Understanding epistemic relevance

Abstract

Agents require a constant flow, and a high level of processing, of relevant semantic information, in order to interact successfully among themselves and with the environment in which they are embedded. Standard theories of information, however, are silent on the nature of epistemic relevance. In this paper, a subjectivist interpretation of epistemic relevance is developed and defended. It is based on a counterfactual and metatheoretical analysis of the degree of relevance of some semantic information i to an informee/agent a, as a function of the accuracy of i understood as an answer to a query q, given the probability that q might be asked by a. This interpretation of epistemic relevance vindicates a strongly semantic theory of information, according to which semantic information encapsulates truth. It accounts satisfactorily for several important applications and interpretations of the concept of relevant information in a variety of philosophical areas. And it interfaces successfully with current philosophical interpretations of causal and logical relevance.

The Philosophy of Information: Ten Years Later

“…Likewise, astrological data may, accidentally, lead to a scientic discovery but they are not, for this reason, epistemically relevant information. Of course, there are many ways in which misinformation may be indirectly, inferentially or metatheoretically relevant, yet this is not what is in question here” (Floridi 2008b, 84–85).…”

“…It has been treated as obvious by several philosophers who have handled semantic information with all the required care, including Grice, Dretske, and Adams. It does make life easier when dealing with difficult and controversial issues such as some paradoxes about the alleged informativeness of contradictions (they are not informative now because they are false [Floridi 2004]); the link between semantic information and knowledge (knowledge encapsulates truth because it encapsulates semantic information, which, in turn, encapsulates truth, as in a three‐doll matryoshka [Floridi 2006]); or the nature of relevant information [Floridi 2008b]). Despite all this, it would be ungenerous to dismiss the contribution by Scarantino and Piccinini as a fruitless mistake.…”

Recommender Systems and their Ethical Challenges

“…To evaluate the system performance, one may compare the RS's predictions to the actual user behaviour after a recommendation is made. In the domain of news recommendations, a good recommendation may be defined as a news item that is relevant to the user (Floridi, 2008), and one may use click-through rates as a proxy to evaluate the accuracy of the system's recommendations. Similar RS are designed to develop a model of individual users and to use it to predict the users' feedback on the system's recommendation, which is essentially a prediction problem.…”

What the Near Future of Artificial Intelligence Could Be

“…Part of the difficulty is to get the level of abstraction right (Floridi 2008a(Floridi , 2008b, i.e. to identify the set of relevant observables (Bthe seeds of time^) on which to focus because those are the ones that will make the real, significant difference.…”