Racial Bias in Fans and Officials: Evidence from the Italian Serie A

Recent scholarship studying the impact of race-based prejudice has emphasized its rampant persistence throughout all aspects of modern society, including the world of sports. Prior research from American leagues has shown that even referees, trained officials intended to enact neutral judgements, are subject to bias against Black and dark-skinned players. To extend these studies and inform policies aimed at combating racial bias in public spaces more broadly, we report results from a unique dataset of over 6500 player-year observations from the Italian Serie A to examine whether these biases persist in European football. Our results show that darker-skinned players receive more foul calls and more cards than lighter-skinned players, controlling for a range of potential confounders and productivity-relevant mediators. By exploiting an absence of fans induced by the COVID-19 pandemic, we also present preliminary evidence that fans may play a key role in inducing poor calls against darker-skinned players. © The Author(s) 2023.

Blending camera and 77 GHz radar sensing for equitable, robust plethysmography

With the resurgence of non-contact vital sign sensing due to the COVID-19 pandemic, remote heart-rate monitoring has gained significant prominence. Many existing methods use cameras;however previous work shows a performance loss for darker skin tones. In this paper, we show through light transport analysis that the camera modality is fundamentally biased against darker skin tones. We propose to reduce this bias through multi-modal fusion with a complementary and fairer modality - radar. Through a novel debiasing oriented fusion framework, we achieve performance gains over all tested baselines and achieve skin tone fairness improvements over the RGB modality. That is, the associated Pareto frontier between performance and fairness is improved when compared to the RGB modality. In addition, performance improvements are obtained over the radar-based method, with small trade-offs in fairness. We also open-source the largest multi-modal remote heart-rate estimation dataset of paired camera and radar measurements with a focus on skin tone representation. © 2022 Owner/Author.

Equity-Driven Sensing System for Measuring Skin Tone–Calibrated Peripheral Blood Oxygen Saturation (OptoBeat): Development, Design, and Evaluation Study

Background: Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO2) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra–low-cost smartphone-based optical sensing system that captures SpO2 and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO2 within 1% of the ground truth in levels as low as 75%. Objective: The objective of this work is to test the following hypotheses and implement an ultra–low-cost smartphone adapter to measure SpO2: skin tone has a significant effect on pulse oximeter measurements (hypothesis 1), images of skin tone can be used to calibrate pulse oximeter error (hypothesis 2), and SpO2 can be measured with a smartphone camera using the screen as a light source (hypothesis 3). Methods: Synthetic skin with the same optical properties as human skin was used in ex vivo experiments. A skin tone scale was placed in images for calibration and ground truth. To achieve a wide range of SpO2 for measurement, we reoxygenated sheep blood and pumped it through synthetic arteries. A custom optical system was connected from the smartphone screen (flashing red and blue) to the analyte and into the phone’s camera for measurement. Results: The 3 skin tones were accurately classified according to the Fitzpatrick scale as types 2, 3, and 5. Classification was performed using the Euclidean distance between the measured red, green, and blue values. Traditional pulse oximeter measurements (n=2000) showed significant differences between skin tones in both alternating current and direct current measurements using ANOVA (direct current: F2,5997=3.1170 × 105, P<.01;alternating current: F2,5997=8.07 × 106, P<.01). Continuous SpO2 measurements (n=400;10-second samples, 67 minutes total) from 95% to 75% were captured using OptoBeat in an ex vivo experiment. The accuracy was measured to be within 1% of the ground truth via quadratic support vector machine regression and 10-fold cross-validation (R2=0.97, root mean square error=0.7, mean square error=0.49, and mean absolute error=0.5). In the human-participant proof-of-concept experiment (N=3;samples=3 × N, duration=20-30 seconds per sample), SpO2 measurements were accurate to within 0.5% of the ground truth, and pulse rate measurements were accurate to within 1.7% of the ground truth. Conclusions: In this work, we demonstrate that skin tone has a significant effect on SpO2 measurements and the design and evaluation of OptoBeat. The ultra-low-cost OptoBeat system enables smartphones to classify skin tone for calibration, reliably measure SpO2 as low as 75%, and normalize to avoid skin tone–based bias.

Dual Sensor Indian Masked Face Dataset

With the advancements in deep learning technologies, real-world applications like face detection, gender prediction, and face recognition have achieved human-level performance. However, the emergence of the COVID-19 pandemic brought new challenges to existing deep learning algorithms. People are forced to wear a mask to limit the spread of COVID-19. These face masks occlude a significant portion of the face, thereby posing multiple challenges to existing algorithms. Images captured using surveillance cameras have a low resolution which hinders the model performance. Along with this, skin tone, ethnicity and attire also play a significant role in detection and recognition performance. India is a large country with huge diversity in skin tone and attire of the people. To address the challenges due to masks in the Indian context, we propose a novel Dual Sensor Indian Masked Face (DS- IMF) dataset, which contains images captured in constrained environmental settings with a variety of masks and degrees of occlusion. Multiple experiments are performed on the DS- IMF dataset at different resolutions. Experimental results demonstrate the limitations of existing algorithms on low-resolution masked face images. The proposed dataset can be found at http://www.iab-rubric.org/resources/dsimf.html. © 2021 IEEE.