The rise of “fake news,” misinformation, disinformation, digital falsehoods and foreign influence coincides with society’s increasing outsourcing of its thinking to algorithms. We no longer actively explore the informational landscape, we passively await the decisions of algorithms to tell us what to read, what to watch, what to share and what to buy. Whereas search algorithms once decided for us what the most important Websites were, today we no longer even play a role in what we consume, merely sitting passively in our chairs as infinitely scrolling streams of content are force-fed us by algorithms. Even scholars and scientists no longer spend their days immersed in the literature of their fields, they let Google Scholar tell them what papers to cite. As smart speakers eliminate the last shreds of informational context from our lives, will information literacy completely fade away?
The outsourcing of thought to machines dates back to the origins of the computing revolution, as the science fiction canon of the era touted a brave new world in which machines would do society’s thinking for it. The outcomes of this intellectual outsourcing, as seen through the years of science fiction, have ranged from enlightenment, with humans free to focus on unfettered creativity, to slow or sudden annihilation, as those intelligent machines see their creators as competition or useless nuisances.
The digital world was supposed to put the informational riches of the world at our fingertips. Every piece of information published in the history of humanity was to be available with a mouse click at precisely the moment we needed it. The early Web was presented as a globalized extension of the then-user-centric computing world. Users would turn to their computers with an informational need and the machine would act as a tool to assist them in locating the documents they needed, much as machines had done since the dawn of the keyword search.
The information seeking process of the early Web mirrored that of the traditional offline world, with humans driving the process and the machine merely offering a keyword search alternative to subject tags and a larger available collection of content.
Over time, however, machines inevitably took over the role of gatekeeper, with their scoring algorithms deciding what was the most “relevant” and “reputable.” From sorting results by mere keyword density to incorporating myriad and increasingly personalized metrics, machines began to decide what we should see and not see.
Web users willingly placed their informational needs in the hands of these all-powerful algorithms, only too happy to let a machine make the hard judgement calls about relevance and reputation. Rather than spend hours scrolling through search results, comparing and contrasting each entry and researching its provenance and context, users simply clicked on the first search result and moved on, trusting that an opaque black box algorithm somewhere had magically selected the “best” result out of everything on the entire Web and done so in the blink of an eye.
Even academic literature reviews, once a revered component of the research process, have rapidly devolved into quick keyword searches of Google Scholar, leaving it to Google’s algorithms to inadvertently arbitrate what constitutes the most “significant” findings of an entire field’s literature.
The rise of smart speakers has increased our dependency on algorithms, as we no longer can see beyond the first search result. When we ask our digital assistant for an answer, we get only an answer plucked from the open Web, without the benefit of knowing that had we run that search ourselves using a Web browser we would have seen that every other search result contradicts the answer of the top site that was used to surface the answer we heard.
The end result is that we have become ever more disconnected from the sources of the information we use and the decision-making process of how those sources are scored, sorted and filtered to present to us.
As we increasingly outsource our thinking to algorithms, we are losing our information literacy and the skills necessary to think critically about the information we see.
Rather than take a skeptical view of what we find online and seek out alternative viewpoints, we blindly trust that the algorithms powering the modern Web are giving us the best results.
Algorithms that are optimized to hold our attention and deliver the most addictive information are being trusted by the public to deliver unbiased results that represent the “best” available information.
Increasingly we no longer even actively search for information. We merely sign into social platforms and allow ourselves to be passively force-fed an endless stream of what profit-optimized algorithms believe will addict us the most.
These social media algorithms are optimized for virality and addictiveness, rather than truthfulness and evidentiary reporting. The more emotional, fact-free and false a post is, the more likely it is to be pushed viral by these algorithms.
Putting this all together, as we have outsourced our thinking to algorithms, we have set aside our search for enlightenment and placed ourselves in the hands of algorithms optimizing for entertainment.
The information we see is no longer based on what is the most relevant or useful to us, but rather what will extract our attention for the longest period of time in order to show us the most ads.
In other words, we are not outsourcing our thinking to algorithms designed to surface the “best” information. We are outsourcing our thinking to algorithms designed to moderate, monetize, manipulate and mine us.
These algorithms make decisions for us based on what makes us the most economically valuable to their creators, rather than what is best for us. Digital falsehoods may be bad for society, but they are economic gold for social media companies and thus prioritized by their algorithms for us.
In the end, in many ways our modern epidemic of digital falsehoods exists because we have become so reliant on technology that we’ve stopped teaching our citizenry how to think.
