Bonnemains, V., Saurel, C., & Tessier, C. (2018). Embedded ethics: some technical and ethical challenges. Ethics and Information Technology, 20(1), 41-58. Web.
Ethics remains a critical issue in the development of artificial intelligence (AI). AIs are becoming capable and trusted to make decisions that can have a significant effect on people’s lives. Because of this, questions regarding how their decision-making processes should be programmed and which decisions should be left to humans are growing more important. The paper gives examples of ethical issues related to the development of decision-making AI. By highlighting common high-risk ethical decisions through a modified version of the trolley dilemma in a military scenario, the article demonstrates the importance of ethical concerns in the design and training of AI. Bonnemains et al. provide an overview of ethics related to autonomous machines, which can provide background for the issues surrounding the proliferation of AIs which can affect people.
Brynjolfsson, E., & Mcafee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 3-11.
This article provides a thorough overview of the business side of modern advances in AI technology. It lists a few common applications where machine learning, as a particular form of AI, has found practical use. Thus, the article can be used for background information on the business forces driving the adoption of AI. Furthermore, it can be used to identify sectors and areas where AI is likely to be used more in the future.
“The business of artificial intelligence” also includes a section on the common risks and limitations of current AI systems. This section explains the areas in which AI is significantly weaker than humans, and the dangers posed by incorporating such systems without considering these weaknesses. Overall, the article can provide context for the research paper by giving arguments both for and against the application of AI, and identifying the areas where such application is the most promising.
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. Web.
This article explores the ramifications of automation through the means of AI. It defines the tasks that are most conducive to being automated by the application of machine learning methods. The article also details the economic causes and consequences of automating processes. As automation is driven to a significant extent by such business and economic factors, exploring them is crucial to understanding the forces driving the adoption of AI and its effect on the workforce. Finally, a section is dedicated to the economic and policy factors that will slow down the rate of adoption of AI.
The article can provide a strong economic background for the research paper. The factors discussed therein can give insight into the potential effects that the adoption of AI can have on the workforce. In particular, the paper’s findings can be used to identify fields that are susceptible to automation and will, therefore, see a reduction in demand for human labor. Ultimately, Brynjolfsson & Mitchell’s work can help provide further backing for the research paper’s information on workforce and economic implications.
Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National bureau of economic research. Web.
This article discusses the effects that AI on innovation, particularly in research and development. Although primarily concerned with scientific innovation, the arguments made by the authors can be applied to other areas, including business innovation and development in information technology. It provides historical context on the development of AI and automation before exploring the potential ramifications of AI-driven innovation. The article goes into detail on the economic, ethical, and policy implications of adopting AI for innovation, particularly the possibility of one actor acquiring an innovation advantage that would make competition impossible.
The insights of Cockburn et al. are crucial when considering the future developments in policy and can be critical to discussing future policy changes that may be necessary to adopt AI technology. In particular, the authors’ concerns for intellectual property laws, which may be a significant factor in the aforementioned implications for competition, are a significant point of interest for research into the ramifications of AI adoption.