This chapter focuses on the role of digital intermediaries in shaping technology, society, and economy under what Casilli and Posada call “the paradigm of the platform.” They trace the historical relationship between platforms, markets, and enterprises to demonstrate the role of algorithms in matching users, pieces of software, goods, and services, and how platforms can create value from the content and data generated by users. Their primary argument is that platforms play a fundamental role in establishing a digital labor relationship with their users by allocating underpaid or unpaid tasks to them. In order to enable and coordinate users’ contributions, platforms need to standardize and fragment (“taskify”) labor processes. The authors conclude by highlighting the link between platformization and automation, with the tech giants employing their users’ data to produce artificial intelligence and machine-learning solutions to an expanding range of problems.

High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis

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Research in machine learning (ML) has 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 research community \,---\,one bias-centered, the other power-aware. 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.

In many robotic processes such as milling and drilling, there are multiple solutions for robot poses, as the rotation around the tool axis remains as a degree of freedom (DoF) in positioning. Yet until now, this DoF causes additional efforts in CAM programming as it requires manual intervention. Instead, this DoF can be used to optimize the robot pose according to different criteria of the robot such as stiffness or avoidance of backlash effects. This paper presents different criteria for optimization of the robot pose in machining and describes the optimization of robot stiffness based on a novel method for its identification, which is in detail described on this paper. Furthermore, the potential of the automatic resolution of the DoF is outlined enabling staff without robot knowledge to define reasonable robot paths