Cognitive architectures for artificial intelligence ethics

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also.

Disembodied information

Purpose

Contemporary practices of information management tend to approach information as discrete and decontextualised units. The creation and capture of electronically generated metadata, specific to individual transactions, have become a primary concern of the archival and records management literature. The prevalent model of discrete metadata capture lends itself easily to automation, but it cannot emulate the intellectual control offered by traditional classification structures such as file plans. The purpose of this paper is to provide a critical review of the literature.

Design/methodology/approach

The paper provides a critical review of literature.

Findings

Recognition of contextual structures and relationships cannot at present be automated, natural language processing capabilities are poor, and metadata can easily become decoupled from “disembodied” discrete units of information. Discrete metadata capture has been developed in the context of commercial transactions rather than information management.

Practical implications

File plans as explicit organisations of knowledge can be used to generate contextually significant metadata for records. Such metadata may then be of considerable value to digital curation processes.

Originality/value

This critique will be useful in considering practical approaches to metadata capture.