26 citations · 86 across the 9 of their papers we have counts for
10 papers
Digital Labor Platforms as Infrastructure of Extractivism
Julian Posada
This chapter examines how digital platforms, as infrastructures of extractivism in contemporary artificial intelligence, enable corporations to appropriate data and labour while co…
The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
Sheza Munir, Benjamin Mah, Krisha Kalsi +5
In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" paradigm rests on a positivisti…
The Human Factor in AI Red Teaming: Perspectives from Social and Collaborative Computing
Alice Qian Zhang, Ryland Shaw, Jacy Reese Anthis +9
Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI…
Documenting Data Production Processes: A Participatory Approach for Data Work
Milagros Miceli, Tianling Yang, Adriana Alvarado Garcia +4
The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized che…
The Data-Production Dispositif
Milagros Miceli, Julian Posada
Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evalu…
Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?
Milagros Miceli, Julian Posada, Tianling Yang
Research in machine learning (ML) has primarily argued that models trained on incomplete or biased datasets can lead to discriminatory outputs. In this commentary, we propose movin…
Wisdom for the Crowd: Discoursive Power in Annotation Instructions for Computer Vision
Milagros Miceli, Julian Posada
Developers of computer vision algorithms outsource some of the labor involved in annotating training data through business process outsourcing companies and crowdsourcing platforms…
The Coloniality of Data Work in Latin America
Julian Posada
This presentation for the AIES 21 doctoral consortium examines the Latin American crowdsourcing market through a decolonial lens. This research is based on the analysis of the web…
We Haven't Gone Paperless Yet: Why the Printing Press Can Help Us Understand Data and AI
Julian Posada, Nicholas Weller, Wendy H. Wong
How should we understand the social and political effects of the datafication of human life? This paper argues that the effects of data should be understood as a constitutive shift…
The Future of Work Is Here: Toward a Comprehensive Approach to Artificial Intelligence and Labour
Julian Posada
This commentary traces contemporary discourses on the relationship between artificial intelligence and labour and explains why these principles must be comprehensive in their appro…