arXiv subjects

Hilke Schellmann

Publications and source records attributed to Hilke Schellmann.

3 recordsLinked to original sources

arXiv2025

Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia

Automatic Speech Recognition (ASR) systems' growing use warrants robust auditing approaches to ensure equitable transcription quality, especially for people with speech disorders like aphasia who disproportionately depend on ASR. While academic and industry audits have revealed performance disparities across user populations, standard auditing practices often overlook nuances that risk masking harm to marginalized groups. We identify three common pitfalls in standard ASR audits: (1) adhering to one method of text standardization, which can mask variance in ASR performance and ignore the standardization preferences of marginalized communities; (2) displaying high-level demographic findings without considering performance disparities by nuanced intersectional subgroups, or conditioning on relevant acoustic properties; and (3) reporting only one gold-standard metric (Word Error Rate), which inadequately quantifies common generative AI errors like hallucinations. We propose a holistic auditing framework addressing these pitfalls, and in a case study of six popular ASR systems, find consistently worse ASR performance for speakers with aphasia relative to a control group. We call on practitioners to implement these robust, community-driven ASR auditing practices better suited for the rapidly changing ASR landscape.

cs.CY↗

arXiv2024

Careless Whisper: Speech-to-Text Hallucination Harms

Speech-to-text services aim to transcribe input audio as accurately as possible. They increasingly play a role in everyday life, for example in personal voice assistants or in customer-company interactions. We evaluate Open AI's Whisper, a state-of-the-art automated speech recognition service outperforming industry competitors, as of 2023. While many of Whisper's transcriptions were highly accurate, we find that roughly 1\% of audio transcriptions contained entire hallucinated phrases or sentences which did not exist in any form in the underlying audio. We thematically analyze the Whisper-hallucinated content, finding that 38\% of hallucinations include explicit harms such as perpetuating violence, making up inaccurate associations, or implying false authority. We then study why hallucinations occur by observing the disparities in hallucination rates between speakers with aphasia (who have a lowered ability to express themselves using speech and voice) and a control group. We find that hallucinations disproportionately occur for individuals who speak with longer shares of non-vocal durations -- a common symptom of aphasia. We call on industry practitioners to ameliorate these language-model-based hallucinations in Whisper, and to raise awareness of potential biases amplified by hallucinations in downstream applications of speech-to-text models.

cs.CL↗

arXiv2022

An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Crucially, rather than challenging or affirming the assumptions made in psychometric testing -- that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job -- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.

cs.CY↗