Can we ever really trust algorithms to make decisions for us? Previous research has proved these programs can reinforce society’s harmful biases, but the problems go beyond that. A new study ...

There are three key reasons why predictive algorithms can make big mistakes. 1. The Wrong Data An algorithm can only make accurate predictions if you train it using the right type of data.

For example, users can feed their locally stored data into a large language model (LLM), such as Llama. The so-called SIFT algorithm (Selecting Informative data for Fine-Tuning), developed by ETH ...

This is in response to evidence that demographics-blind ML algorithms discriminate due to skewed data,” he says. But the “fair” ML algorithms have tended to make straightforward choices based on ...

The Justice in Forensic Algorithms Act aims to ensure that when algorithmic analyses are used as evidence in court, defendants get to know how the tools reached their conclusions and allow them to ...

For example, the kidney allocation system is an algorithm-based protocol used to prioritize patients for kidney transplants on the basis of the amount of time they have been on the national ...

It doesn’t take much to make machine-learning algorithms go awry The rise of large-language models could make the problem worse ...

Adam Aleksic talks about his new book 'Algospeak,' which details how algorithms are changing our vocabulary; plus, we check in with Hennessy + Ingalls bookstore.