• Corpus ID: 53670121

Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps

@article{Muller2018EconomicsOH,
  title={Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps},
  author={Daniel Muller},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.06606}
}
In recent years, artificial intelligence (AI) decision-making and autonomous systems became an integrated part of the economy, industry, and society. The evolving economy of the human-AI ecosystem raising concerns regarding the risks and values inherited in AI systems. This paper investigates the dynamics of creation and exchange of values and points out gaps in perception of cost-value, knowledge, space and time dimensions. It shows aspects of value bias in human perception of achievements and… 

Figures and Tables from this paper

Automated Tactical Decision Planning Model with Strategic Values Guidance for Local Action-Value-Ambiguity
TLDR
An offline action-value structure analysis is proposed to exploit the compactly represented informativeness of net utility of actions to extend the scope of planning to value uncertainty scenarios and to provide a real-time value-rational decision planning tool.

References

SHOWING 1-10 OF 72 REFERENCES
Artificial Intelligence Techniques for Rational Decision Making
  • T. Marwala
  • Computer Science
    Advanced Information and Knowledge Processing
  • 2014
TLDR
Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
TLDR
This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI, and describes structural properties of M DPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans.
Explanation in Artificial Intelligence: Insights from the Social Sciences
Explainable Planning
TLDR
This paper considers the opportunities that arise in AI planning, exploiting the modelbased representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.
Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making
TLDR
The theory of marginalization of irrationality in decision making is proposed to deal with the problem of satisficing in the presence of rationality.
Time is Not Money: The Case for Multi-dimensional Accounting in Value-based Software Engineering
Time is money", or so goes the old saying. Perhaps influenced by this aphorism, some strategies for incorporating costs in the analysis of software design express all costs in currency units for
A mechanism for social selection and successful altruism.
TLDR
A simple and robust mechanism, based on human docility and bounded rationality, is proposed that can account for the evolutionary success of genuinely altruistic behavior.
Explaining Explanations in AI
TLDR
This work contrasts the different schools of thought on what makes an explanation in philosophy and sociology, and suggests that machine learning might benefit from viewing the problem more broadly.
Soft Goals Can Be Compiled Away
TLDR
It is shown that optimal and satisficing cost-based planners do better on the compiled problems than optimal and satisfying netbenefit planners on the original problems with explicit soft goals, and that penalties, or negative preferences expressing conditions to avoid, can also be compiled away using a similar idea.
...
1
2
3
4
5
...