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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in

Michael Auer1, Mark D Griffiths2

  • 1neccton GmbH, Davidgasse 5, 7052, Muellendorf, Austria.

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|July 19, 2022
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Artificial intelligence (AI) algorithms can accurately predict problem gambling using player tracking data. Problem gamblers exhibit distinct behaviors like higher spending and more frequent deposits, which AI models identify effectively.

Keywords:
Artificial intelligenceOnline casinoOnline gamblingPlayer trackingProblem gambling

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Area of Science:

  • Computational social science
  • Behavioral economics
  • Digital health

Background:

  • Artificial intelligence (AI) is increasingly vital for detecting online problem gambling.
  • AI algorithms require robust datasets to identify gambling behavior patterns.
  • Problem gambling screens are a key method for collecting data to train AI models.

Purpose of the Study:

  • To identify significant behavioral patterns predicting self-reported problem gambling.
  • To compare the efficacy of random forest and gradient boost machine algorithms in predicting problem gambling.
  • To analyze real-world online casino player data to understand objective gambling behaviors.

Main Methods:

  • Analysis of player tracking data from 1,287 online casino players (September 2021 - February 2022).
  • Utilized the Problem Gambling Severity Index (PGSI) for self-reported problem gambling assessment.
  • Trained random forest and gradient boost machine algorithms using variables like wagering, depositing, and gambling frequency.

Main Results:

  • The random forest model demonstrated superior accuracy in predicting self-reported problem gambling compared to gradient boost.
  • Problem gamblers exhibited distinct behaviors: higher daily/session losses, more frequent session deposits, and depleted accounts.
  • A subgroup of high-harm problem gamblers showed amplified versions of these behaviors.

Conclusions:

  • AI algorithms can accurately predict self-reported problem gambling using objective player tracking data.
  • Distinct behavioral patterns in online gambling are indicative of problem gambling.
  • Player tracking data offers a valuable resource for developing effective AI-driven tools for problem gambling detection.