Short answer
When designing algorithmic systems, focus on providing explanations that clearly articulate the system's process and potential biases, while also considering how to directly support users in evaluating the accuracy and appropriateness of the system's outputs.
- Field
- User-Centred Design
- Source
- Academic Publication (2018)
- Method
- Online Experiment
- Evidence
- Moderate effect
Providing explanations for algorithmic systems increases user understanding of system operation and their sense of control, though it does not necessarily improve their ability to judge the accuracy or sensibility of the system's outputs. This user-centred design research insight is drawn from a 2018 study published in Academic Publication. Using Online experiment, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing algorithmic systems, focus on providing explanations that clearly articulate the system's process and potential biases, while also considering how to directly support users in evaluating the accuracy and appropriateness of the system's outputs.
Algorithmic Explanations Enhance User Awareness and Perceived Control, but Not Output Correctness
Providing explanations for algorithmic systems increases user understanding of system operation and their sense of control, though it does not necessarily improve their ability to judge the accuracy or sensibility of the system's outputs.
Academic Publication · 2018
Key Findings
- 01All tested explanations increased participants' awareness of how the News Feed algorithm functions.
- 02Explanations improved participants' ability to assess potential system bias and their perceived control over the content they see.
- 03Explanations were less effective in helping participants evaluate the correctness or sensibility of the algorithm's output.
Application
Design takeaway
When designing algorithmic systems, focus on providing explanations that clearly articulate the system's process and potential biases, while also considering how to directly support users in evaluating the accuracy and appropriateness of the system's outputs.
How to apply
When developing user interfaces for AI-powered tools, integrate concise, informative explanations about how the AI reaches its conclusions. Supplement these explanations with features that allow users to flag incorrect or nonsensical outputs and provide feedback on the system's performance.
Project actions
- 01When designing a system that uses algorithms, think about how you will explain its workings to the user.
- 02Consider if your explanations will help users judge the quality of the system's output, or if you need other features for that.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Empirically investigates the impact of explanations on user perceptions.
- +Provides actionable insights for designing transparency mechanisms.
Limitations
The explanations provided might not have been sufficiently detailed or tailored to the specific user's needs. The study did not explore long-term effects of explanations on user trust and behavior.
Reliability & validity
The study's validity is supported by its experimental design, which allows for causal inferences about the effect of explanations. Reliability would depend on the precise measures used to assess user beliefs and judgments, and the consistency of results across participants.
Think critically
To what extent should designers prioritize user awareness and control over the accuracy of algorithmic outputs when designing for transparency?
Design Principles
"Algorithmic transparency mechanisms should aim to enhance user awareness and perceived control, but must be complemented by strategies that directly address the evaluation of output quality."
In an era of increasingly complex algorithmic decision-making, understanding how users perceive and interact with these systems is crucial. This research highlights that while explanations are valuable for user empowerment, designers must consider additional strategies to address user concerns about output quality and consistency.
What This Means for Your Design
When you explain how a computer system (like a social media feed) works, people understand it better and feel like they have more control. However, they don't necessarily get better at telling if the system's results are correct or make sense.
How to use in your project
- 1.Reference this study when discussing the importance of user understanding and control in your design project, especially if your project involves algorithmic decision-making.
- 2.Use the findings to justify the inclusion of specific explanation features or user feedback mechanisms in your design proposal.
Add to My Project
Quick Cite
Paragraph starter
This research by Rader, Cotter, and Cho (2018) demonstrates that while algorithmic explanations significantly enhance user awareness of system mechanics and perceived control, their effectiveness in improving judgments about output correctness is limited. This suggests that for design projects involving algorithmic decision-making, a multi-faceted approach to transparency is necessary, combining clear explanations with mechanisms that directly support users in evaluating the quality and appropriateness of the system's outputs.
Source
Academic Publication
Explanations as Mechanisms for Supporting Algorithmic Transparency
journal · 2018
View sourceQuestions About This Research
- What does the research say about algorithmic explanations enhance user awareness and perceived control, but not output correctness?
- When designing algorithmic systems, focus on providing explanations that clearly articulate the system's process and potential biases, while also considering how to directly support users in evaluating the accuracy and appropriateness of the system's outputs. Evidence: Academic Publication (2018).
- Why does "Algorithmic Explanations Enhance User Awareness and Perceived Control, but Not Output Correctness" matter for design?
- In an era of increasingly complex algorithmic decision-making, understanding how users perceive and interact with these systems is crucial. This research highlights that while explanations are valuable for user empowerment, designers must consider additional strategies to address user concerns about output quality and consistency.
- How can designers apply this research?
- When designing algorithmic systems, focus on providing explanations that clearly articulate the system's process and potential biases, while also considering how to directly support users in evaluating the accuracy and appropriateness of the system's outputs.
- What were the main findings?
- All tested explanations increased participants' awareness of how the News Feed algorithm functions.. Explanations improved participants' ability to assess potential system bias and their perceived control over the content they see.. Explanations were less effective in helping participants evaluate the correctness or sensibility of the algorithm's output.
- What research method was used?
- Online Experiment.
- How strong is the evidence?
- Evidence strength is rated Moderate effect, based on a 2018 journal from Academic Publication.
- What should I do differently in my next project?
- When developing user interfaces for AI-powered tools, integrate concise, informative explanations about how the AI reaches its conclusions. Supplement these explanations with features that allow users to flag incorrect or nonsensical outputs and provide feedback on the system's performance.
- What are the limitations?
- The study focused on a specific social media algorithm, and the effectiveness of explanations may vary across different types of algorithmic systems and user demographics. The specific content of the explanations themselves was not deeply analyzed for differential impact.