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 moving the research focus beyond bias-oriented framings by adopting a power-aware perspective to study up ML datasets. This means accounting for historical inequities, labor conditions, and epistemological standpoints inscribed in data. We draw on HCI and CSCW work to support our argument, critically analyze previous research, and point at two co-existing lines of work within our community -- one bias-oriented, the other power-aware. This way, we highlight the need for dialogue and cooperation in three areas: data quality, data work, and data documentation. In the first area, we argue that reducing societal problems to bias misses the context-based nature of data. In the second one, we highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets. Finally, we propose expanding current transparency-oriented efforts in dataset documentation to reflect the social contexts of data design and production.
Developers of computer vision algorithms outsource some of the labor involved in annotating training data through business process outsourcing companies and crowdsourcing platforms. Many data annotators are situated in the Global South and are considered independent contractors. This paper focuses on the experiences of Argentinian and Venezuelan annotation workers. Through qualitative methods, we explore the discourses encoded in the task instructions that these workers follow to annotate computer vision datasets. Our preliminary findings indicate that annotation instructions reflect worldviews imposed on workers and, through their labor, on datasets. Moreover, we observe that for-profit goals drive task instructions and that managers and algorithms make sure annotations are done according to requesters commands. This configuration presents a form of commodified labor that perpetuates power asymmetries while reinforcing social inequalities and is compelled to reproduce them into datasets and, subsequently, in computer vision systems.
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 in social and political relations. We explore how datafication, or quantification of human and non-human factors into binary code, affects the identity of individuals and groups. This fundamental shift goes beyond economic and ethical concerns, which has been the focus of other efforts to explore the effects of datafication and AI. We highlight that technologies such as datafication and AI (and previously, the printing press) both disrupted extant power arrangements, leading to decentralization, and triggered a recentralization of power by new actors better adapted to leveraging the new technology. We use the analogy of the printing press to provide a framework for understanding constitutive change. The printing press example gives us more clarity on 1) what can happen when the medium of communication drastically alters how information is communicated and stored; 2) the shift in power from state to private actors; and 3) the tension of simultaneously connecting individuals while driving them towards narrower communities through algorithmic analyses of data.
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 traffic of ninety-three platforms, interviews with Venezuelan data workers of four platforms, and the analysis of the documentation issued by these organizations. The findings show that (1) centuries-old global divisions of labor persist, in this case, with requesters located in advanced economies and workers in the Global South. (2) That the platforms configuration of the labor process constrains the agency of these workers when producing annotations. And, (3) that ideologies originating from the Global North serve to legitimize and reinforce this global labor market configuration.
This commentary traces contemporary discourses on the relationship between artificial intelligence and labour and explains why these principles must be comprehensive in their approach to labour and AI. First, the commentary asserts that ethical frameworks in AI alone are not enough to guarantee workers rights since they lack enforcement mechanisms and the representation of different stakeholders. Secondly, it argues that current discussions on AI and labour focus on the deployment of these technologies in the workplace but ignore the essential role of human labour in their development, particularly in the different cases of outsourced labour around the world. Finally, it recommends using existing human rights frameworks for working conditions to provide more comprehensive ethical principles and regulations. The commentary concludes by arguing that the central question regarding the future of work should not be whether intelligent machines will replace humans, but who will own these systems and have a say in their development and operation.
