Intelligence in light of perspectivalism and AI responsibilization

Journal of responsible technology Pub Date : 2022-12-01 DOI:10.1016/j.jrt.2022.100047

Christian Hugo Hoffmann

{"title":"Intelligence in light of perspectivalism and AI responsibilization","authors":"Christian Hugo Hoffmann","doi":"10.1016/j.jrt.2022.100047","DOIUrl":null,"url":null,"abstract":"<div><p>What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (<span>Hoffmann, 2022b</span>). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.</p></div>","PeriodicalId":73937,"journal":{"name":"Journal of responsible technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666659622000245/pdfft?md5=085f80040985d3e9c57290c7d930a69a&pid=1-s2.0-S2666659622000245-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of responsible technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666659622000245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (Hoffmann, 2022b). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于透视主义和人工智能责任的智能

合成“AI”中的I代表什么?这个指导性问题的答案似乎很简单，那就是智力;然而，乍一看，人们可能会想知道智力到底是什么。尽管心理学、神经科学和动物认知研究等领域都给出了智力的定义，但这些粗糙的方法被证明过于系统化、僵化和简化(Hoffmann, 2022b)。与此同时，新型人工智能(AI)，从社交机器人到认知助手，正在激发人们对新答案的需求，以便与其他类型的智能进行有意义的比较。在本文中，我们致力于解决这一需求，提出了一个开放的、可扩展的、松散的框架，用于理解人类、其他动物和人工智能的智能，该框架最终基于因果学习作为智能的中心主题。目标是展示这个框架对人工智能责任的工具价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊