This study builds on person perception and human AI interaction (HAII) theories to investigate how content and source cues, specifically race, ethnicity, and nationality, affect judgments of AI-generated content in a high-stakes self-presentation context: college applications. Results of a pre-registered experiment with a nationally representative U.S. sample (N = 644) show that content heuristics, such as linguistic style, played a dominant role in AI detection. Source heuristics, such as nationality, also emerged as a significant factor, with international students more likely to be perceived as using AI, especially when their statements included AI-sounding features. Interestingly, Asian and Hispanic applicants were more likely to be judged as AI users when labeled as domestic students, suggesting interactions between racial stereotypes and AI detection. AI attribution led to lower perceptions of personal statement quality and authenticity, as well as negative evaluations of the applicant's competence, sociability, morality, and future success.

AI-based systems have the potential to assist employers with many aspects of human resources (HR) management, from benefits administration to coaching and development to its most common HR use case, applicant screening. The global HR technology market based on predictive models was already rapidly growing prior to 2022, but attention to AI tools received a dramatic boost with the advent of large language models (LLMs), which are models that are highly adept at understanding, summarizing, and evaluating text data. Given the primacy of text data in the job application process, an emerging HR use case for modern LLMs is to ingest entire application dossiers--including resumes, essays, and transcripts captured from interviews--and output seemingly cogent assessments of candidates' qualifications. As hiring use cases proliferate, however, employers and policymakers are racing to establish guidelines around whether the algorithmic evaluation of candidates comports with employment discrimination law, and how to audit commonly deployed AI tools to ensure they are not discriminatory. The ethical and legal implications of using predictive tools in HR has motivated a body of academic work (Raghavan et al., 2020; Tambe et al., 2019). Policymakers have matched the attention of firms and researchers, introducing a wave of legislation governing high-stakes algorithmic decision making, and hiring in particular (e.g., New York LL 144 or Illinois 820 ILCS 42).

Forty percent of respondents said their job interviewer didn't know they used ChatGPT to write application materials. ChatGPT is making waves across in recruitment and HR. Forty-six percent of job seekers are using the AI chatbot to write their resumes and/or cover letters, and seven in 10 report a higher response rate from companies, according to a February survey of current and recent job seekers. The survey also found that 78% of respondents got an interview when using application materials written by ChatGPT; 59% were hired after applying to a job using materials written by ChatGPT; and 11% were denied a job when the interviewer discovered they used ChatGPT. The primary motivation for job seekers using ChatGPT to write their application materials is to save time.

I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU. This blog post is a mish-mash of how to proceed in your PhD applications from A to Z. It discusses what is important and what is not. It discusses application materials like the statement of purpose (SoP) and how to make sense of these application materials. There are some excellent sources out there on this topic and it is worth stopping for a second and understand what this blog post will give you and what other sources can give you. This blog post is mainly focused on PhD applications for deep learning and related fields like natural language processing, computer vision, reinforcement learning, and other sub-fields of deep learning. This blog post assumes that you already have a relatively strong profile, meaning you probably have already one or multiple publications under your belt and you worked with more than one person on research. This blog post is designed to help you optimize your chance for success for top programs.

The summer school occurs at the first two weeks of the Third Frederick Jelinek Memorial Summer Workshop. The North American Chapter of the Association for Computational Linguistics (NAACL) is again offering an exciting summer school opportunity for a limited number of undergraduate students interested in the field of Human Language Technology. The 2016 Jelinek Summer Workshop will be held from Monday, June 13 to Friday, August 5, 2016 at the Johns Hopkins University in Baltimore, Maryland. The first two weeks of the workshop (from June 13 to 24) is a summer school that provides general introductions to the major areas of study within the field of Human Language Technology (e.g., Natural Language Processing, Automatic Speech Recognition, Machine Translation, Information Retrieval) as well as sessions on specialized research topics of current interest in the field. The NAACL scholarship is used to support undergraduate students to participate in the two-week summer school.