Building Explainable, Transparent & Accountable AI Models
Why Explainable AI?
We are witnessing a rise in AI-powered services and chatbots that inadvertently pose dangerous business, privacy, social, environmental, cultural, and political outcomes through data leaks, misinformation or bogus human-like interactions.
We addressed these concerns by researching and developing solutions for our enterprise customers using state-of-the-art techniques, such as explainability and interpretability methods, Human-in-the-loop (HITL) techniques, Fairness, Accountability, and Transparency (FAT) algorithms, and AI governance and oversight frameworks.
Explainable AI models are essential for our Human-centric Augmented Intelligence ecosystems as they enable us to identify potential issues or biases in the data and intervene immediately where necessary.
Reducing Your Legal Risks
We support our customers and partners with their explainable AI efforts, combined with our human-in-the-loop machine learning frameworks, to prepare for the upcoming AI regulations in various regions worldwide.
We invest in these models to bring several business benefits to our customers, including reducing their legal and regulatory risks, improving their customer trust and retention, and mitigating reputational risks. We apply these models throughout our business operations and research and it is the core part of Nebuli’s services.
Human-in-the-Loop
Nebuli’s human-in-the-loop approach helps to save time and costs by automating certain aspects of decision-making while still allowing human experts to provide valuable insights and produce final decisions.
We strongly believe that having human oversight and involvement in any AI system’s output generation and decision-making process helps to mitigate potential biases or errors and enhance the transparency, accountability and explainability of any deployed AI system. This is part of Nebuli’s benchmark of building effective collaborations between humans and machines.