Understand AI decisions before it takes decisions for your customers
“Responsible AI (RAI) is the only way to mitigate AI risks. Future payoffs will give early adopters an edge that competitors may never be able to overtake.”
“Earn and keep your customers' trust. Lack of trust in AI systems is a growing barrier to adoption in enterprises with more organizations selecting enterprise products based on AI commitments and practices. A responsible AI approach earns trust.“
“To create trust in AI, organizations must move beyond defining Responsible AI principles and put those principles into practice. To build trust among employees and customers, develop explainable AI that is transparent across processes and functions.”
of global customers think organizations must be held accountable for their misuse of AI
Accenture’s 2022 Tech Vision research
of AI projects will deliver erroneous outcomes through 2022 due to bias in data, algorithms or the teams responsible for managing them
identify AI as the biggest potential cause of unintended consequences. Only a handful are fully capable of assessing AI-adoption risks
Global survey of risk managers
Responsible AI (RAI) is a proven way to remedy unreliable models that silently fail over time and impact the customers’ experience with faulty decisions.
AI-led decisions need to be evaluated on different fronts like business risks, safety issues, data health, and equality. RAI enables concrete AI decisions that are free from biases and risks, leading to higher customer satisfaction over time.
With RAI, teams develop, deploy, and scale AI for constructive causes and good intentions to impact people and society positively. It nurtures people’s trust and confidence in AI by transforming AI applications into more accountable, ethical, and transparent systems.
Find out with Responsible AI
Is AI supporting accurate decisions?
Is AI violating any policy or privacy?
Is there a way to monitor AI and its outcomes?
The crux of trustworthiness
Localizes the root cause of biases and identifies issues such as sampling imbalance, data irregularities, and partial sources.
Provides complete transparency at global, regional, and local levels with a step-by-step view behind every model decision.
Implements risk identifiers such as drift, quality, and performance monitors to thoroughly track and trigger anomalous behavior.
Enables hierarchical accountability and partial controls to minimize risks and maintain a high-speed recovery environment.
Supports end-to-end security for data and model artifacts to ensure complete privacy for customers and the enterprise.
Our approach to Responsible AI
Censius is a Responsible AI partner for enterprises, helping AI/ML teams to swiftly cross the bridge from unreliable, biased, and risk-prone AI models to a scalable ecosystem of trustworthy AI solutions.
Censius offers an end-to-end AI observability platform that delivers automated monitoring and proactive troubleshooting to build reliable models.
- Continuously monitor performance, data quality, and model drifts
- Track for prediction, data, and concept drifts
- Real-time alerts for violations
- Track performance across model versions
- Ensure models are running correctly, even without ground-truth
- Compare a model's historical performance
- Understand the "why" behind model decisions
- Improve performance for specific cohorts
- Ensure that models stay compliant
AI Observability Guide
A proven route to implement Responsible AI
Get your hands on our thorough AI Observability guide for the following takeaways
Top industry-wide practices
Critical novel concepts
Business impact of observability
What do you mean by Responsible AI?
Responsible AI (RAI) brings practices to develop, deploy, and scale AI for good causes that impact people and society fairly
Are We Ready For AI Explainability?
AI is scaling, yes. But are we ready to shift our focus on explainability?
Model Monitoring in Machine Learning - All You Need to Know
What can go wrong with models in production? What needs to be monitored and Why?