Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across groups. In such settings, trained models can be dominated by the relationships that best fit the majority group, leading to disparate performance. We propose framing toxicity detection as multi-task learning (MTL), allowing a model to specialize on the relationships that are relevant to each demographic group while also leveraging shared properties across groups. With toxicity detection, each task corresponds to identifying toxicity against a particular demographic group. However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. This lets us learn group specific representations in each branch which are not cross contaminated by irrelevant labels. Results on synthetic and real data show that using CondMTL improves predictive recall over various baselines in general and for the minority demographic group in particular, while having similar overall accuracy.

翻译：算法偏差常源于不同子群體的有效性差異，即預測關係在不同群體間存在變化。例如在毒性語言檢測中，針對不同人口群體的評論內容可能呈現顯著差異。此類場景下，訓練模型可能被最適配多數群體的關係主導，導致性能表現失衡。我們提出將毒性檢測框架構建為多任務學習（MTL），使模型既能針對各人口群體的相關關係進行特化學習，又能利用跨群體的共享特徵。在毒性檢測中，每個任務對應識別針對特定人口群體的毒性內容。然而傳統多任務學習要求每個數據點具備所有任務的標籤。為解決此問題，我們提出條件多任務學習（CondMTL），其損失函數僅考慮與給定人口群體相關的訓練樣本。這使我們能在各分支中學習到不受無關標籤交叉污染的群體特化表徵。在合成數據與真實數據上的實驗結果表明，CondMTL在保持總體準確率相當的前提下，能夠顯著提升普遍情況（特別是少數人口群體）的預測召回率，優於各類基準方法。