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381 papers
This study designs a qualitative survey aimed at software startup educators at Universities, and gathers enough information so it can help the academic community in improving their own courses.
A conceptual model to explain for coop competition among software firms in OSS projects is proposed and shows two aspects of coopetition can be managed at the same time based on firm gatekeepers.
A multi-vocal literature review focusing on practitioner literature is conducted in order to compile a list of metrics used by software startups, intended to serve as a basis for further research in the area.
This paper discusses a research framework for implementing AI ethics in industrial settings and presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.
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An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Software Engineers work using highly diverse methods and practices, and general theories in software engineering are lacking. A recent attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering. Essence is a method-agnostic progress manageme...
This paper provides a baseline for ethics in AI based software development by reporting results from an industrial multiple case study on AI systems development in the health care sector, and explores the current state of practice out on the field in the absence of formal methods and tools for ethically aligned design.
It is found that the key conditions for making these blockchain-based solutions viable are attaining technological maturity and competences, ecosystem thinking and adequate governance of these ecosystems, and finally (3) achieving legal and regulatory predictability.
A holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products is described and a research agenda with seven questions for future studies is proposed.
It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
General Science
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