Data-Driven Game Development: Ethical Considerations

TL;DR

This paper discusses the ethical challenges of data-driven game development, highlighting issues like bias, transparency, and fairness, and explores potential solutions and open problems in the field.

Contribution

It provides a comprehensive discussion of ethical concerns in game data science and proposes directions for addressing bias, transparency, and fairness in player modeling.

Findings

Bias in algorithms can marginalize player groups

Black box models lack interpretability, affecting trust

Open problems remain in ensuring ethical data use

Abstract

In recent years, the games industry has made a major move towards data-driven development, using data analytics and player modeling to inform design decisions. Data-driven techniques are beneficial as they allow for the study of player behavior at scale, making them very applicable to modern digital game development. However, with this move towards data driven decision-making comes a number of ethical concerns. Previous work in player modeling as well as work in the fields of AI and machine learning have demonstrated several ways in which algorithmic decision-making can be flawed due to data or algorithmic bias or lack of data from specific groups. Further, black box algorithms create a trust problem due to lack of interpretability and transparency of the results or models developed based on the data, requiring blind faith in the results. In this position paper, we discuss several factors affecting the use of game data in the development cycle. In addition to issues raised by previous work, we also raise issues with algorithms marginalizing certain player groups and flaws in the resulting models due to their inability to reason about situational factors affecting players' decisions. Further, we outline some work that seeks to address these problems and identify some open problems concerning ethics and game data science.