Sep 2026· Cambridge University Press eBooks· 17 references
Abstract
In 2023, the arrival of Chat GPT 3o heralded many viral conversations about what it means to interact with artificial intelligence (e.g., Roose, 2023). People read about individuals having extended conversations with chatbots, often leading to complex consequences that raise deeper questions. What do we mean by “empathy” in these spaces? What are the ethical consequences for building or using technology for emotional support? There have been cases of large language models giving better and more empathetic medical advice than trained medical workers (Ayers et al., 2023; see later replication by Ovsyannikova et al., 2025). There have also been popular write-ups about people developing relationships with Artificial Intelligence (AI) platforms explicitly positioned as romantic partners (Patel, 2024). In this introductory chapter, we will highlight core themes that unite the chapters throughout the volume, highlighting connections across social sciences, humanities, and engineering and computers science to juxtapose perspectives and illustrate connections and ongoing controversies.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
