Deliver Responsible AI
Deliver Responsible AI
Shape a Culture of Accountability with Continuous Responsible AI
AI impacts lives. It’s more important than ever to build AI responsibly and adhering to this duty requires the detection and mitigation of bias, support for internal governance processes, and the reduction of risk through human involvement.
The Fiddler AI Observability platform brings ethics to the forefront. Through continuous real-time model monitoring, you ensure precise and rapid detection of bias in both datasets and ML models. With Fiddler, AI outcomes and predictions can be fair and inclusive.
Build and Deploy Responsible AI Solutions
De-risk AI with Hidden Disparities
It’s almost impossible to ensure fairness in ML models if you don’t understand how models are behaving or why certain predictions are made. How can model bias be detected and assessed if you can’t extract causal drivers in your data and models?
Fiddler reduces model risk by enabling the deployment of AI governance and model risk management processes. Not only are coverage and efficiency increased, but human input into the decision-making loop for ML is enabled.
- Explain models in human-understandable terms to increase trust and transparency.
- Automate documentation of prediction explanations for governance requirements.
- Increase transparency and visibility into even the most complex models with explainable AI.
Develop Clear Guidelines for Fairness
No one wants to manage a PR catastrophe or incur fines and penalties.
Fiddler provides practical tools that support internal model governance processes and we provide practical tools, expert guidance, and white glove customer service to develop responsible AI practices.
Fiddler integrates deep explainable AI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI.
- Roll back models, data, and code to reproduce predictions and determine if bias was involved.
- Understand and explain decision-making factors to address customer complaints.
- Save money from fines and penalties by reducing occurrences.
Measure Fairness Metrics
How nice would it be to select multiple protected attributes at the same time to detect hidden intersectional unfairness? Or to benefit from fairness metrics when analyzing model performance?
With Fiddler, you can compare and measure a multitude of fairness metrics and evaluate, detect, and mitigate potential bias in both training and production datasets.
- Find deep-rooted biases with model performance metrics and analysis across protected classes.
- Deliver access to standard intersectional fairness metrics such as disparate impact, equal opportunity, and demographic parity.
- Provide model change and policy controls, coupled with analytics and reporting.
Fairness Features
Featured Resources
Frequently Asked Questions
What is Responsible AI?
Responsible AI is the practice of developing, deploying, and maintaining artificial intelligence in a way that is ethical, transparent, fair, and accountable. It ensures AI systems align with societal values, support human decision-making, and avoid causing harm. This includes detecting and mitigating bias, enabling explainability, ensuring data fairness, and complying with governance standards.
What can Responsible AI help mitigate?
Responsible AI helps mitigate model risks such as bias, discrimination, and lack of transparency, that can lead to regulatory non-compliance and monetary or reputational damage. With the right tools, organizations can proactively detect hidden disparities in models, understand causal drivers of predictions, and adjust model behavior to ensure outcomes are fair and inclusive across diverse user groups.
Why is it important to combine Responsible AI with generative AI?
Without proper safeguards, generative AI can produce harmful responses and generate misinformation. Combining Responsible AI practices with generative AI provides transparency into what content is being generated, ensures LLM responses are aligned with ethical and organizational standards, and moderates risks like toxicity or hallucinations.
Why are Responsible AI practices important to an organization?
Responsible AI practices reduce risk, enhance trust, and ensure AI aligns with legal, ethical, and business goals. Organizations that adopt Responsible AI practices can improve model transparency, avoid or reputational damage, and maintain confidence in deploying AI to production.
How can Responsible AI support regulatory compliance?
Responsible AI provides the tools and documentation needed to meet compliance standards around fairness, transparency, and accountability. Features like explainability, bias detection, audit trails, and model rollback help organizations demonstrate due diligence, respond to audits, and avoid fines or penalties in regulated industries such as finance, healthcare, and education.