Transparency in AI means making AI systems and their decision-making processes clear and understandable to all stakeholders. It involves disclosing how AI models are built, the data they use, and how decisions are made. Transparent AI helps build trust, ensures accountability, and allows users to understand and challenge decisions if necessary. In complex systems, transparency […]
Fairness in AI is the principle that AI-driven decisions should be free from discrimination and treat all groups equitably. It ensures that AI models don’t perpetuate biases or create unfair advantages for specific groups. Fairness can be approached in different ways depending on the context, such as striving for demographic parity or ensuring equality of
Algorithmic bias refers to a systematic and repeatable error in AI systems that produces unfair outcomes, often favoring one group over another. This bias can emerge from skewed data used in training or from flawed assumptions in the model itself. For example, a recruitment algorithm may unfairly prioritize certain candidates due to historical data reflecting
Project Overview: The virtual assistant will handle customer interactions for a hypothetical insurance company. The agent will manage tasks such as: Key Components: Custom iFrame
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Custom iFrame These chatbots cover common use cases in customer interactions for a B2B SaaS company. Pricing InquiryFor example: How much do your services cost? Demo RequestExample: Can I schedule a demo? Support InquiryEx: I need help with your product. Subscription CancellationEx: How do I cancel my subscription? Feature RequestEx: Can you add a new