Leveraging AI With Transparency
We only use AI to keep research human
At Terapage, the accuracy and reliability of AI in a research process is a key criteria that determines the validity of research outcomes and one which our esteemed clients and their stakeholders expect from us. Hence, we have put in place various measures to uphold and strengthen the accuracy and reliability of leveraging AI in online research.
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Where and how we use
Artificial Intelligence
Unlike our competitors Terapage is not built on Artificial Intelligence. Terapage is built with Artificial Intelligence. Our use of AI is strategically and considerably done to preserve authenticity of research insights and researcher's perspective.
- Research ideation space: We leverage AI to help researchers exchange their research ideas and design with Tera (our AI bot). Tera is plugged to a lot of data sources within the context of research and best practices.
- AI-aided moderator probes: We utilise AI in live chats, instant messaging, discussions and forums only to prompt researchers towards questions, comments and contributions that may yield more authentic insights.
- AI transcription: We utilise AI to accurately transcribing interviews and discussions in a manner that automatically detects languages, slangs, cultural nuances and sentiments.
- Summarise interviews and discussions: We use AI to accurately summarise interviews and discussions in a way that preserves the meaning, subject, context and still easily assimilated by the researcher.
- AI-aided data analysis and presentation: We use AI to aid quantitative and qualitative data analysis and visualisation towards improving the insights.
- Automated interview: We use AI to facilitate the automated interview module. We do this in a way that preserves the meaning, subject, context, detects languages, slangs, cultural nuances and sentiments.
- Synthetic data generation: We use AI to facilitate the generation of synthetic data. This includes the creation of AI personas, attributes, characteristics to enable the simulation and generation of AI responses. We do this in a way that preserves the meaning, subject, context, detects languages, slangs, cultural nuances and sentiments.
We uphold authenticity
and transparency
Some vital answers to the questions that you might have:
- Terapage seamlessly integrates AI into its user experience, eliminating the need for specialized training. We provide extensive resources to support users in leveraging this feature effectively.
- Terapage offers customers the flexibility to opt-in or opt-out of AI features on their platform.
- Terapage is committed to obtaining prior consent before utilizing customer data to train an AI model.
- We assure you that our AI implementation is meticulously aligned with all contractual obligations related to customer data ownership, security, and privacy. Furthermore, we maintain agreements with our sub-processors to ensure data is not retained for model training, and all AI services comply with our customers' privacy, confidentiality, and security requirements, including those outlined in HIPAA, GDPR, and CCPA.
Rigorous AI training
and testing
Our AI systems undergoes rigorous training and testing with diverse and comprehensive datasets. This robust training and testing process is designed to minimise errors and optimise the accuracy of the analysis.
Get Started For FreeRegular updates and improvements
We continuously update and refine our AI algorithms based on feedback and new developments in the field. This commitment to continuous learning and adaptation helps us ensure that our system remains accurate and reliable.
Transparency in AI methodology
We believe that transparency is key to trust. That is why we ensure that our AI processes are explainable, making it clear how our systems arrived at a certain conclusion. This helps our users to understand the AI's decision-making process and verify its accuracy.
Get Started For FreeUser control and customisation
While our AI provides powerful data analysis capabilities, we believe that human oversight remains essential. Where applicable, our platform allows users to review, modify, and validate the AI’s contributions. This helps to address potential biases or errors that might arise from the data analysis.
Addressing bias in AI
To tackle the issue of bias, our AI models are designed and trained to identify and minimise potential biases. We perform regular audits to detect any hidden biases in the algorithms, and we are committed to continually refining our AI models to ensure they provide equitable and unbiased results.
Get Started For FreeData quality checks
Before any data is analysed, it is subjected to quality checks to identify any anomalies or inconsistencies that could impact the analysis. This helps to ensure that the data going into the analysis stage is as accurate and reliable as possible, before being aided by AI.
Peer review and collaboration
We encourage collaboration on our platform, providing options for peers to review and validate various research activities, including but not limited to participant selection and recruitment, study design, data collection, data analysis and findings. This process helps to mitigate errors or biases in the research process.
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