AI in Service InteractionsEthics and Social Impacts of AI
Abstract
Efforts to develop “empathic AI” often assume a universal definition for empathy—a singular, objective understanding of “human” experiences. This implies that achieving empathic AI is only a matter of optimization difficulty, where researchers should find the “correct” way to align AI systems with human-like empathy—essentially an engineering problem. In this chapter, we challenge this assumption. We begin by emphasizing the multifaceted nature of empathy. Then, we explore how variations in (1) the multiple forms of empathy and (2) the many groups we are embedded within present significant challenges in creating a universally applicable empathic AI. We discuss the difficulties of codifying empathy (an inherently context-dependent phenomenon) into AI systems, as well as the recent evidence for cultural biases and moral stereotypes in widely used Large Language Models (an important class of AI systems), highlighting the broader ethical, epistemic, and possibly existential issues inherent in designing machines that “understand” or “feel” others as humans do. We conclude by advocating for a more contextualized approach to empathic AI, one that is culture-aware, context-sensitive, and pluralistic, moving beyond the reductionist notion of “humans” as a monolith. We do not intend to address the question in our chapter’s title with a simple yes-or-no answer; instead, we advocate for asking questions such as “What kind of empathy?” and “Empathy for whom?” as researchers and engineers move toward developing more empathic generative language models.
Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 179 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 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
In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study 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
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The findings show that speed related agile practices are used to a greater extent in comparison to quality practices, and that software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.
Jevgenija Pantiuchina, Marco Mondini, Dron Khanna et al.· International Conference on...· 84 citations· ⚡4
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
