Responsible AI (RAI) brings practices to develop, deploy, and scale AI for good causes that impact people and society fairly
What is Responsible AI?
Responsible AI (RAI) designs practices to 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 the AI system.
RAI helps transform AI applications into more accountable, ethical, and transparent. It evaluates organizational AI efforts from both ethical and legal points of view.
With the growing use of AI systems in all domains, many questions arise around AI ethics, trust, legality, and data governance. AI-led decisions need to be evaluated on different fronts like business risks, data privacy, health - safety issues, and equality.
AI applications are business-critical and deal with sensitive data. Therefore people need to understand the role of AI in depth.
Is AI supporting accurate decisions?
Is it violating any policy or privacy?
Is there any mechanism to monitor this technology and its outcomes?
Such questions are effectively addressed by a new governance framework- Responsible AI.
Why is Responsible AI Important?
With Responsible AI, enterprises set key objectives and establish governance strategies for AI initiatives. RAI enables:
Reduced bias in datasets
Responsible AI helps ensure that both algorithm and underlying data are unbiased and representative of ground truth.
RAI brings a security-first approach to ensure ethical use of data. It protects the privacy and security of your sensitive data to avoid its unethical use by any means. It helps mitigate risk and benefits people, organizations, and society as a whole.
Responsible AI drives transparency across processes and functions. It enables human-understandable explanations for predictions made in contrast to traditional black-box ML.
ML development processes should be documented to avoid their alteration for evil intention.
Models supporting AI initiatives should be adapted to complex environments without introducing bias.
Practicing Responsible AI
Responsible AI is an enabler of market growth, development, and competitive edge. RAI journey requires designing the following best practices for it.
- Building all-inclusive and diverse team
- Ensuring transparent and explainable AI systems
- Ensuring measurable processes and tasks wherever possible
- Developing and executing guidelines on how RAI is implemented in the organization
- Performing time-to-time RAI checks for ML algorithms and data platforms
- Using automated tools for fairness, monitoring, and explainability. AI observability solutions such as Censius AI Observability Platform can help here. It automates ML model monitoring for desired performance metrics
Ultimately RAI is getting AI in the right way for moral things.