Given that interpretive blindness stems from relying on a restricted set of testimony sources, what practical strategies could an individual or a society employ to actively combat this bias in a modern information landscape dominated by social media and specialized interest groups?

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ion to few sources of testimony and a natural co-dependence between beliefs and interpretation (Asher and Paul, 2018 ) . Relying on testimony from a restricted set of sources to update one’s beliefs leads to the mutual reinforcement of our confidence in the source and our belief in ; this creates a bias that can preclude learning when an agent tries to exploit new data that are incompatible with or simply distinct from . Agents who are interpretively blind will discount any evidence that challenges their beliefs. We use Wolpert’s 2018 extended Bayesian framework to prove our results. While IB…

humans (and perhaps soon by sophisticated machine learning algorithms) whose beliefs are guided and shaped by testimony. When learning through testimony—perhaps the primary way that most people acquire information nowadays—an agent acquires beliefs through conversations with other agents, or from books, newspapers or social networks, and so on. Typically, such people lack direct access to the phenomena described via that testimony. Typically too, humans only pay attention to a restricted set of bodies of testimony from a limited number of sources for their information—which makes sense in…

body of evidence can give a sense of community, as has been amply documented in the scholarly literature and the press. But this trade off can lead to a problem in learning: when we rely on testimony to learn and we restrict the testimony we pay attention to, the confirming evidence for the evaluation hypothesis and what it supports threaten to collapse into one. We now turn to see how iterated Bayesian updating in learning from testimony can ultimately lead to a situation where only evaluation hypotheses supporting our restricted evidence are credible and this leads to IB. 3 IB in a first…

Interpretive Blindness Nicholas Asher 1 1 1 CNRS, IRIT and Julie Hunter 2 2 2 Linagora GSO Abstract We model here an epistemic bias we call interpretive blindness (IB). IB is a special problem for learning from testimony, in which one acquires information only from text or conversation. We show that IB follows from a co-dependence between background beliefs and interpretation in a Bayesian setting and the nature of contemporary testimony. We argue that a particular characteristic contemporary testimony, argumentative completeness , can preclude learning in hierarchical Bayesian settings, even…

predictive accuracy of a testimony source, or the extent to which testimony from other sources agrees with its content. One could also require a longer or more thorough exploration of the data about the phenomenon before the agent’s restricting himself to a small subset for exploitation (once again an application of the work in Cesa-Bianchi and Lugosi ( 2006 ) ). All of these ideas and more have been proposed. Simply requiring evaluation hypotheses that obey exogenous constraints, however, begs the question of why should accept them. In fact, the interdependence of testimony, new information…