Toloka’s Commitment to Responsible AI: How We Prioritize Ethics, Safety, and Excellence

Toloka’s mission is to empower businesses with high-quality data to develop safe, responsible and trustworthy AI products.

In fact, we have championed Responsible AI across 10 years of company growth by focusing on data excellence, leading the way in social impact with global opportunities for data annotators, and contributing to the AI community with scientific research. 

We recognize our role in shaping the future of AI alongside our customers, and we uphold the highest standards of privacy, security, safety, and fairness in AI development. 

Safety and evaluations: Ensuring AI integrity

Above all else, we support our customers in meeting the highest safety and performance standards for their AI systems. Our data and evaluation services go beyond standard assessments, providing a framework for enhancing AI reliability and performance.

• Our comprehensive risk assessments and safety evaluations are customized to the model’s use case—coding, visual models, reasoning, or industry-specific needs. 

• In rigorous testing and evaluations, we focus on real-world scenarios to ensure AI systems are ready for deployment.

• We collaborate with partners to develop robust safety benchmarks and domain-specific benchmarking tailored to specialized use cases. We are continuously raising the bar of industry standards on safety and reliability. 

• AI Agents benefit from our pioneering safety evaluations as we advance best practices through red teaming, benchmarking, and other techniques for agentic safety.

Learn more about our approach to evaluation and safety.

Community impact: Fair treatment of the humans behind AI

Human insight remains essential in AI development. Even with advances in self-supervised learning, human expertise is critical for evaluation pipelines and domain-specific datasets.

We built the Mindrift platform as a global community for data production where domain experts contribute to AI projects focused on their areas of expertise. We prioritize the well-being of our experts, annotators, and contributors by guaranteeing fair hourly rates, flexible working conditions, and ethical task design.

• Contributors around the world find opportunities for flexible part-time engagement, with fair and transparent compensation.

• Ethical task design ensures that AI annotators and experts work in environments that prioritize safety and respect. We verify that contributors meet legal age requirements and limit their exposure to explicit or potentially toxic content.

• Contributors work within structured task pools that match their expertise, and they can opt out of tasks without penalty.

• Open communication means contributors have access to real-time communication channels for collaboration on tasks, and they can always provide feedback to improve our processes and workflows.

Transparency is paramount. We are open about our processes and people, ensuring fair treatment of all contributors. 

Learn more about the Mindrift platform.

Data excellence: Diversity and reliability

We know that our data is behind powerful technology, and we take that responsibility very seriously. By delivering high-quality data that is accurate, truthful, and free of biases, we help AI applications remain grounded in real-world facts and perform effectively in diverse scenarios. 

Here are some of the ways we support data quality in data production pipelines:

• Our global community of domain experts and contributors are vetted and tested to verify their qualifications. Then they complete  targeted onboarding specific to each project they participate in.

• Quality Assurance specialists verify tasks in tailored quality workflows that are designed to meet the unique requirements of each project.

Platform-wide anti-fraud measures protect data integrity:

• Our algorithms leverage behavioral signals and predictive methods to accurately detect and mitigate fraudulent activity on the data production platform.

• Automated anomaly detection catches problematic behavior among contributors.

• Our anti-fraud team investigates flagged activity case by case, and the results of every investigation are fed into algorithms to continuously improve fraud protection.

Discover how we deploy high-quality data for real-world applications in our Success Stories.

Scientific research: Driving the AI industry forward

Technological innovation at Toloka is rooted in reproducible research with a commitment to open inquiry, intellectual rigor, integrity, and collaboration. Our research team pushes our services to the forefront of the industry with pioneering approaches to data collection, model evaluation, and fine-tuning.

Scientific research has been at the core of our identity since Toloka was founded in 2014. To promote responsible AI development through collaboration, we prioritize the following initiatives: 

• Our research fellowship program supports global AI researchers.

• We contribute to the AI ecosystem by sharing high-quality datasets and post-training methods with the research and open-source community.

• Our research papers are published at top AI conferences (NeurIPS, ICML, CVPR, NAACL, AAAI, and others).

• We collaborate with leading institutions like Hugging Face, Meta AI, Microsoft, Cohere for AI, MIT, Penn State, University of Oslo, and the University of Amsterdam.

We also share insights through public media and educational webinars. Explore our latest research initiatives.

Privacy and security: A foundation of trust

Responsible data production starts with an unwavering commitment to data privacy and security. We integrate privacy principles at every stage of our processes, making data protection a core consideration from the outset. 

Key elements of our approach include: 

• Our dedicated security and privacy team continuously monitors, assesses, and enhances security measures across all systems and workflows.

• We employ a robust security framework that includes strict access controls, data encryption, and regular security assessments. 

• To minimize risks to personal data, we prioritize the use of de-identified and synthetic data for AI training whenever possible.

• We ensure that all third-party partners for AI systems and AI tools or add-ons comply with our stringent privacy and security standards through rigorous vendor assessments.

Learn more on our Security and Privacy portal.

Shaping a responsible AI future

At Toloka, our approach to responsible AI is built on trust, security, excellence, and fairness.  By adhering to responsible practices, we are driving AI forward in a way that benefits businesses and society as a whole.

Together, we can create a future where integrity is at the core of technology.