Authentication Challenges in Customer Service Settings Experienced by Deaf and Hard of Hearing People
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Customer services are important for answering questions, providing information, and handling issues. Often people want to connect with a customer service representative, yet, this can be an accessibility barrier for Deaf and Hard of Hearing People (DHH) when voice calls are the only method. However, little is known about the challenges experienced by DHH people in solely voice-based customer service settings (e.g., authenticating themselves over the phone to their banks). To address this, we interviewed 18 DHH people to understand the challenges when remotely authenticating themselves with customer service. Though DHH people are not unique in their attitudes and behaviors toward mobile authentication, we found that voice-based authentication and services are challenging. Furthermore, DHH people can often be put in positions where they must trust third-party support (e.g., human interpreters) when discussing sensitive information to authenticate themselves through voice-based authentication and services. We present several avenues of future work needed to address these challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it