The information age has bought both bounty and pestilence. Today, we are deluged with both correct and incorrect information. If we knew how to tell apart correct claims from incorrect, we would have inched that much closer to utopia. But the lack of nous in telling apart generally ‘obvious’ incorrect claims from correct claims has brought us close to the precipice of disarray. Thus, improving people’s ability to identify untrustworthy claims as such takes on urgency.
http://gojiberries.io/2020/08/31/the-misinformation-age-measuring-and-improving-digital-literacy/
Inferring the Quality of Evidence Behind the Claims: Fact Check and Beyond
One way around misinformation is to rely on an expert army that assesses the truth value of claims. However, assessing the truth value of a claim is hard. It needs expert knowledge and careful research. When validating, we have to identify with which parts are wrong, which parts are right but misleading, and which parts are debatable. All in all, it is a noisy and time-consuming process to vet a few claims. Fact check operations, hence, cull a small number of claims and try to validate those claims. As the rate of production of information increases, thwarting misinformation by checking all the claims seems implausibly expensive.
Rather than assess the claims directly, we can assess the process. Or, in particular, the residue of one part of the process for making the claim—sources. Except for claims based on private experience, e.g., religious experience, claims are based on sources. We can use the features of these sources to infer credibility. The first feature is the number of sources cited to make a claim. All else equal, the more number of sources saying the same thing, the greater the chances that the claim is true. None of this is to undercut a common observation: lots of people can be wrong about something. A harder test for veracity if a diverse set of people say the same thing. The third test is checking the credibility of the sources.
Relying on the residue is not a panacea. People can simply lie about the source. We want the source to verify what they have been quoted as saying. And in the era of cheap data, this can be easily enabled. Quotes can be linked to video interviews or automatic transcriptions electronically signed by the interviewee. The same system can be scaled to institutions. The downside is that the system may prove onerous. On the other hand, commonly, the same source is cited by many people so a public repository of verified claims and evidence can mitigate much of the burden.
But will this solve the problem? Likely not. For one, people can still commit sins of omission. For two, they can still draft things in misleading ways. For three, trust in sources may not be tied to correctness. All we have done is built a system for establishing provenance. And establishing the provenance is not enough. Instead, we need a system that incentivizes both correctness and presentation that makes correct interpretation highly likely. It is a high bar. But it is the right bar—correct and liable to be correctly interpreted.
To create incentives for publishing correct claims, we need to either 1. educate the population, which brings me to the previous post, or 2. find ways to build products and recommendations that incentivize correct claims. We likely need both.