Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

To ensure equitable quality of care, differences in machine learning model performance
between patient groups must be addressed. Here, we argue that two separate mechanisms …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

The increasing popularity of large language models (LLMs) such as ChatGPT has led to
growing concerns about their safety, security risks, and ethical implications. This paper aims …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

As large language models (LLMs) permeate more and more applications, an assessment of
their associated security risks becomes increasingly necessary. The potential for exploitation …

Predictive algorithms are now commonly used to distribute society's resources and
sanctions. But these algorithms can entrench and exacerbate inequities. To guard against …

As in traditional machine learning models, models trained with federated learning may
exhibit disparate performance across demographic groups. Model holders must identify …

The rapid progress in automatic prohibited object detection within the context of X-ray
security screening, driven forward by advances in deep learning, has resulted in the first …

Artificial intelligence (AI) technologies have become the key driver of innovation in society.
However, numerous vulnerabilities of AI systems can lead to negative consequences for …