Large language models (LLMs) are now being considered and even deployed for applications that support high-stakes decision-making, such as recruitment and clinical decisions. While several methods have been proposed for measuring bias, there remains a gap between predictions, which are what the proposed methods consider, and how they are used to make decisions. In this work, we introduce Rank-Allocational-Based Bias Index (RABBI), a model-agnostic bias measure that assesses potential allocational harms arising from biases in LLM predictions. We compare RABBI and current bias metrics on two allocation decision tasks. We evaluate their predictive validity across ten LLMs and utility for model selection. Our results reveal that commonly-used bias metrics based on average performance gap and distribution distance fail to reliably capture group disparities in allocation outcomes, whereas RABBI exhibits a strong correlation with allocation disparities. Our work highlights the need to account for how models are used in contexts with limited resource constraints.

Here we describe a new image representation technique based on the mathematics of transport and optimal transport. The method relies on the combination of the well-known Radon transform for images and a recent signal representation method called the Signed Cumulative Distribution Transform. The newly proposed method generalizes previous transport-related image representation methods to arbitrary functions (images), and thus can be used in more applications. We describe the new transform, and some of its mathematical properties and demonstrate its ability to partition image classes with real and simulated data. In comparison to existing transport transform methods, as well as deep learning-based classification methods, the new transform more accurately represents the information content of signed images, and thus can be used to obtain higher classification accuracies. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit, available on Github.

During our synagogue's Zoom services last week, my family and I found ourselves giggling when we should have been serious. Auto-captions were turned on, and they kept botching the rabbi's Hebrew-laced English. Mourner's Kaddish (memorial prayer) was transcribed as mourner Scottish, and refua shlema (wish for a "full recovery") became with flu wash Emma. Some of the transcriptions bordered on offensive, like when Torah became terrorism and yasher koach (great job!) became wish a cough. We weren't relying on the captions and could laugh at these mistakes.

Each month, Future Tense Fiction--a series of short stories from Future Tense and ASU's Center for Science and the Imagination about how technology and science will change our lives--publishes a story on a theme. Stop me if you've heard this one before." David had heard this one before, but he needed a job. He folded his hands in his lap and summoned the patience he'd learned sitting through Talmudic debates. He waved for Aiden Shure, Town of Our Own's CEO, to continue. "It's a dive bar, lots of rough language from the other patrons, but the bartender says, 'Father, what can I get you?' The priest says: 'Well, I have to lead Mass in the morning, but a wee nip can't hurt. Gimme three fingers of Irish whiskey and cut it with holy water.' So the bartender runs over to the church next door, borrows a bit of holy water, and makes the drink. The priest is satisfied, so the bartender moves on: 'Rabbi, what can I get you?' The rabbi says, 'Well it is the Sabbath day, but if it's not too much work I wouldn't say no to a glass of kosher wine from the vineyards of the Holy Land.' So the bartender finds a bottle of sweet Israeli red, and the rabbi thanks him." Aiden told the joke like he'd practiced it a lot while stuck in traffic. David braced for the punchline he knew was coming. "So the bartender turns to the robot, which has been quietly listening to the other patrons.

A Slate staff writer who regularly reports on Christianity responds to Andrew D. Hudson's "A Priest, a Rabbi, and a Robot Walk Into a Bar." The Michigan-based company Covenant Eyes markets itself to Christians who want to stop viewing pornography. Its software takes screenshots of a user's screen activity, uses A.I. to scan it for pornographic imagery, and then sends regular reports to the user and a designated "ally" who has agreed to hold him accountable. The company's name comes from a Bible verse that reads, "I made a covenant with my eyes not to look lustfully at a young woman." Everyone wants technology to reflect their own worldview, and religious conservatives are no exception.

This room of the university's Artificial Intelligence Lab looked like the study of an Oxford don. The walls were covered with bookcases and -- of course! There was a small end-table with a lace doily. The visitors were not at all what one might expect in a computer lab. There was a Catholic priest, a Lutheran minister and a rabbi.