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The Moral Choice Machine.

Patrick Schramowski1, Cigdem Turan1, Sophie Jentzsch1,2

  • 1Department of Computer Science, Darmstadt University of Technology, Darmstadt, Germany.

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Summary
This summary is machine-generated.

Machine learning can extract ethical reasoning from human texts to understand moral choices. This approach, the Moral Choice Machine (MCM), quantifies and tracks biases across time and cultures.

Keywords:
AIfairness in machine learningmachine learningmoral biasnatural language processingtext-embedding models

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Area of Science:

  • Artificial Intelligence
  • Ethics
  • Natural Language Processing

Background:

  • Equipping machines with ethical decision-making capabilities is crucial for global security.
  • Understanding the nuances of human morality is complex.
  • Current AI lacks robust ethical reasoning frameworks.

Purpose of the Study:

  • To develop a method for extracting deontological ethical reasoning from human texts.
  • To quantify moral biases present in language.
  • To analyze the evolution and cultural variations of ethical choices.

Main Methods:

  • Utilized machine learning and the Universal Sentence Encoder to analyze human texts.
  • Created a template of prompts and responses to assess "right" and "wrong" conduct.
  • Developed the Moral Choice Machine (MCM) to calculate bias scores based on contextual sentence embeddings.

Main Results:

  • Text corpora accurately reflect societal ethical and moral choices, even with context.
  • The MCM successfully extracted and quantified moral biases.
  • Training data from 1510-2009 revealed the evolution of ethical choices over time.
  • Analysis of diverse cultural sources highlighted cultural dynamics in moral decision-making.

Conclusions:

  • Human texts contain recoverable imprints of social, ethical, and moral choices.
  • Moral biases can be extracted, quantified, tracked, and compared across cultures and historical periods.
  • This research provides a foundation for developing more ethically aligned AI systems.