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Artificial Intelligence

Artificial Intelligence problem: Accountability

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Literature Review: Accountability 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.


3 Accountability

THE PROBLEM — The responsibility for algorithms in many important decisions affecting our lives is not clear and some sort of framework for managing algorithmic accountability is now needed from supranational regulators, governments and the private sector. It’s essential to establish rigorous governance processes that can quickly identify when a model begins to fail, complete with defined operating controls on inputs or data and output or model results.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — As with most ethical discourses, AI organizational or business ethics cannot be seen as black and white, or right and wrong. The lack of a coherent theory is currently stifling explainable AI. Model-centric mechanisms often provide insufficient information of broader algorithmic decision-making processes. It’s essential to establish rigorous governance processes that can quickly identify when a model begins to fail, complete with defined operating controls on inputs (data) and output (model results). Regulators are currently looking to existing laws and regulations to define the parameters, or set the “guardrails” by which they will evaluate system outputs. Algorithmic Impact Assessments may become an abstract exercise, which does not account for the harms algorithmic systems can engender in practice. A novel framework uses the cyclical, infrastructural and engineering nature of dataset development to draw on best practices from the software development lifecycle. Two aspects of clinical artificial intelligence used for decision-making are moral accountability for harm to patients and safety assurance to protect patients against such harm. It is also made the case that AI use is not inherently wrong, as part of a predictive approach to counterterrorism on the part of liberal democratic states. It will be important for governments to consider and prepare for the implications of AI on society, work, and human purpose.

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


ArtifIntel
“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