Seclea Explainable and Responsible AI

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AI application development is complicated by diverse stakeholder requirements, including fairness, explainable & responsible AI, and regulations. Seclea’s cutting-edge research-backed platform makes it simple for your team to meet these requirements.

Seclea seamlessly integrates with your existing machine-learning pipelines. It runs in the background so your data scientists and ML engineers can focus on achieving their core objectives.

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Product Description

AI application development is complicated by diverse stakeholder requirements, including fairness, explainable & responsible AI, and regulations. Seclea's cutting-edge research-backed platform makes it simple for your team to meet these requirements.

Seclea seamlessly integrates with your existing machine-learning pipelines. It runs in the background so your data scientists and ML engineers can focus on achieving their core objectives.

  1. Fairness & Non-Discrimination: Identify and mitigate bias in your data models - at any stage of the AI lifecycle, from raw data to production models. Ensuring your AI applications are fair, achieving compliance, reducing AI risk, and improving stakeholder trust.
  2. Explainable AI with Traceability: Explain AI models and their decisions, answering questions like Why does a model behave in a certain way? What are the reasons behind individual decisions? How can I trace model behavior and decisions back to model training and data?
  3. Risk Management: Identify, track and manage your AI risks. Mitigation activities are automatically tracked and the risk register is updated in real-time.
  4. Regulatory Compliance: Semi-automatic compliance platform, identifying, tracking, and recording AI compliance activities. Track and report on your pre- and post-market applications in real time.
  5. Continuous Monitoring: Monitor your AI application post-market to manage regulatory compliance, AI risk, and performance. Manage activities of your post-marketAI applications with complete visibility and assurance.
  6. Audit & Accountability: Identify what human, data, and model development & training activities lead to certain model behaviors and decisions—track back an issue to its root cause with full accountability.

Technology Features:

  • Tools for building Responsible and Explainable AI solutions
  • Ensuring Fair and transparent AI
  • Making AI explainable and trackable
  • Auditing AI applications
  • Identity, track and manage AI risks
  • Monitor AI applications at all stages of their lifecycle
  • Ensure regulatory compliance with global regulations, standards, and guidelines

Business Outcomes:

  • Trustworthy Artificial Intelligence (AI)
  • AI application compliant with global regulatory requirements
  • De-risking AI application development and adoption