Driving Responsible AI

Driving 
Responsible AI

For over 10 years, Toloka has championed ethical data production to drive Responsible AI.

Responsible AI made actionable with Toloka

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

Privacy & security

Privacy and security principles are integrated into every aspect of our data handling processes. The Toloka and Mindrift platforms are built on a robust security framework that includes strict access controls, data encryption, and regular security assessments.

Fair treatment of the humans behind AI

Toloka strives to empower people worldwide with flexible opportunities to earn extra income on the Mindrift platform. We prioritize the well-being of our contributors by guaranteeing fair hourly rates, flexible working conditions, and ethical task design. Our Code of Ethics is built on fair cooperation, inclusion, privacy and confidentiality.

Safety for LLMs and AI agents

We develop robust safety benchmarks, tailored evaluations, and comprehensive risk assessments to continually raise the bar of industry standards on safety and reliability. With pioneering safety evaluations for AI agents, we advance best practices through red teaming, benchmarking, and other techniques for agentic safety.

Research efforts

Technological innovation at Toloka is rooted in reproducible scientific research with a commitment to open inquiry, intellectual rigor, integrity, and collaboration. We share our research with the AI community and offer open-source datasets and benchmarks.

Our contributions to responsible AI development

Custom Safety Evaluations for AI Systems and Agents
Custom Safety Evaluations for AI Systems and Agents
Custom Safety Evaluations for AI Systems and Agents
Custom Safety Evaluations for AI Systems and Agents
Hands-on Tutorial: Labeling with LLMs and Human-in-the-Loop
Hands-on Tutorial: Labeling with LLMs and Human-in-the-Loop
Hands-on Tutorial: Labeling with LLMs and Human-in-the-Loop
Hands-on Tutorial: Labeling with LLMs and Human-in-the-Loop
Responsible AI: Driving your business with safe and ethical AI
Responsible AI: Driving your business with safe and ethical AI
Responsible AI: Driving your business with safe and ethical AI
Responsible AI: Driving your business with safe and ethical AI
Democratizing LLMs With Human Insight
Democratizing LLMs With Human Insight
Democratizing LLMs With Human Insight
Democratizing LLMs With Human Insight
Creating Responsible AI Products Using Human Oversight
Creating Responsible AI Products Using Human Oversight
Creating Responsible AI Products Using Human Oversight
Creating Responsible AI Products Using Human Oversight
Testing The Limits: Three Ways AI Benchmarks Are Evolving
Testing The Limits: Three Ways AI Benchmarks Are Evolving
Testing The Limits: Three Ways AI Benchmarks Are Evolving
Testing The Limits: Three Ways AI Benchmarks Are Evolving
Forbes: 4 Cornerstones for Building a Future Where We Can Trust AI
Forbes: 4 Cornerstones for Building a Future Where We Can Trust AI
Forbes: 4 Cornerstones for Building a Future Where We Can Trust AI
Forbes: 4 Cornerstones for Building a Future Where We Can Trust AI
Aligning LLMs to Low-Resource Languages
Aligning LLMs to Low-Resource Languages
Aligning LLMs to Low-Resource Languages
Aligning LLMs to Low-Resource Languages
Open Datasets and Benchmarks on Hugging Face
Open Datasets and Benchmarks on Hugging Face
Open Datasets and Benchmarks on Hugging Face
Open Datasets and Benchmarks on Hugging Face