Explainable AI

What is Explainable AI?

Explainable AI (XAI) includes ways to help people grasp AI decisions. Unlike old AI models that keep their workings secret, XAI tries to show how choices happen. This openness builds trust and keeps AI use ethical. XAI has many methods such as: Models You Can Get: These are easy-to-understand models, like decision trees or linear regression. They give clear reasons for their guesses. After-the-Fact Explanations: These ways explain stuff after the AI makes a guess. They use fancy methods like LIME and SHAP to make sense of tricky models such as neural networks and ensemble methods. Picture Tools: Graphs, charts, and other visuals that help folks see the connections and patterns the AI spots.

Why is Explainable AI important?

XAI matters for many reasons. It affects trust, compliance, and how well AI works. Here's why XAI is so crucial: Trust and Adoption: AI needs people's trust to catch on. A PwC survey shows 76% of folks see trust as a big roadblock for AI (PwC 2018). When AI explains itself, it helps folks understand and trust it more. This transparency makes AI decisions clearer to everyone. Regulatory Compliance: Some laws, like the EU's GDPR, say companies must explain computer-made choices (Goodman & Flaxman, 2017). XAI has an impact on helping businesses follow these rules. It makes it easier for them to show how their AI works and meets legal standards. Ethical AI Use: AI systems can sometimes spread biases from their training data without meaning to. Explainability helps spot and fix these biases making sure AI is used and (Binns, 2018). Better Decisions: Knowing why AI makes certain choices lets people make better decisions by mixing human know-how with AI smarts (Doshi-Velez & Kim, 2017). Fixing and Upgrading Models: Explanations help coders figure out why models predict certain things. This makes it easier to fix bugs and make the models better (Ribeiro, Singh, & Guestrin, 2016).

What are benefits of using Explainable AI?

XAI has lots of perks in different fields: XAI gives clear views into how AI makes decisions. This makes AI more open and responsible. Adadi and Berrada talked about this in 2018. XAI has an impact on trust. It makes AI easier to get. When people understand AI better, they trust it more. This leads to more people using AI. Doshi-Velez and Kim wrote about this in 2017. XAI helps companies follow rules. It explains why AI chose something. This cuts down on legal problems. Goodman and Flaxman pointed this out in 2017. Companies need to explain AI choices to stay out of trouble. XAI helps spot biases in AI models. This promotes fairness and cuts down on unfair results (Binns, 2018). Clear explanations boost teamwork between humans and AI. Users can make good use of AI insights while using what they know about their field (Ribeiro et al. 2016). Getting how a model works lets developers tweak and upgrade AI systems. This leads to better performance and more accurate guesses (Adadi & Berrada, 2018).

How Alltius AI Enables Organizations to use Explainable AI?

Alltius' provides leading enterprise AI technology for enterprises and governments to harness and extract value from their current data using variety of technologies Alltius' Gen AI platform enables companies to create, train, deploy and maintain AI assistants for sales, support agents and customers in a matter of a day. Alltius platform is based on 20+ years of experience at leading researchers at Wharton, Carnegie Mellon and University of California and excels in improving customer experience at scale using Gen AI assistants catered to customer's needs. Alltius' successful projects included but are not limited to Insurance(Assurance IQ), SaaS (Matchbook), Banks, Digital Lenders, Financial Services (AngelOne) and Industrial sector(Tacit).

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