Demo Data from EDBT 2026 / reviewers in the wild / expert
Surakshya Aryal
dblp:352/8964
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 67% Speech recognition and synthesis · 33% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Speech recognition and synthesis
automatic speech recognition |
0.7 | 1 | 2023 | Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023 |
Machine learning › Trustworthy machine learning › fairness
demographic bias |
0.7 | 1 | 2023 | Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023 |
Machine learning › Trustworthy machine learning
fairness |
0.7 | 1 | 2023 | Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
systematic literature review · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Hey, Siri! Why Are You Biased against Women? (Student Abstract)abstractThe intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women. Surakshya Aryal, Mikel K. Ngueajio, Saurav K. Aryal, Gloria J. Washington |
AAAI | 1 |