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Cornell researchers developed a more equitable method for choosing top candidates from a large applicant pool in cases where insufficient information makes it hard to choose.
While humans still make many high-stakes decisions – like who should get a job, admission to college or a spot in a clinical trial – artificial intelligence (AI) models are increasingly used to narrow down the applicants into a manageable shortlist.
Polymath scholar Sendhil Mullainathan ’93, a behavioral economist who has combined computational and social sciences to produce pioneering work on health care, poverty and the criminal justice system, will deliver three public lectures at Cornell Nov. 11-13 for the Messenger Lectures series.
With artificial intelligence increasingly integrated into our daily lives, one of the most pressing concerns about this emerging technology is ensuring that the new innovations being developed consider their impact on individuals from different backgrounds and communities. The work of researchers like Cornell Tech PhD student Ben Laufer is critical for understanding the social and ethical implications of algorithmic decision-making.
In an era where digital threats are ever-evolving, the need for advanced education and research in cybersecurity, trust, and safety is paramount. Cornell Tech’s new Security, Trust, and Safety (SETS) Initiative, a cutting-edge program aimed at revolutionizing these fields, aims to address these challenges head-on. The director of the SETS program, Google alum Alexios Mantzarlis, brings a wealth of experience and a vision to this critical endeavor.
Thomas Ristenpart, a Professor at Cornell Tech and in the Computer Science Department at Cornell University, received the esteemed Test of Time Award at the 33rd USENIX Security Symposium. This accolade recognizes his co-authored 2014 paper, “Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing,” for its enduring impact on the field over the past 10 years.
Taking race into account when developing tools to predict a patient’s risk of colorectal cancer leads to more accurate predictions when compared with race-blind algorithms, researchers find.
While many medical researchers have argued that race should be removed as a factor from clinical algorithms that predict disease risks, a new study finds that, at least for colorectal cancer, including race can help correct a data issue – inaccurate recording of family history for Black patients.
In an examination of thousands of human-written college admissions essays and those generated by AI, researchers found that the AI-generated essays are most similar to essays authored by students who are males, with higher socioeconomic status and higher levels of social privilege.
A multidisciplinary team aims to build a more inclusive AI shaped by global cultures and knowledge – one of three projects that make up Cornell’s new Global Grand Challenge: The Future.
