Year: 2023
Type: conference-paper
Abstract: Neuro-symbolic integration of symbolic and subsymbolic techniques represents a fast-growing AI trend aimed at mitigating the issues of neural networks in terms of decision processes, reasoning, and interpretability.Several state-of-theart neuro-symbolic approaches aim at improving performance, most of them focusing on proving their effectiveness in terms of raw predictive performance and/or reasoning capabilities.Meanwhile, few efforts have been devoted to increasing model trustworthiness, interpretability, and efficiency-mostly due to the complexity of measuring effectively improvements in terms of trustworthiness and interpretability.This is why here we analyse and discuss the need for ad-hoc trustworthiness metrics for neurosymbolic techniques.We focus on two popular paradigms mixing subsymbolic computation and symbolic knowledge, namely: (i) symbolic knowledge extraction (SKE), aimed at mapping subsymbolic models into human-interpretable knowledge bases; and (ii) symbolic knowledge injection (SKI), aimed at forcing subsymbolic models to adhere to a given symbolic knowledge.We first emphasise the need for assessing neuro-symbolic approaches from a trustworthiness perspective, highlighting the research challenges linked with this evaluation and the need for ad-hoc trust definitions.Then we summarise recent developments in SKE and SKI metrics focusing specifically on several trustworthiness pillars such as interpretability, efficiency, and robustness of neuro-symbolic methods.Finally, we highlight open research opportunities towards reliable and flexible trustworthiness metrics for neuro-symbolic integration.
DOI: 10.15439/2023f6019
Authors: Andrea Agiollo, Andrea Omicini
Institution: University of Bologna
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G1131269346, G4508289328
OpenAlex ID: W4387619784