How do you increase AI user trust and satisfaction?

As humans, we often find it disconcerting that some AI models can predict outcomes accurately but cannot tell the reasoning behind their conclusions. This distinction is very different from our human way of thinking. In human cognition, understanding and explaining are intertwined. We tend to trust decisions or actions when we understand the reasoning behind them. When I teach this object I often use this example: "I will fail you in this class". The student's immediate response is "Why?" The student wants an explanation, a rationale. Without this, the student might feel the decision is arbitrary or unfair. Explainable AI aims to provide such explanations, so we can trust in the systems and be sure that it considers the regulations.

To boost AI user trust and satisfaction at PwC & iVCV & uphold responsible AI, we must prioritize transparency, ethics, and continuous improvement. We're on a mission to provide AI solutions that are not just cutting-edge but also reliable and ethical. Transparency is key – we'll openly communicate how our AI systems work, their limitations, and data handling. We'll implement robust ethical guidelines, ensuring fairness, privacy, and bias mitigation in AI. Our relentless commitment to user feedback and iterative refinement will lead to AI solutions that consistently exceed expectations, fostering trust and satisfaction among our users. We won't settle for less because our aim is to set the gold standard for responsible and user-friendly AI.

Implement explainable AI (XAI) techniques like showing feature importance, decision trees, example-based explanations, etc. This provides visibility into how the AI arrived at decisions. Clearly communicate limitations, uncertainties, and potential biases in the AI system upfront. Being transparent builds credibility. Allow users to ask "why" questions about AI behavior and have the system provide explanations in an understandable way.

The main goals of XAI are: - To increase transparency - XAI aims to open the "black box" of AI systems by explaining their internal logic, reasoning, and decision-making processes in an understandable way. To build trust - By making AI more interpretable and transparent, XAI helps users gain confidence in AI systems, understand its strengths/limitations, and make informed decisions about its use. To enable auditing - XAI facilitates inspection of AI systems for issues like bias, errors, or unfairness, allowing them to be debugged and improved. To support collaboration - XAI allows AI developers and subject-matter experts to work together more effectively to build safe and useful AI applications.

I am absolutely positive and encouraged about AI and other innovation. Anything new or old, alive or not (or in between these 2 states))) can be used for good or bad. To increase user trust we need to help people to use AI more in their everyday life. Or better show them how they have already been using it for decades. Then they will be mostly satisfied and also they will see how today AI is still a baby so that even those who want to do something bad using it still must do most of the heavy lifting. However, having said that I did not mean we should not control anything. We must control and regulate everything (including AI) as much as it is needed - but not more)))

Trustworthiness is a critical component of AI explainability, ensuring that AI not only offers explanations but also consistently makes reliable, ethical, and compliant decisions. Understanding why AI makes specific decisions is essential. It's about trust, accountability, and fairness in the systems we design and build. Without knowing how the decision was made, do we really want to trust the black box? Reliability: AI systems should provide explanations that are accurate, reliable, and consistent in different situations.

The need for XAI is highly correlated with the impact of the AI solution. For example if we are building an AI movie recommendation engine, the impact of the engine recommending the wrong movie is low so I may not be bothered with an explanation of how the model works. But if we are building an AI model with high impact such as illness diagnosis in healthcare or making millions dollar decision then we need to understand how the model works and how it derives its outcomes.

Imagine running an org with a partner that recommends decisions, but doesn't explain why. That's the challenge with AI today. In my recent keynote on crafting an AI strategy, I explained why trust is the currency of the AI realm. The importance of XAI isn't just to satisfy curiosity or regulatory demands. XAI is fundamental for businesses to build trust in AI.

AI is a very complex algorithm and it's not always easy to find where a decision originated from. I agree that finding a way to transparently look into its working would be optimal as the programmers, However at the same time I would argue that most engineers and people don't know the math that goes behind machine learning and ML models. Scrutiny and security is important but it should not impede our drive to continue growing as it hasn't in the past. AI should have all the controls in place so we can explain it's reasoning for audit purposes but not having such controls hasn't stopped us from developing other technologies. So my question is, why AI?

On the topic of legal changes, as AI becomes more pervasive, many countries are working on or have implemented AI-related legislations. These laws often emphasize transparency, fairness, and the right of individuals to understand decisions made by AI that affect them. This has made AI not just a matter of building trust but also of regulatory compliance. Companies that don't prioritize explainability might find themselves at odds with these legal standards, potentially facing penalties or restrictions. Thus, the push towards AI is both a practical and a legal necessity in the evolving landscape of AI governance.

While explainable AI techniques are crucial, boosting user trust goes beyond just the technical aspects. Incorporate user-centric design practices to make AI interfaces intuitive, and offer users options to understand how decisions are made. Use plain language to explain the algorithms at work, maybe even provide case studies that detail how the AI arrived at specific conclusions in a relatable way. Additionally, consider implementing real-time feedback loops where users can validate or challenge AI decisions. This not only builds trust but can also serve as a valuable data source for ongoing AI refinement. By integrating both technical transparency and user-focused features, you offer a more rounded, trustworthy AI experience.

Communicating the intricacies of AI to users is quite like the challenge of translating complex financial jargon to shareholders. Just as an effective CFO breaks down convoluted financial strategies into digestible insights to build shareholder trust, so should AI practitioners elucidate their AI models.

Walk them through how AI is used to make decisions. Transparency: AI systems should provide clear and accessible explanations of their decision-making processes. Traceability: There should be a trail of information that allows users to trace the AI's reasoning back to its source data and algorithms. Understandability: Explanations should be in terms that users can understand and should match their expertise. Reliability: AI systems should provide explanations that are accurate, reliable, and consistent in different situations.