SearcharxivSearch

arXiv subjects

Meica Magnani

Publications and source records attributed to Meica Magnani.

2 recordsLinked to original sources

Aspirational Affordances of AI

As artificial intelligence (AI) systems increasingly permeate processes of cultural and epistemic production, there are growing concerns about how their outputs may confine individuals and groups to restricted narratives about who or what they could be. In this paper, we advance the discourse surrounding these concerns by making three contributions. First, we introduce the concept of aspirational affordance to describe how culturally shared interpretive resources, such as concepts, images, and narratives, can shape individual cognition, and in particular exercises of imagination. We show the usefulness of this concept for grounding the evaluation of psychological risks posed by AI. Second, we provide three reasons for scrutinizing AI's influence on aspirational affordances: AI's influence is potentially more potent, but less public, than that of traditional sources; the influence is not simply incremental, but ecological, transforming the entire landscape of practices that shape aspirational affordances; and it is highly concentrated, with a few corporate-controlled systems mediating a growing portion of production. Our third contribution is to advance such a scrutiny of AI's influence by introducing the concept of aspirational harm. In the context of AI systems, such harms arise when AI-enabled aspirational affordances distort or diminish available interpretive resources in ways that undermine individuals' ability to imagine relevant practical possibilities. Through three case studies, we illustrate how aspirational harms extend the existing discourse on AI-inflicted harms beyond representational and allocative harms, warranting separate attention. Overall, this paper aims to advance our understanding of the psychological and societal stakes of AI in shaping individual and collective aspirations.

cs.CY

The Effectiveness of Embedded Values Analysis Modules in Computer Science Education: An Empirical Study

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern's program that embeds values analysis modules into CS courses. The resulting data suggest that such modules have a positive effect on students' moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

cs.CY