It's critical that AI is developed and used responsibly. To that end, we've established guiding principles for the Toloka platform, actionable insights that we share with the community, and research grants to support the latest discoveries in the field.
We consider it a privilege to contribute to the AI community with responsible data production that supports ethical approaches to training, testing, and monitoring AI.
Technological innovation at Toloka is rooted in reproducible scientific research with a commitment to open inquiry, intellectual rigor, integrity, and collaboration. We share our findings with the AI community and support research groups all over the world with Toloka grants.
AI models are shaped by what their training data leaves out and what it over-represents. Human biases within the data, model design, data drift, explainability of the predictions, and methods for training and testing can lead to unethical outcomes. We address bias with a two-pronged approach: fair representation of our diverse crowd from different backgrounds distributed across 200 countries, and reliable results achieved using aggregation and our proven quality control methods.
AI is more human than one might think. Even with the rise of self-supervised learning approaches, the need for human-powered data labeling continues to grow. Our priority is to make sure that data labelers are treated well by maintaining fair wages, facilitating skill development, giving them a platform to be heard by AI companies, and supporting regional communities.
We support model reproducibility and invest in the AI community by sharing public datasets and open-source code for our data quality algorithms, plus our open API and integrations. We want everyone to access the technologies we develop because together we can shape the future of the AI industry.
Trust, Ethics, and Responsibility workshop series at VLDB, ICML, NeurIps
Toloka CEO on monitoring model quality for responsible AI, VentureBeat Data Summit
Impactful civic AI research by Northeastern and UNAM in collaboration with Toloka
Toloka presents new reproducible dataset at prestigious Data-Centric AI workshop launched by Andrew Ng
Crowd-Kit Python library offers efficient data aggregation and quality metrics to improve reliability