Search

Methods that combine local and global features have recently shown excellent performance on multiple challenging deep image retrieval benchmarks, but their use of local features raises at least two issues. First, these local features simply boil down to the localized map activations of a neural...

Despite being able to capture a range of features of the data, high accuracy models trained with supervision tend to make similar predictions. This seemingly implies that high-performing models share similar biases regardless of training methodology, which would limit ensembling benefits and render...

Researchers create a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them.

We investigate whether three types of post hoc model explanations--feature attribution, concept activation, and training point ranking--are effective for detecting a model's reliance on spurious signals in the training data. Specifically, we consider the scenario where the spurious signal to be...

We study high-dimensional robust statistics tasks in the streaming model. A recent line of work obtained computationally efficient algorithms for a range of high-dimensional robust estimation tasks. Unfortunately, all previous algorithms require storing the entire dataset, incurring memory at least...

Current literature and public discourse on “trust in AI” are often focused on the principles underlying trustworthy AI, with insufficient
attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to
the fore: how should AI...

Textual data can pose a risk of serious harm. These harms can be categorised along three axes: (1) the harm type (e.g. misinformation, hate speech or racial stereotypes) (2) whether it is \textit{elicited} as a feature of the research design from directly studying harmful content (e.g. training a...

A machine-learning model can identify the action in a video clip and label it, without the help of humans.

We aim to (1) build a resource for language technology development, (2) bridge generational gaps in cultural and language knowledge, and at the same time (3) provide socio-economic opportunities through language preservation.
The created data spans diverse topics of cultural importance, and...

Background

Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a...

Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling the knowledge gaps needed to solve the...

How has recent AI Ethics literature addressed topics such as fairness and justice in the context of continued social and structural power asymmetries? We trace both the historical roots and current landmark work that have been shaping the field and categorize these works under three broad umbrellas...

A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.

Building robust deterministic deep neural networks is still a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification accuracy in some situations. On the other hand, some methods simultaneously increase classification accuracy, out-of...

Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of work has advanced our understanding of these systems by providing empirical and theoretical analyses. However, existing empirical results are mixed, and theoretical proposals...

When mathematical modelling is applied to capture a complex system, multiple models are often created that characterize different aspects of that system. Often, a model at one level will produce a prediction which is contradictory at another level but both models are accepted because they are both...

When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased." While much of the work in algorithmic fairness over the last several years has focused on developing various definitions of...

Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.

When self-driving cars crash, who’s responsible? Courts and insurers need to know what’s inside the ‘black box’

Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extrinsic metrics} for evaluating fairness in downstream applications and 2) \emph{intrinsic metrics} for estimating fairness...