Search arXivSearch

arXiv · 2403.18623

Antitrust, Amazon, and Algorithmic Auditing

Abstract

In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone's life. Unlike traditional markets, market participant behavior is easily observable in these markets. We present a series of empirical investigations into the extent to which Amazon engages in practices that are typically described as self-preferencing. We discuss how the computer science tools used in this paper can be used in a regulatory environment that is based on algorithmic auditing and requires regulating digital markets at scale.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Jens Frankenreiter, Stefan Bechtold, Krishna P. Gummadi. 2024-04-25. Antitrust, Amazon, and Algorithmic Auditing. https://arxiv.org/abs/2403.18623

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution

We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA-CHES projection models, which map responses onto three validated dimensions (lrgen, lrecon, galtan) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain-of-thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one-third the spread of major European parties. Supported by a multi-trait multi-method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available at https://github.com/sakhadib/LLM-Ideoplasticity.

cs.CY

Students' Perceptions of Peer Grading

Peer grading is widely used in education, yet it elicits mixed reactions from educators and students. Although many studies have examined students' views of peer grading, their findings are scattered, and no clear overall picture has emerged. To address this gap, we conducted a mixed-source thematic analysis of literature and student discussions on Reddit. To scale our analysis of the Reddit data, we fine-tuned a Gemini 2.5 text-classification model to classify an initial dataset of 659 posts and 6,607 comments by relevance. Manual review of the items classified as relevant by the model yielded a final dataset of 114 posts and 300 comments. Drawing on evidence from 107 papers and the Reddit dataset, we found that students perceive peer grading as both beneficial and problematic. Positive perceptions included learning and understanding benefits, skill development, engagement, and collaboration, while negative perceptions centered on unreliable grading, unfairness, weak feedback quality, emotional stress, and workload. Reddit discussions also suggested an emerging concern that remains underexplored in the literature: AI use in peer grading may weaken students' trust in the accuracy and authenticity of the process. We further identified eight mitigation strategies and mapped them to the negative perceptions they help address. Among these, instructor oversight and training played the most central role.

cs.CY

Work, Wellbeing, and Choice: Empirical Lessons for AI Futures

Advances in AI-driven automation have raised questions about how humans might find wellbeing in a world where paid employment is less necessary or less available than before. Paid work has been variously characterized as both a contributor and an impediment to human wellbeing. What is already known about the relationship between paid work and wellbeing? What factors influence wellbeing among people who do not work---or who do not need to work? And how might these factors bear upon prospective AI-induced economic transformations? To help provide empirical grounding for these questions, we survey the psychological, sociological, and economic literature that investigates the relationship between wellbeing and work. We draw on evidence from multiple populations, including the unemployed, retirees, lottery winners, and financially dependent spouses. This comparative review draws from studies across OECD countries, China, India, and Gulf states. We identify three key factors that mediate the relationship between work status and wellbeing: (1) agency and choice---whether the exit from work is voluntary or involuntary, as well as long-term agency; (2) the availability of alternative sources of work's latent benefits---such as volunteering, hobbies, or state-provisioned employment; and (3) social and systemic context---including cultural norms around work and the robustness of social safety nets. We draw on these three factors to derive specific implications for different AI automation scenarios, connecting the empirical evidence to concrete policy considerations.

cs.CY