How do you use user feedback to explain AI?

The ability to explain AI is key! Some regulated sectors such as financial services and healthcare may be required to explain how their algorithms derived decisions. Also, some regulations such as GDPR give the right to citizens to demand companies to explain how their algorithms derived outcomes if they believe that they have been unfairly impacted by the AI algorithms outcomes.

Just as a musician needs to understand his instrument to create harmonious music, users need to understand AI to effectively leverage its capabilities. Imagine visiting a foreign country without understanding the language; you may get by, but the experience is enriched if you understand and converse in the local tongue. Similarly, explainable AI can help users navigate its complex "language," making the technology less of a black box and more of an open book, fostering trust and maximizing utility.

As AI becomes more sophisticated, executives seek explainability in AI models to understand their logic and outcomes. Data science professionals must now bridge the gap between complex AI concepts and non-technical stakeholders by articulating these concepts in simple terms. This fosters confidence and trust in AI technology, leading to wider adoption. By explaining AI in layman's terms, executives can make informed decisions and maximize the benefits of AI systems while minimizing potential risks. Emphasizing explainability is crucial for the successful integration of AI in various industries. It is imperative for Data Scientists and AI Scientists to continually hone their communication skills to stay in the loop with stakeholders.

Explaining AI is crucial for its acceptance and effective utilization, especially in critical sectors like healthcare. At eKare, we prioritize making our AI solutions understandable to healthcare professionals. This not only fosters trust in the technology but also enables clinicians to make informed decisions, enhancing patient care.

Explaining AI is crucial to foster transparency, trust, and accountability. As AI systems increasingly influence our lives - from personalized recommendations to critical decision-making in healthcare or finance - understanding how these systems operate becomes imperative. By explaining AI, we can ensure that decisions are fair, unbiased, and comprehensible to users. This not only fosters user trust but also allows users to appropriately interact with AI, optimizing its use. Furthermore, it is a step towards ethical AI, ensuring accountability for its actions.

User feedback is the compass that guides product evolution. Consider the refinement of Netflix's recommendation algorithm: it continually learns from implicit feedback, like viewing habits, and explicit feedback, like ratings. Similarly, Evernote’s in-app surveys represent an efficient way of collecting explicit, qualitative feedback, guiding the future development of the platform. Whether you're gathering qualitative sentiments or quantitative data, user feedback serves as an invaluable tool for creating user-centric products and services.

User feedback is invaluable in the iterative process of improving AI solutions. At eKare, we actively seek the perspectives of healthcare professionals who use our tools. Their real-world experiences and suggestions guide our enhancements, ensuring our AI solutions remain practical, effective, and user-friendly.

User feedback in an AI context pertains to user responses to the system's output, its interpretability, user interface, and overall user experience. Such feedback provides insights into the AI's efficiency, whether its outputs are understandable, how users perceive the interaction, and any concerns they may have. For instance, in a music recommendation AI, user feedback may concern the appropriateness of the recommended tracks. For an AI system used in traffic management, feedback might highlight the clarity and usefulness of its traffic predictions. The aim is to solicit, study, and integrate this feedback to refine AI systems and better elucidate their functionalities for users.

A classic feedback loop could be to determine if the user chose a different option than the one that the AI system had recommended. Capture the actual result that the human has chosen and add it to your next model training.

In order to get feedback that’s valuable, it's crucial to make the process user-friendly. One effective strategy is using "in-situ" feedback mechanisms, such as simple thumbs-up or thumbs-down ratings, or emotion-based reactions that users can quickly tap while interacting with the AI. This allows for collecting feedback in real-time without disrupting the user experience.

Collecting user feedback on AI is essential for continuous improvement. At eKare, we employ various methods, such as user surveys, interviews, and usability testing. These provide valuable insights into user experiences and enable us to refine our AI tools to better serve the needs of healthcare professionals and patients alike.

Despite the challenges in collecting user feedback for AI, it's crucial to recognize that feedback doesn't have to be technical in nature. In fact, non-technical feedback can often be the most impactful. Feedback can be as simple as asking users if the AI solution is working effectively, whether they are seeing ROI, or if they have encountered any bias or ethical issues during its use. These types of questions can provide valuable insights into the real-world impact of AI systems and help identify areas for improvement. To collect user feedback effectively, organizations should create an open and accessible feedback channel, encourage users to share their experiences, and analyze the feedback to glean actionable insights.

I think user feedback is an excellent tool in AI development. User feedback should be from a very diverse ROUND TABLE that captures a well balanced cross section of feedback. Great care needs to be put into capturing user feedback. Capturing the "feedback loop" as someone commented may not necessarily be the correct answer, it may just be the popular answer. I strongly believe it is very important to know the difference between the popular answer and the ground truth which is exclusively based in facts as opposed to popular beliefs. Great care and a lot of work is necessary to determine the ground truth!

Consider the analogy of a master chef who tweaks her recipe based on customer feedback. She analyzes the feedback and makes changes to improve the taste. Similarly, user feedback provides invaluable insights into how well an AI system is understood, how much it is trusted, and how it could be improved. We can use analytics techniques like Reinforcement Learning to adjust our AI models based on feedback. We demonstrate transparency and enhance user trust by explaining the changes implemented and highlighting the improvements.

User feedback is pivotal in shaping the explainability of AI tools. At eKare, we use insights from users to create interfaces that make AI processes clear and understandable. This transparency facilitates healthcare professionals' understanding of our tools, leading to more confident and informed decision-making.

Integrating user feedback can also foster a two-way relationship, where AI learns from user interactions, while users benefit from more tailored services. It can help identify AI system blind spots, ensuring that the system not only functions optimally but is also socially responsible. For example, if an AI for a music streaming service keeps suggesting heavy metal to a classical music lover, user feedback can correct this misalignment, resulting in a more personalized and satisfying user experience.

Incorporating user feedback to clarify AI functionalities greatly aids in fostering confidence and transparency. At eKare, this practice is instrumental in breaking down complexities of our AI solutions. It empowers healthcare professionals with a better understanding of the tools, enhancing their ability to leverage AI for improved patient outcomes.

"In the realm of AI, and intelligent automation, the ACT model is the guiding light to success. Alignment, Clarity, and Transparency form the foundation for progress. As leaders, the first step we take is to clearly define any challenge that lies ahead. Understanding the intricacies, significance, stakeholders, and desired results empowers us to employ powerful problem-solving tools like the 5 Whys, Fishbone Diagram, or Problem Statement. By doing so, we demonstrate a clear and logical path to effectively address the issue at hand, paving the way for streamlined automation and transformative outcomes."

Three "dos" and "donts" for using user feedback to explain AI: Dos: 1. Do actively seek user feedback. 2. Do use feedback to improve explanation clarity. 3. Do use feedback to ensure that explanations are relevant. 4. Do iteratively refine AI explanations based on feedback. 5. Do validate changes with further user feedback. Don'ts: 1. Don't assume you know what users want to understand. 2. Don't ignore feedback suggesting explanations are too complex or too vague. 3. Don't consider the job done after one round of feedback. 4. Don't disregard the diversity in your user base. 5. Don't make changes based on a single piece of feedback.

In our pursuit to explain AI and make it more understandable, we must remember that not all feedback is constructive. We need to filter them through the lens of objective, data-backed analysis. Sharing irrelevant or counter-productive feedback could worsen the performance and outcomes of an AI system. When you collect user feedback, you'll get some highly useful ones, some outliers, and a few that could be shared with an intent to game the system. Tweaking the system based on every feedback received could worsen user experience and alienate some users.