Automatic speech recognition (ASR) systems have seen substantial improvements in the past decade; however, not for all speaker groups. Recent research shows that bias exists against different types of speech, including non-native accents, in state-of-the-art (SOTA) ASR systems. To attain inclusive speech recognition, i.e., ASR for everyone irrespective of how one speaks or the accent one has, bias mitigation is necessary. Here we focus on bias mitigation against non-native accents using two different approaches: data augmentation and by using more effective training methods. We used an autoencoder-based cross-lingual voice conversion (VC) model to increase the amount of non-native accented speech training data in addition to data augmentation through speed perturbation. Moreover, we investigate two training methods, i.e., fine-tuning and domain adversarial training (DAT), to see whether they can use the limited non-native accented speech data more effectively than a standard training approach. Experimental results show that VC-based data augmentation successfully mitigates the bias against non-native accents for the SOTA end-to-end (E2E) Dutch ASR system. Combining VC and speed perturbed data gave the lowest word error rate (WER) and the smallest bias against nonnative accents. Fine-tuning and DAT reduced the bias against non-native accents but at the cost of native performance.@en