The gaming industry has seen tremendous growth in recent years, but with this growth grew the problem of toxic behavior in gaming. In online games, players usually face hate speech, harassment, cheating, and other negative behaviors that can significantly impact their gaming experience. The demand for gaming moderation models and data annotation of user-generated content in games has become increasingly crucial to ensure that gaming companies do not lose business while staying on top of regulations and the social impact their games make.
AI-powered solutions, such as chat filters and machine learning algorithms, have the potential to detect and analyze toxic behavior in gaming effectively. However, these solutions require annotated data to perform better, and data annotation is one of the most effort-intensive parts of AI/ML projects. Typically in any machine learning project, 70% of the time spent will be on data annotation. Yet, most companies often ignore data annotation best practices until they start to impact results or cause problems.
We will look at data annotation best practices to incorporate into your gaming moderation project to ensure that the model accurately identifies toxicity.
Defining Clear Guidelines
A clear and well-defined set of guidelines is essential to ensure consistency and accuracy in moderation efforts. It includes definitions of what constitutes inappropriate behavior and specific annotations for each type of behavior. For example, the guidelines may specify that racial slurs are unacceptable and should be annotated as hate speech, while more general insults or teasing may not be considered hate speech.
Having clear and specific guidelines for annotations helps to ensure that annotators can consistently identify and label hate speech while also providing the AI model with a clear understanding of what constitutes hate speech.
Utilizing best-in-class annotation tools
Investing in good-quality tools and technology is crucial. It increases productivity and collaboration that, help speed up the annotation process, and ensures that the training data set is of high quality. Annotation tools also allow one to manage workflows, track progress, and stay on top of deadlines.
By using best-in-class annotation tools, you can ensure that your data is annotated to the highest standards, leading to better performance of your machine learning models.
The team at iMerit is tool agnostic, which means we leverage a range of tools to make data labeling as painless, easy, and fast as possible for our clients. iMerit has its tools, works with client tools, and has a robust ecosystem of tooling partners, including AWS Sagemaker, Appen, Dataloop, Deepen, SuperAnnotate, etc., allowing us to cater to any project across industries.
Full-time Annotation Workforce and SMEs
Leveraging highly-skilled annotators can increase the accuracy of annotations and reduce turnaround time. Best practices suggest that the annotation team undergo training with subject matter experts having experience in gaming content moderation.
Our robust skilling structure provides labeling specialists and domain experts to our clients. iMerit’s highly skilled full-time employees across the US, India, and Bhutan, with over 92% retention rate, make it easier to ramp up data labeling projects without compromising quality. It allows long-term solutions with a knowledge ramp-up curve as employees achieve deep expertise on customer needs.
Data Security
A data annotation company should comply with regulatory requirements based on the level of security your data requires. They also should have a documented data security approach for people, technology, and infrastructure. Look for certifications like SOC 2 Type II and ISO 27001:2013 to ensure high-security protocols.
Quality Control – Closed feedback cycle
Monitoring and evaluating the effectiveness of data annotation processes and tools is critical to constantly improving moderation efforts. It may include collecting user feedback, conducting audits of moderation efforts, and regularly evaluating the performance of automated solutions. iMerit employs an evaluation model that continually assesses deliverables, key metrics, quality control processes, and business outcomes.
By adhering to these best practices in data annotation, gaming content/behavior moderation teams can effectively and efficiently identify and mitigate inappropriate behavior in online gaming communities, creating a safer and more positive user experience.
Partnering with a Data Annotation Company
Data annotation is a complex and time-consuming task requiring a high level of expertise. Gaming companies looking to tackle toxic behavior in gaming should consider partnering with ML operations companies with the right expertise and experience in data annotation of user-generated content across languages.
Conclusion
The gaming industry is taking steps to address the problem of toxic behavior in gaming, but there is still much work to be done. Many gaming companies use AI solutions to detect and analyze inappropriate behavior, and some partner with data annotation companies to improve their AI model performance in detecting toxicity. However, ever-evolving language and player behavior require gaming companies to stay ahead of the curve and adopt the latest tools and technologies, including AI and data annotation. iMerit combines the best predictive and automated annotation technology with world-class data annotation and subject matter experts to create safer gaming platforms.
