Machine Learning (ML) is increasingly accessible to users with limited knowledge of its theoretical foundations. However, misapplying it can lead to negative consequences. This paper reports on a qualitative study designed to reveal challenges that novices encounter when learning about basic ML concepts and building their first models. Twenty participants were introduced to fundamental ML concepts for classification through an interactive tutorial involving an off-the-shelf GUI application, built their own ML model for a shape gesture dataset, and participated in a semi-structured interview. A thematic analysis revealed insights into these challenges, particularly around problem selection and multi-dimensionality, but also around what constitutes ML, algorithm selection , cross-validation, and interpreting visualizations. Despite these and other misconceptions, participants reflected on good model building practices, discussing that algorithm selection might require knowledge and context and that input features may introduce bias. We discuss the findings’ implications for the design of ML tools for novices.

The increasing adoption of machine learning (ML) raises ethical concerns, particularly regarding bias. This study explores how ML practitioners with limited experience in bias understand and apply bias definitions, detection measures, and mitigation methods. Through a take-home task, exercises, and interviews with 22 participants, we identified five key themes: sources of bias, selecting bias metrics, detecting bias, mitigating bias, and ethical considerations. Participants faced unresolved conflicts, such as applying fairness definitions in practice, selecting context-dependent bias metrics, addressing real-world biases, balancing model performance with bias mitigation, and relying on personal perspectives over data-driven metrics. While bias mitigation techniques helped identify biases in two datasets, participants could not fully eliminate bias, citing the oversimplification of complex processes into models with limited variables. We propose designing bias detection tools that encourage practitioners to focus on the underlying assumptions and integrating bias concepts into ML practices, such as using a harmonic mean-based approach, akin to the F1 score, to balance bias and accuracy.

The job of a camera operator is challenging, and potentially dangerous, when filming long moving camera shots. Broadly, the operator must keep the actors in frame while safely navigating around obstacles and while fulfilling an artistic vision. We propose a unified hardware and software system that distributes some of the camera operator’s burden, freeing the operator up to focus on safety and aesthetics during a take. Our real-time system provides solo operators with end-to-end control so that they can balance on-set responsiveness to action against planned storyboards and framing while looking where they are going. By default, we film without a field monitor. Our LookOut system is built around a lightweight commodity camera gimbal mechanism, with heavy modifications to the controller, which would normally just provide active stabilization. Our control algorithm reacts to speech commands, video, and a premade script. Specifically, our automatic monitoring of the live video feed saves the operator from distractions. In preproduction, an artist uses our graphical user interface (GUI) to design a sequence of high-level camera “behaviors.” Those can be specific, based on a storyboard, or looser objectives, such as “frame both actors.” Then, during filming, a machine-readable script, exported from the GUI, ties together with the sensor readings to drive the gimbal. To validate our algorithm, we compared tracking strategies, interfaces, and hardware protocols and collected impressions from (a) filmmakers who used all aspects of our system and (b) filmmakers who watched footage filmed using LookOut.