![]() ![]() The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The insights gained will facilitate discussions on the future of data sharing and ownership in accessibility research contributing to informing the development of inclusive AI applications and assistive technologies. The work proposes to take a mixed-method research approach to gain a deep understanding of the need and challenges of shared resources in this field. Under such tension between making their data accessible and restricting access to protect the people represented in the data, this paper serves as a starting point to call for action in developing guidelines and frameworks for ethical use and sharing of accessibility datasets. However, sharing data sourced from people with disabilities or older adults poses ethical and privacy concerns, which significantly limit the availability and re-use of accessibility datasets. Datasets and data sharing play an important role in training and testing machine learning models and helping deployed systems work better in the real world. While advances in technologies like artificial intelligence promise a lot of possibilities for the disability community, they are centered around data-driven approaches. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. ![]() We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. ![]() Even when data are collected and are publicly available, it is often difficult to locate them. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.ĭatasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups.In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets - datasets sourced from people with disabilities and older adults - that can potentially play an important role in mitigating bias for inclusive AI-infused applications. As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. ![]()
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