Study
Innovation & DesignRecentStrong effect

Bridging the Sociotechnical Gap in Explainable AI Enhances User Trust and Adoption

A systematic framework for understanding and addressing the divide between AI's technical capabilities and user needs is crucial for successful XAI system design and market integration.

Proceedings of the ACM on Human-Computer Interaction · 2023

01

Key Findings

  • 01The sociotechnical gap in XAI is a significant challenge that impacts user understanding and trust.
  • 02A systematic framework can effectively chart this gap by connecting AI guidelines to XAI contexts.
  • 03Operationalizing this framework leads to actionable insights for improving XAI design and user adoption.
02

Application

Design takeaway

Integrate a sociotechnical analysis into the AI design process to ensure technical explainability aligns with user needs and expectations, thereby fostering trust and driving adoption.

How to apply

When designing or evaluating an AI system, use a structured approach to identify potential disconnects between the system's explainability features and the target users' understanding, needs, and goals. Adapt existing AI guidelines to the specific XAI context.

Project actions

  • 01When researching AI, consider the 'human side' of the technology.
  • 02Think about how users will interact with and understand AI explanations, not just how they are generated.
03

Method & Evidence

AimHow can a sociotechnical framework be developed and applied to systematically chart and address the gap between technical affordances and social needs in Explainable AI (XAI) systems?
MethodEmpirical derivation of a framework through case studies, followed by validation in a new domain.
ProcedureThe researchers conducted two case studies in distinct domains to empirically derive a framework. This framework connects AI guidelines within the context of XAI and demonstrates how to use them to address the sociotechnical gap. The framework was then applied to a third case study in a new domain to showcase its utility.
ContextExplainable AI (XAI) systems across various domains.

Variables

IV["AI guidelines","XAI context"]
DV["Sociotechnical gap in XAI","User understanding and trust"]
CV["Domain of application","Specific AI system"]
04

Strengths & Limitations

Strengths

  • +Empirical grounding through case studies.
  • +Development of a transferable framework.

Limitations

The complexity of AI and diverse user needs can make it challenging to fully bridge the sociotechnical gap in a single design project.

Reliability & validity

The framework's validity is supported by its application across multiple domains. Reliability would depend on consistent application of the framework's steps and interpretation of findings.

Think critically

To what extent can a standardized framework truly capture the nuanced sociotechnical gaps across diverse AI applications and user groups?

05

Design Principles

"Design AI systems with a dual focus on technical performance and human-centered explainability, actively bridging the sociotechnical divide."

As AI systems become more prevalent, their effectiveness hinges on user acceptance and trust. Failing to align technical explainability with user expectations can lead to adoption barriers and market failure. Designers must consider the human element alongside technical specifications to create AI solutions that are not only functional but also comprehensible and trustworthy.

06

What This Means for Your Design

When creating AI that explains itself (XAI), it's easy to focus only on the tech. But people need to understand it in their own way. This research shows a method to check if the AI's explanation matches what people actually need to know, which helps them trust and use the AI better.

How to use in your project

  • 1.Use the framework's principles to analyze the sociotechnical context of your chosen AI system.
  • 2.Identify potential gaps in explainability and propose design solutions that bridge these gaps.
07

Add to My Project

08

Quick Cite

(2023). Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI. Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3579467 Retrieved from https://designdex.org/study/d7ae5998-2c0c-4855-8d54-c63a80c9131f/bridging-the-sociotechnical-gap-in-explainable-ai-enhances-user-trust-and-adoption

Paragraph starter

This research highlights the critical sociotechnical gap in Explainable AI (XAI), emphasizing the need to align technical affordances with user needs. By employing a systematic framework derived from case studies, designers can better understand and address this divide, leading to more effective and trustworthy AI systems. This approach is crucial for ensuring user adoption and market success in AI-driven innovations.

09

Source

Proceedings of the ACM on Human-Computer Interaction

Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI

journal · 2023

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Questions about this research

What does the research say about bridging the sociotechnical gap in explainable ai enhances user trust and adoption?
Integrate a sociotechnical analysis into the AI design process to ensure technical explainability aligns with user needs and expectations, thereby fostering trust and driving adoption. Evidence: Proceedings of the ACM on Human-Computer Interaction (2023).
Why does "Bridging the Sociotechnical Gap in Explainable AI Enhances User Trust and Adoption" matter for design?
As AI systems become more prevalent, their effectiveness hinges on user acceptance and trust. Failing to align technical explainability with user expectations can lead to adoption barriers and market failure. Designers must consider the human element alongside technical specifications to create AI solutions that are not only functional but also comprehensible and trustworthy.
How can designers apply this research?
Integrate a sociotechnical analysis into the AI design process to ensure technical explainability aligns with user needs and expectations, thereby fostering trust and driving adoption.
What were the main findings?
The sociotechnical gap in XAI is a significant challenge that impacts user understanding and trust.. A systematic framework can effectively chart this gap by connecting AI guidelines to XAI contexts.. Operationalizing this framework leads to actionable insights for improving XAI design and user adoption.
What research method was used?
Empirical derivation of a framework through case studies, followed by validation in a new domain..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2023 journal from Proceedings of the ACM on Human-Computer Interaction.
What should I do differently in my next project?
When designing or evaluating an AI system, use a structured approach to identify potential disconnects between the system's explainability features and the target users' understanding, needs, and goals. Adapt existing AI guidelines to the specific XAI context.
What are the limitations?
The framework's effectiveness may vary depending on the specific domain and the complexity of the AI system. Transferability to entirely novel contexts may require adaptation.
Is there evidence that sociotechnical gap affects design outcomes?
A structured approach, grounded in real-world examples, can help designers identify and close the gap between what AI can technically do and what users actually need and understand, thereby improving the adoption of AI technologies. As AI systems become more prevalent, their effectiveness hinges on user acceptance and Source: Proceedings of the ACM on Human-Computer Interaction (2023).
Where does this gap explainable research apply?
Explainable AI (XAI) systems across various domains. It sits within innovation & design research on designdex.org.

Related research topics

sociotechnical gap design research · evidence on sociotechnical gap · does sociotechnical gap improve design outcomes · gap explainable studies for designers · sociotechnical gap and gap explainable findings · innovation & design research evidence