Infographics from Google Gemini coupled with Google NotebookLM

 

You can click on the image to see more detail. 

This graphic was created from a simple prompt on Google's NoteBookLM with information from its own research. Here is a summary of the sources:

Development of AI from a Research Perspective

The collected sources examine the rising need for algorithmic audits to ensure accountability and mitigate harm from AI systems, noting the current lack of shared standards and the risk of "audit-washing." Concurrently, multiple sources expose the often exploitative human labor—referred to as the "extraction machine"—that is hidden behind AI products, especially in data annotation and content moderation outsourced to the Global South. This essential, low-paid work perpetuates a digital colonialism and drives profits for a consolidating group of "Big AI" firms, which also face increasing criticism for their environmental impact and for using unremunerated creative work (generative AI as "theft"). The texts emphasize that meaningful change, whether in audit effectiveness or labor conditions, will require collective political action and established ethical standards and data literacy within organizations.

Here is a list of the sources used in the analysis of both the statement and the infographic. You might be surprised. if I asked for a report, it would used all these sources to my an extremely extensive report and up to 110 more. The list is too long to post. 

ads for regulatory inspection of algorithmic systems. Ada Lovelace Institute. Adler, M. (2022, April 28). Why Elon Musk Wants to ‘Open Source’ Twitter’s Algorithms. Blomberg. Ahamat, G., Chang, M., & Thomas, C. (2021, April 17). Types of Assurance in AI and the role of Standards. Centre for Data Ethics and Innovation (CDEI) Blog. Ajunwa, I. (2021). An Auditing Imperative for Automated Hiring Sys-tems. Harvard Journal of Law and Technology. Ajunwa, I. (2021, March 24). The Auditing Imperative for Automated Hiring. Harvard Journal of Law and Technology, 34(2). Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15, e35179. Alfandi, O., & Abdul, S. (2024). Ethical challenges and solutions of generative AI: An interdisciplinary perspective. MDPI.