Editing Deep Learning Revolution (section)

=== Ethical Considerations ===
There are many ethical considerations when it comes to the continued development of deep learning in ASR. One prominent issue that continues to be researched is bias. Even well-trained ASR systems often face problems in large variations in speech that are due to characteristics such as: age, gender, race, speech impairments, and accents.<ref>Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021). Quantifying bias in automatic speech recognition. ''arXiv preprint arXiv:2103.15122''.</ref> Further training and research is needed with more diverse datasets to combat this issue and make ASR’s accessible to the greater audience. Similarly, fairness will need to be taken into consideration. Fairness in ASR relates to how equally a system performs between different subgroups of a population.<ref>Veliche, I. E., & Fung, P. (2023, June). Improving Fairness and Robustness in End-to-End Speech Recognition Through Unsupervised Clustering. In ''ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'' (pp. 1-5). IEEE.</ref>