AI Trust Gap and User Experience

From: aidotengineer

The AI Trust Gap

Currently, Artificial Intelligence (AI) faces a significant trust gap among users [00:00:35]. Recent research by Edelman in December 2024 revealed that only 32% of US adults trust AI, and merely 44% of adults globally feel comfortable with how businesses are using AI [00:00:40], [00:00:44], [00:00:48].

Causes of the Trust Gap

The primary reason for this distrust is “AI slop” [00:00:56]. This refers to instances where users encounter Generative AI (GenAI) failures in websites, products, or interfaces, providing incorrect or nonsensical information [00:00:59], [00:01:02]. Examples include search results suggesting one “eat rocks every day” or websites listing a car for $1 [00:01:13], [00:01:15], [00:01:18], [00:01:20]. Many website creators are seen as “stuffing AI chatbots” into everything and misleadingly presenting it as “magic” [00:01:25], [00:01:27], [00:01:29].

Reimagining User Experience with AI

Despite the current challenges, GenAI offers a significant opportunity for a UX revolution [00:01:31], [00:01:35]. Its real “magic” lies in enabling users to interact with machine learning models using natural language, a capability previously unavailable [00:01:38], [00:01:43], [00:01:46].

To leverage this potential, it’s crucial to return to UX first principles [00:01:56]. Don Norman’s “Design of Everyday Things” emphasizes simplicity and efficiency [00:02:05], [00:02:07], [00:02:10], [00:02:12]. His concept of “invisible interfaces” from “The Invisible Computer” describes software so seamless and intuitive that users forget they are even using it [00:02:18], [00:02:20], [00:02:23], [00:02:25], [00:02:28].

The opportunity for AI is to design interfaces that genuinely feel “magical,” not by poorly integrating chatbots, but by accelerating the “need finding” phase of design [00:02:31], [00:02:34], [00:02:36], [00:02:39], [00:02:41].

Accelerating Design with AI Simulation

A new approach to the design lifecycle proposes using AI simulation to empower designers [00:03:03], [00:03:05]. This is analogous to how pilots use simulations for extensive practice in complex environments [00:03:13], [00:03:15], [00:03:19], [00:03:22], [00:03:23], [00:03:26].

The AI-Accelerated Need Finding Process

The proposed process for need finding in the era of AI simulation shares similarities with existing methods but introduces key differences [00:04:32], [00:04:35], [00:04:57].

1. Define Audience: Start with comprehensive data representing target audiences, including demographic, psychographic, and contextual information [00:05:06], [00:05:08], [00:05:11], [00:05:14], [00:05:16], [00:05:18], [00:05:21], [00:05:24], [00:05:27].
2. Intent Mapping with Intelligent Twins: Convert audience data into “intelligent twins”—active simulations representing user behaviors, needs, and motivations [00:05:34], [00:05:36], [00:05:39], [00:05:41], [00:05:43], [00:05:46], [00:05:49]. These twins become active participants in the design simulation [00:05:52], [00:05:54].
3. Task Evaluation: Brief intelligent twins to evaluate interfaces based on specific tasks, similar to how human designers conduct heuristic analysis [00:05:56], [00:05:59], [00:06:02], [00:06:05], [00:06:07]. This allows for evaluation of specific interfaces or websites [00:06:10], [00:06:13], [00:06:17].

Benefits of AI Simulation

• Scale and Speed: Intelligent twins can operate with greater velocity than human teams, allowing for large-scale audits, such as a global audit of sports websites [00:06:23], [00:06:25], [00:06:28], [00:06:31], [00:06:33], [00:06:36], [00:06:39]. This can help identify cultural nuances and engagement strategies across different regions [00:06:41], [00:06:43], [00:06:45], [00:06:48], [00:06:50], [00:06:53], [00:06:55].
• Granular Insights: Simulated actions can quickly roll up insights at a high level or allow for granular dives into specific areas of friction within the user experience [00:07:49], [00:07:50], [00:07:52], [00:07:55], [00:07:57], [00:09:06], [00:09:09], [00:09:12], [00:09:15].
• Addressing Pain Points: By understanding user pain points more effectively, this methodology can help solve design issues, ultimately repairing the AI trust gap [00:08:53], [00:08:55], [00:08:58], [00:09:00], [00:09:04].

Future Considerations and Improvements

The methodology is experimental and in its early stages [00:10:53], [00:10:55]. Key areas for improvement include:

• Reproducibility: Standardizing parameters like briefing instructions, simulated audience dimensions, audit runs, and task completion/failure criteria in a code repository [00:11:00], [00:11:04], [00:11:06], [00:11:09], [00:11:12], [00:11:14], [00:11:16], [00:11:19].
• Test and Control: Applying a test and control methodology to isolate the strengths of intelligent twins in design need finding [00:11:24], [00:11:26], [00:11:28], [00:11:32], [00:11:34]. This will help determine how it complements human teams across different industries, geographies, and domains [00:11:35], [00:11:38], [00:11:41], [00:11:43].

Ultimately, users don’t need more ineffective GenAI chatbots [00:11:57], [00:12:00], [00:12:03]. Instead, they benefit from better websites, mobile apps, and interfaces that offer clarity and simplicity [00:12:06], [00:12:08], [00:12:11], [00:12:14]. AI simulation can serve as a powerful tool to empower design teams, enabling them to gather insights smarter, faster, and better, thus creating experiences that restore trust in AI [00:12:17], [00:12:19], [00:12:21], [00:12:24], [00:12:27], [00:12:29], [00:12:32].