Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps
It addresses risks and inefficiencies in AI integration for economic and societal applications, but the approach appears incremental.
The paper investigates value biases and multi-dimensional gaps in the human-AI ecosystem, highlighting how human perceptions of achievements and costs are encoded in AI systems, and proposes a value-driven strategy with cost awareness to address real-world problems.
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.