6 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…