Artificial Intelligence

Artificial Intelligence problem: Ethical Issues

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Literature Review: Ethical Issues problem for Artificial Intelligence

This “Ten Problems for Artificial Intelligence in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.

Without further ado, these are the ten problems 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 introductory section, a short presentation of the ten selected references and a conclusions section.

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

5 Ethical Issues

THE PROBLEM — Artificial Intelligence raises a number of ethical issues such as privacy, bias and responsibility attribution. Guidelines for responsible design and implementation are needed, together with governance frameworks for the public sector activities. Legal and societal impacts are also matter of concern. It remains to be agreed the shape of these new ethical standards and how they do differ from conventional ones.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — The low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. Sociotechnical systems should be systematically designed to avoid gaps in moral culpability, accountability, and active responsibility. Countries making the major contribution to the research on AI ethical issues, namely the USA, UK, and China, mostly engage in domestic collaboration. But what exactly are these ethical standards and how do they differ from conventional standards? Transparency and Explainability. In the COVID-19 world, difficulties accessing sufficiently timely, robust, granular, standardized and documented data for AI purposes have been evident. Across the diverse landscape of use cases in financial services, AI has the potential to enable significant benefits as well as to lead to serious harms in five distinct areas. Organizations make use of only a limited subset of mitigation measures and focus on only a limited set of issues. When considering the role of AI in cybersecurity from systems level, there are three areas of great impact: system robustness, system resilience, system responses. Discussions in Europe on how to face problems regarding the changes within labor markets due to technology have so far been led by economists. There is an urgent need for governments to make new legislation to prevent lethal autonomous weapons becoming a reality.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …

“Ten Problems for Artificial Intelligence in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

booklet updated on 19 Jun 2021, now on sale as version 1.2

By TenProblems

Literature Reviews for Inquisitive Minds