoke on the topic \u201cWhat knowledge is becoming in an age of abundance<\/em>\u201c. At the end of his presentation, I asked David how humans could filter the torrent of information becoming increasingly available to us, so we wouldn\u2019t be overwhelmed by irrelevant or trivial information and could focus on what we wanted to see.<\/p>\n

David looked at me for a long moment and said, \u201cI think this is going to be the defining problem of the internet in the future.\u201d<\/p>\n

He was right.<\/p>\n

Since that question and answer 17 years ago, the filter problem has become a dominant issue, whether we search for useful and relevant information online or rely on automated feeds.<\/p>\n

When I spoke with David, we were both thinking about how to handle the sheer volume of information that would become available online. As it turns out, that was the simplest part of the filter problem. Unscrupulous people discovered they could make money posting lies and misinformation that credible people couldn\u2019t distinguish from the facts.<\/p>\n

Slowly but surely, hastened by the rise and intrusion of advertising online, organizations and individuals began paying good money to boost their messages, reliable or not, to the top of search results.<\/p>\n

And then came AI.<\/p>\n

How do I find the information I need?<\/h2>\n

The original filter problem was fundamentally an information retrieval problem.<\/p>\n

More and more information came online. Search engines helped us find what we wanted. RSS readers, bookmarks, directories, recommendation systems, and eventually social-media feeds helped us cope with the growing volume.<\/p>\n

The implicit assumption was that somewhere in the torrent was good information, and our principal problem was finding it.<\/p>\n

Though this was already a difficult problem, it was conceptually straightforward.<\/p>\n

Find the relevant information.<\/em><\/p>\n

But finding relevant information turned out to be only the beginning.<\/p>\n

How do I know the information is trustworthy?<\/h2>\n

The problem changed when the internet became an economically valuable distribution system.<\/p>\n

Now there was an incentive to manipulate the information that people encountered. Search engine optimization, link farming, clickbait, advertising, political propaganda, fabricated stories, and, eventually, coordinated disinformation all exploited the fact that being seen is valuable.<\/p>\n

The filter therefore acquired a second job:<\/p>\n

Is this information relevant and trustworthy?<\/em><\/p>\n

Filters could no longer rank information solely by relevance. They increasingly had to make judgments about credibility. Unfortunately, it\u2019s hard to make judgments about the trustworthiness of information when its source potentially knows more about the topic than you do<\/a> (or, at least, claims to).<\/p>\n

The problem became:<\/p>\n

Find relevant information. Then determine whether it deserves to be trusted.<\/em><\/p>\n

But there was another problem. Someone had to build the filters.<\/p>\n

Who decides what I should see?<\/h2>\n

The organizations doing the filtering aren\u2019t neutral librarians. They\u2019re businesses with their own objectives.<\/p>\n

Google doesn\u2019t merely answer the question \u201cWhat information is most useful to this person?\u201d A social-media platform doesn\u2019t merely answer \u201cWhat would this person find interesting?\u201d Their algorithms operate within economic systems designed to maximize particular outcomes\u2014advertising revenue, engagement, retention, subscriptions, growth, and so on.<\/p>\n

The people who build our filters inevitably embed their own objectives.<\/p>\n

And that\u2019s true whether those objectives are commercial, political, ideological, or simply technological.<\/p>\n

And the enshittification<\/a> of our filters follows.<\/p>\n

A concrete example<\/h3>\n

Years before generative AI, researchers found that Google searches for \u201csupplements for cancer\u201d produced a mixture of high- and low-quality information<\/a>. Only about one-quarter of the 160 results they evaluated were in their highest-quality category, and 496 advertisements appeared alongside them. They found no statistically significant relationship between the quality of a result and its position in the search results.<\/p>\n

And then came AI<\/h2>\n

AI changes the filter problem because it simultaneously affects the amount of information, the cost of producing it, the quality of the filtering, and our relationship with the filter itself.<\/p>\n

AI makes the torrent vastly larger<\/h3>\n

Generative AI has made producing plausible text, images, audio, and video extraordinarily cheap.<\/p>\n

The old information-abundance problem assumed humans produced most of the information.<\/p>\n

That assumption is becoming increasingly questionable.<\/p>\n

A single person\u2014or increasingly, an automated system\u2014can generate enormous quantities of apparently individualized content.<\/p>\n

So the input to the filter is no longer merely an abundance of human-created information. It increasingly includes information produced by machines at machine scale.<\/p>\n

Here\u2019s an example. A 2025 study examined more than a thousand YouTube and TikTok videos about biomedical topics<\/a> and identified 57 that appeared to be low-quality AI-generated material. The problems weren\u2019t merely stylistic. Researchers found factual errors, missing context, misleading framing, garbled text, and disorganized explanations. The AI-generated videos had viewership, like, and comment rates that were not significantly different from those of the overall sample.<\/p>\n

The torrent is no longer merely something we have to filter. It is something machines can manufacture.<\/p>\n

Can AI filter the torrent?<\/h3>\n

At first sight, AI is potentially an extraordinarily powerful filter.<\/p>\n

It can summarize, cluster, compare, extract, classify, identify patterns, find relationships, and reduce thousands of documents to a manageable set.<\/p>\n

But there is a fundamental difference between using AI to help me filter information and trusting it to tell me what\u2019s true or important.<\/p>\n

I have two simple tests for trusting a person or system: Can I ask for independent references? And will it tell me when it doesn\u2019t know?<\/a><\/p>\n

LLMs can sometimes provide references, but you still have to check those references. Research has also found that people tend to overestimate the accuracy of LLM answers<\/a>, particularly when the systems provide confident or lengthy explanations.<\/p>\n

That makes trusting an LLM as the final arbiter of a torrent of information a crapshoot.<\/p>\n

Who filters the filter?<\/h2>\n

When Google gives me thousands of search results, I know\u2014at least conceptually\u2014that I am looking at a search engine\u2019s selection of documents.<\/p>\n

When ChatGPT gives me five paragraphs answering a question, I am receiving a synthesized condensation of what the system considers relevant.<\/p>\n

The filter has moved much closer to becoming the answer, and that creates many new<\/em> problems:<\/p>\n