47 citations · 57 across the 13 of their papers we have counts for
13 papers
Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report
Austin T. Hoag, Apostolos Modas, Yunhao Ba +9
Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In this technical report, we prese…
Yes, But Not Always. Generative AI Needs Nuanced Opt-in
Wiebke Hutiri, Morgan Scheuerman, Shruti Nagpal +2
This paper argues that a one-size-fits-all approach to specifying consent for the use of creative works in generative AI is insufficient. Real-world ownership and rights holder str…
Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
Fabio Morreale, Wiebke Hutiri, Joan Serrà +2
The rise of AI-generated music is diluting royalty pools and revealing structural flaws in existing remuneration frameworks, challenging the well-established artist compensation sy…
How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures
Tanvina Patel, Wiebke Hutiri, Aaron Yi Ding +1
There is increasingly more evidence that automatic speech recognition (ASR) systems are biased against different speakers and speaker groups, e.g., due to gender, age, or accent. R…
TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation
Wiebke Hutiri, Mircea Cimpoi, Morgan Scheuerman +2
Dataset transparency is a key enabler of responsible AI, but insights into multimodal dataset attributes that impact trustworthy and ethical aspects of AI applications remain scarc…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
Wiebke Hutiri, Tanvina Patel, Aaron Yi Ding +1
Detecting and mitigating bias in speaker verification systems is important, as datasets, processing choices and algorithms can lead to performance differences that systematically f…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators
Wiebke Hutiri, Oresiti Papakyriakopoulos, Alice Xiang
The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing…
Benchmark Dataset Dynamics, Bias and Privacy Challenges in Voice Biometrics Research
Casandra Rusti, Anna Leschanowsky, Carolyn Quinlan +3
Speaker recognition is a widely used voice-based biometric technology with applications in various industries, including banking, education, recruitment, immigration, law enforceme…
Towards Trustworthy Edge Intelligence: Insights from Voice-Activated Services
W. T. Hutiri, A. Y. Ding
In an age of surveillance capitalism, anchoring the design of emerging smart services in trustworthiness is urgent and important. Edge Intelligence, which brings together the field…
Design Guidelines for Inclusive Speaker Verification Evaluation Datasets
Wiebke Toussaint Hutiri, Lauriane Gorce, Aaron Yi Ding
Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is nece…
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Wiebke Toussaint, Aaron Yi Ding, Fahim Kawsar +1
Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-devi…