We explore how an AI model's decision fairness affects people's engagement with and perceived fairness of the model if they are subject to its decisions, but could repeatedly and strategically respond to these decisions. Two types of strategic responses are considered -- people could determine whether to continue interacting with the model, and whether to invest in themselves to improve their chance of future favorable decisions from the model. Via three human-subject experiments, we found that in decision subjects' strategic, repeated interactions with an AI model, the model's decision fairness does not change their willingness to interact with the model or to improve themselves, even when the model exhibits unfairness on salient protected attributes. However, decision subjects still perceive the AI model to be less fair when it systematically biases against their group, especially if the difficulty of improving one's qualification for the favorable decision is larger for the lowly-qualified people.

Acer has unveiled their new TravelMate laptops mere days ahead of Computex. These business laptops are traditionally known for their slim form factor and long battery life. The new models announced today come loaded with more power-efficient Intel Core Ultra CPUs as well as useful AI capabilities. There's also a 2-in-1 version and a clamshell with a bigger 16-inch display, and they all use recycled materials in both design and packaging. Acer puts forth three offerings on the table: Acer TravelMate P6 14, Acer TravelMate P4 Spin 14, and Acer TravelMate P4 16.

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field.

A 13-year-old boy from Oregon has won the Young Scientist Challenge by inventing an artificial intelligence treatment for pancreatic cancer. Rishab Jain created an algorithm to improve cancer treatment by using AI to locate and track the pancreas in real time. A prime challenge in radiation treatment is locating the pancreas itself, which is often obscured by the stomach or other organs, resulting in healthy cells being inadvertently hit. Rishab's algorithm improves accuracy and increases the impact of radiation treatment, according to organizers of the competition. The seventh grade student said he started the project last year, when he learned that pancreatic cancer, the third-leading cause of cancer deaths, is devastating and fast-growing.

Running a background check for a sensitive job in an investment bank, government office or large corporation traditionally takes 10 to 15 days. In today's fast-moving world, where a tweet can change everything in a matter of seconds, that's too long. Shlomo Mirvis had been conducting background checks the old fashioned way for Israeli intelligence firm Kela. Mirvis had to do his work manually. "The world of background checks hardly changed in the last 40 years," he tells ISRAEL21c.