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

TL;DR

This paper explores the economic implications of human-AI interactions, highlighting value biases and gaps in perception across multiple dimensions, and proposes strategies for more effective, value-driven AI development.

Contribution

It introduces a framework for understanding value biases in AI systems and advocates for rethinking goal definitions and problem-solving principles in AI development.

Findings

Identifies value biases in human perception of AI achievements and costs.

Proposes a value-driven, cost-aware strategy for AI problem-solving.

Highlights the importance of multidimensional gaps in AI economic modeling.

Abstract

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 costs that encoded in AI systems. It also proposes rethinking hard goals definitions and cost-optimal problem-solving principles in the lens of effectiveness and efficiency in the development of trusted machines. The paper suggests a value-driven with cost awareness strategy and principles for problem-solving and planning of effective research progress to address real-world problems that involve diverse forms of achievements, investments, and survival scenarios.