Considerations for Ethical Speech Recognition Datasets
Speech AI Technologies are largely trained on publicly available datasets or by the massive web-crawling of speech. In both cases, data acquisition focuses on minimizing collection effort, without necessarily taking the data subjects’ protection or user needs into consideration. This results to models that are not robust when used on users who deviate from the dominant demographics in the train- ing set, discriminating individuals having different dialects, accents, speaking styles, and disfluencies. In this talk, we use automatic speech recognition as a case study and examine the properties that ethical speech datasets should possess towards responsible AI ap- plications. We showcase diversity issues, inclusion practices, and necessary considerations that can improve trained models, while facilitating model explainability and protecting users and data sub- jects. We argue for the legal & privacy protection of data subjects, targeted data sampling corresponding to user demographics & needs, appropriate meta data that ensure explainability & account- ability in cases of model failure, and the sociotechnical & situated model design. We hope this talk can inspire researchers & practi- tioners to design and use more human-centric datasets in speech technologies and other domains, in ways that empower and respect users, while improving machine learning models’ robustness and utility.
Ethical Considerations for Responsible Data Curation
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustnes…
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have shown how gender classification systems can be biased against women with darker skin tones. While…
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standard Internet data collection methods in low- and middle-income countries, we analyze human-centri…