When Curation Becomes Creation

Algorithms, Microcontent, and the Vanishing Distinction between Platforms and Creators

Authors: Liu Leqi, Dylan Hadfield-Menell, and Zachary C. Lipton

To appear in Communications of the ACM (CACM) and available on arXiv.org.

Ever since social activity on the Internet began migrating from the wilds of the open web to the walled gardens erected by so-called platforms (think Myspace, Facebook, Twitter, YouTube, or TikTok), debates have raged about the responsibilities that these platforms ought to bear. And yet, despite intense scrutiny from the news media and grassroots movements of outraged users, platforms continue to operate, from a legal standpoint, on the friendliest terms. 

You might say that today’s platforms enjoy a “have your cake, eat it too, and here’s a side of ice cream” deal. They simultaneously benefit from: (1) broad discretion to organize (and censor) content however they choose; (2) powerful algorithms for curating a practically limitless supply of user-posted microcontent according to whatever ends they wish; and (3) absolution from almost any liability associated with that content.

Today’s platforms play an increasingly active role in shaping what people see, arguably creating derivative media products of their own.

This favorable regulatory environment results from the current legal framework, which distinguishes between intermediaries (e.g., platforms) and content providers. This distinction is ill-adapted to the modern social media landscape, where platforms deploy powerful data-driven algorithms (so-called AI) to play an increasingly active role in shaping what people see and where users supply disconnected bits of raw content (tweets, photos, etc.) as fodder. 

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Notes on Response to “The AI Misinformation Epidemic”

On Monday, I posted an article titled The AI Misinformation Epidemic. The article introduces a series of posts that will critically examine the various sources of misinformation underlying this AI hype cycle.

The post came about for the following reason: While I had contemplated the idea for weeks, I couldn’t choose which among the many factors to focus on and which to exclude. My solution was to break down the issue into several narrower posts. The AI Machine Learning Epidemic introduced the problem, sketched an outline for the series, and articulated some preliminary philosophical arguments.

To my surprise, it stirred up a frothy reaction. In a span of three days, the site received over 36,000 readers. To date, the article received 68 comments on the original post, 274 comments on hacker news, and 140 comments on machine learning subreddit.

To ensure that my post contributes as little novel misinformation as possible, I’d like to briefly address the response to the article and some common misconceptions shared by many comments. Continue reading “Notes on Response to “The AI Misinformation Epidemic””

The AI Misinformation Epidemic

Interest in machine learning may be at an all-time high. Per Google Trends, people are searching for machine learning nearly five times as often as five years ago. And at the University of California San Diego (UCSD), where I’m presently a PhD candidate, we had over 300 students enrolled in both our graduate-level recommender systems and neural networks courses.

Much of this attention is warranted. Breakthroughs in computer vision, speech recognition, and, more generally, pattern recognition in large data sets, have given machine learning substantial power to impact industry, society, and other academic disciplines.

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