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Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English). Our comprehensive experiments establish that existing methods are limited in their ability to prevent biased behavior in current toxicity detectors. We then propose an automatic, dialect-aware data correction method, as a proof-of-concept. Despite the use of synthetic labels, this method reduces dialectal associations with toxicity. Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases. 

Temperature sensitivity of soil organic carbon (SOC) decomposition is one of the major uncertainties in predicting climate‐carbon (C) cycle feedback. Results from previous studies are highly contradictory with old soil C decomposition being more, similarly, or less sensitive to temperature than decomposition of young fractions. The contradictory results are partly from difficulties in distinguishing old from youngSOCand their changes over time in the experiments with or without isotopic techniques. In this study, we have conducted a long‐term field incubation experiment with deep soil collars (0–70 cm in depth, 10 cm in diameter ofPVCtubes) for excluding root C input to examine apparent temperature sensitivity ofSOCdecomposition under ambient and warming treatments from 2002 to 2008. The data from the experiment were infused into a multi‐pool soil C model to estimate intrinsic temperature sensitivity ofSOCdecomposition and C residence times of threeSOCfractions (i.e., active, slow, and passive) using a data assimilation (DA) technique. As activeSOCwith the short C residence time was progressively depleted in the deep soil collars under both ambient and warming treatments, the residences times of the wholeSOCbecame longer over time. Concomitantly, the estimated apparent and intrinsic temperature sensitivity ofSOCdecomposition also became gradually higher over time as more than 50% of activeSOCwas depleted. Thus, the temperature sensitivity of soil C decomposition in deep soil collars was positively correlated with the mean C residence times. However, the regression slope of the temperature sensitivity against the residence time was lower under the warming treatment than under ambient temperature, indicating that other processes also regulated temperature sensitivity ofSOCdecomposition. These results indicate that oldSOCdecomposition is more sensitive to temperature than young components, making the old C more vulnerable to future warmer climate.

 

Extracellular enzymes catalyze rate‐limiting steps in soil organic matter decomposition, and their activities (EEAs) play a key role in determining soil respiration (SR). Both EEAs and SR are highly sensitive to temperature, but their responses to climate warming remain poorly understood. Here, we present a meta‐analysis on the response of soil cellulase and ligninase activities and SR to warming, synthesizing data from 56 studies. We found that warming significantly enhanced ligninase activity by 21.4% but had no effect on cellulase activity. Increases in ligninase activity were positively correlated with changes in SR, while no such relationship was found for cellulase. The warming response of ligninase activity was more closely related to the responses of SR than a wide range of environmental and experimental methodological factors. Furthermore, warming effects on ligninase activity increased with experiment duration. These results suggest that soil microorganisms sustain long‐term increases in SR with warming by gradually increasing the degradation of the recalcitrant carbon pool.