Artificial Intelligence

Ten Problems for Artificial Intelligence in the 2020s

Booklet updated on 10 Jun 2022, now on sale as version 2.0

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Literature Review: Artificial Intelligence Problems for the 2020s

The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence [1]. The 2022 report shows that AI systems are starting to be deployed widely into the economy, but at the same time they are being deployed, the ethical issues associated with AI are becoming magnified. Some parts of AI are not very globalized, though, and many AI ethics publications tend to concentrate on English- language systems and datasets, despite AI being deployed globally.

AI is now used across most industries [2]. It solves business problems, detects fraud, improves crop yields, manages supply chains, recommends products, and even assists designers and writers in their work. AI can predict call volume in customer service centers and recommend staffing levels; it also predicts the emotional state and behavior of the person calling to help companies anticipate desirable solutions. AI automates the process for drug discovery, which ultimately led to faster COVID-19 vaccine candidates.

Most educated, interested people in democratic societies with an open market economy think of AI as convenient, admirable, hopeful, close, and worrying and dangerous simultaneously [3]. They tend to agree that AI should develop, but with effective international and domestic governance. They also think AI could create as much as possible but without autonomous decision-making capacity, i.e., not self-aware like Artificial General Intelligence. They understand that private benefit is the real driving force of AI development.

Starting from such general references, this booklet identifies ten relevant issues, as put forward at academic level in the form of recent journal articles, conference proceedings or students’ theses. Four freely accessible internet references have been selected for each issue and direct links are provided at the end of each chapter for own consultation. Our references neither intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, scientifically-sound case studies for dissemination aimed at non-specialist readers. They will also offer even more references through their own bibliography list.

Without further ado, these are the “Ten Problems for Artificial Intelligence in the 2020s” that we are going to introduce in this booklet:

  1. computer games,
  2. intelligent optimization,
  3. accountability,
  4. human intervention,
  5. ethical issues,
  6. bias,
  7. interpretability,
  8. software development,
  9. open source,
  10. operations research.

Each problem has its own dedicated chapter made of an introduction, a snippet from the 1st edition of this booklet, a short presentation of four new case studies, a conclusions section and the references list with links.

The final chapter of this booklet will report the conclusions from each chapter, again, in order to provide a complete executive summary.


[1] Zhang, D. et al., “The AI Index 2022 Annual Report,” AI Index Steering Committee, Stanford Institute for Human-Centered AI, Stanford University, March 2022.

[2] Webb A., et al. Artificial Intelligence – 2022 Tech Trends Report, 15th Edition, Vol. 01. Future Today Institute, 2022.

[3] Yeh, S.C., et al. Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals. Sustainability 2021, 13, 9165.

Artificial Intelligence Problems
“Ten Problems for Artificial Intelligence in the 2020s” booklet for Amazon Kindle, 2022; click on the cover to go to the dedicated Amazon listing page Artificial Intelligence Problems, Boston Dynamics, reinforcement learning, natural language processing, machine learning

By TenProblems

Literature Reviews for Inquisitive Minds