Relational Artificial Intelligence

Abstract

The impact of Artificial Intelligence does not depend only on fundamental research and technological developments, but for a large part on how these systems are introduced into society and used in everyday situations. Even though AI is traditionally associated with rational decision making, understanding and shaping the societal impact of AI in all its facets requires a relational perspective. A rational approach to AI, where computational algorithms drive decision making independent of human intervention, insights and emotions, has shown to result in bias and exclusion, laying bare societal vulnerabilities and insecurities.A relational approach, that focus on the relational nature of things, is needed to deal with the ethical, legal, societal, cultural, and environmental implications of AI. A relational approach to AI recognises that objective and rational reasoning cannot does not always result in the 'right' way to proceed because what is 'right' depends on the dynamics of the situation in which the decision is taken, and that rather than solving ethical problems the focus of design and use of AI must be on asking the ethical question.In this position paper, I start with a general discussion of current conceptualisations of AI followed by an overview of existing approaches to governance and responsible development and use of AI. Then, I reflect over what should be the bases of a social paradigm for AI and how this should be embedded in relational, feminist and non-Western philosophies, in particular the Ubuntu philosophy.

Un an après l’arrivée de ChatGPT: réflexions de l’Obvia sur les enjeux et pistes d’action possibles face à l’IA générative

“…En effet, au cours des dix dernières années, on a vu émerger un grand nombre de déclarations de valeurs et chartes éthiques proposées par différents pays et organismes multipartites (Jobin et al 2019;Tidjon et Khomh, 2022) qui ont justement pour objectif d'énoncer quelles sont ces valeurs auxquelles nous tenons et sur lesquelles nous souhaitons voir l'IA « s'aligner » pour être considéré « responsable ». Malgré quelques angles morts et une effectivité somme toute limitée de par leur nature volontaire et non-contraignante (Jobin et al, 2019;Mittelstadt, 2019;Greene et al, 2019;Buruk et Al., 2020;Adams, 2021;Dignum, 2022), ces déclarations et chartes ont le mérite d'expliciter les valeurs et principes importants non seulement selon le point de vue des entreprises qui produisent de l'IA, mais aussi pour les autres acteurs sociaux qui seront impactés d'une façon ou d'une autre par le déploiement de l'IA dans nos sociétés. Certaines de ces chartes et déclarations ont d'ailleurs été produites sur la base de consultations de la population, comme c'est notamment le cas de la Déclaration de Montréal pour une développement responsable de l'IA (2018).…”

Navigating the ethical terrain of AI in higher education: Strategies for mitigating bias and promoting fairness

“…This strategy necessitates addressing challenges in data-driven AI, particularly those arising from big data and advanced machine learning algorithms, and the potential for biased outcomes due to data collection and processing practices related to demographic factors like race and sex. Along the same lines, Dignum [44] explores the broader societal implications of AI, proposing a relational approach that considers the ethical, legal, societal, cultural, and environmental ramifications of AI technologies. This approach is based on the understanding that objective and rational reasoning does not always lead to the most appropriate outcomes, as the 'right' decision often varies depending on the specific context and dynamics at play.…”

AI ageism: a critical roadmap for studying age discrimination and exclusion in digitalized societies

“…Therefore, by introducing the concept of AI ageism, I propose to go beyond the usage of "bias" as the dominant epistemological tool for understanding the negative effects of algorithmic models and systems. This concept proposes a broader socio-technical inquiry of different forms of ageism existing in AI understood as a socio-technical ecosystem (Dignum 2022). The working definition of AI ageism, open to empirical and theoretical refinement, conveys the following: practices and ideologies operating within the field of AI which exclude, discriminate, or neglect the interests, experiences, and needs of older populations and have or might have disparate impacts on age equality.…”

“…There is a plethora of ways AI had been defined, from simple formulations to complex definitions relating to the technological, scientific, and societal implications of the systems. This article follows the understanding of AI as a "socio-technical ecosystem," which recognizes the interaction between people and technology and how complex infrastructures affect and are affected by society and by human behaviour (Dignum 2022). Machine learning can be defined broadly as a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data (Molina and Garip 2019) and, more specifically, a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed (Brown 2021).…”