Augmented Datasheets for Speech Datasets and Ethical Decision-Making

Abstract: Speech datasets are crucial for training Speech Language Technologies (SLT); however, the lack of diversity of the underlying training data can lead to serious limitations in building equitable and robust SLT products, especially along dimensions of language, accent, dialect, variety, and speech impairment - and the intersectionality of speech features with socioeconomic and demographic features. Furthermore, there is often a lack of oversight on the underlying training data - commonly built on massive web-crawling and/or publicly available speech - with regard to the ethics of such data collection. To encourage standardized documentation of such speech data components, we introduce an augmented datasheet for speech datasets, which can be used in addition to "Datasheets for Datasets". We then exemplify the importance of each question in our augmented datasheet based on in-depth literature reviews of speech data used in domains such as machine learning, linguistics, and health. Finally, we encourage practitioners - ranging from dataset creators to researchers - to use our augmented datasheet to better define the scope, properties, and limits of speech datasets, while also encouraging consideration of data-subject protection and user community empowerment. Ethical dataset creation is not a one-size-fits-all process, but dataset creators can use our augmented datasheet to reflexively consider the social context of related SLT applications and data sources in order to foster more inclusive SLT products downstream.

• The paper introduces an augmented datasheet template for speech datasets to document diverse and ethical practices in SLT.
• The methodology includes a comprehensive literature review to address inherent biases and enhance dataset transparency.
• Demonstrated examples and a call to action illustrate the practical impact on mitigating bias in automated speech recognition applications.

Augmented Datasheets for Speech Datasets and Ethical Decision-Making

The paper "Augmented Datasheets for Speech Datasets and Ethical Decision-Making" addresses the pressing need for improved documentation of speech datasets used in Speech Language Technologies (SLT). The authors, affiliated with Sony AI and Cornell University, propose an enhanced framework for documenting speech datasets, augmenting the existing "Datasheets for Datasets" methodology. This effort is necessitated by the potential for biases in SLT applications, which can result from a lack of diversity in training data, leading to underrepresentation and mischaracterization of different linguistic subpopulations.

Core Contributions

The paper highlights several key contributions:

1. Augmented Datasheet Template: The paper introduces a comprehensive template specifically tailored for speech datasets, encouraging creators to document key aspects such as diversity, data collection methods, and ethical considerations. This is designed to complement the existing datasheet frameworks.
2. In-depth Literature Review: The authors conduct a detailed review of existing literature and datasets, extracting best practices and documenting issues related to bias and ethical considerations. This review informs the design of the augmented datasheets.
3. Demonstration through Examples: The paper exemplifies the application of the augmented datasheets by applying it to various SLT scenarios, highlighting how users can navigate ethical dilemmas through structured documentation.
4. Call to Action for Practitioners: By employing the augmented datasheets, practitioners—from dataset creators to end-users—are encouraged to consider ethical aspects of SLT applications actively. The authors argue for a reflexive process that accounts for social and ethical implications in dataset usage.

Importance of Comprehensive Documentation

One of the significant challenges highlighted in the paper is the underrepresentation of diverse linguistic subpopulations in speech datasets. This can have severe implications, such as reduced recognition accuracy for atypical speech patterns, affecting applications in fields like healthcare and customer service. For instance, failure to accurately capture and transcribe diverse accents, dialects, or speech impairments can lead to disparities in automated speech recognition (ASR) and synthesis. This is particularly pertinent as SLT applications permeate various aspects of daily life, from virtual assistants to legal transcriptions.

The authors propose specific questions within the datasheet template to address these issues, covering aspects such as linguistic diversity, socio-economic factors, and the ethical treatment of data subjects. These questions aim to ensure that datasets are not only comprehensive in their documentation but also inclusive, promoting equitable SLT outcomes.

Practical Implications and Future Directions

The introduction of augmented datasheets is a forward-thinking step, fostering transparency and accountability in the creation and use of speech datasets. Practically, these datasheets serve as a tool for dataset creators to document the motivations and processes behind data collection, ensuring clear communication with dataset users about the scope and limitations of the datasets. Furthermore, by explicitly considering ethical concerns and diversity from the outset, the datasheets help mitigate potential biases in model training and deployment.

Looking forward, the paper suggests that this methodology could be extended to other domains within artificial intelligence, where dataset bias is a known challenge. Additionally, with ongoing advancements in generative AI and semi-supervised learning methods, the application of augmented datasheets can evolve to address new ethical considerations, such as those posed by synthetic data generation.

In conclusion, the paper by Papakyriakopoulos et al. presents a structured approach to improving the documentation and ethical consideration of speech datasets, which is crucial given the growing deployment of SLT in diverse settings. By guiding practitioners to consider ethical implications and by fostering a collaborative process between creators and users, augmented datasheets aim to contribute to the development of more inclusive and fair SLT applications.