Transparency & Trust

Algorithm Effectiveness Rating

mattiealston — Tue, 15 Dec 2020 00:00:00 +0000

Algorithm Effectiveness Rating

Application

Communication

mattiealston Tue, 12/15/2020 - 00:00

Topic

Transparency & Trust

Problem:

In order to understand the likelihood of success, the user wants to know how effective the algorithm is before initiating an operation.

The "PictureThis" app boasts about its 95% accuracy rate, which is also implicitly being honest about its 5% failure rate.

Solution:

The system communicates an effectiveness metric. For example, in pattern recognition operations this could be indicated in terms of what percentage of image capture inputs result in a successful match with the correct data points.

Discussion:

Obviously if a system communicates that it is less than 100% successful 100% of the time, that can undermine the confidence a user has in that system. But in the the long term, acknowledging the fallibility of a system is a vital part of building trust in it (as per Setting Expectations and Acknowledging Limits).

Quantifying the effectiveness with a score provides a couple of advantages over a vaguer, copy-driven approach. Firstly, if the metric is comparable to ones used by similar competitor systems, the user can easily establish how this system compares to others. Secondly, as the score is subject to change, this allows the system to track and communicate how it has improved over time — acknowledging initial shortcomings while establishing a trajectory towards increased success encourages the user to return later and engage further, as opposed to simply overpromising and underdelivering on first use, which can discourage further exploration.

Algorithm Processing Status

leighbryant — Sun, 15 Nov 2020 00:00:00 +0000

Algorithm Processing Status

Application

System Feedback

leighbryant Sun, 11/15/2020 - 00:00

Topic

Transparency & Trust

Problem:

The algorithm may take a while to deliver a result. While this is happening, the user wants to know what is taking place.

The AI-powered application provides both a progress bar and a more granular explanation for the time it is taking in note-form below the bar.

Solution:

The system provides granular progress messages during calculations.

Discussion:

This is important both for the transparency of the system—to reassure the user that the system is operating and hasn't stalled—and to create the impression of an intelligent system with many moving parts. In some cases where there is no actual processing delay, it may even be beneficial to simulate one (as with Demonstrating Thinking). By contrast, sometimes it may undermine the overall impression of the system to expose its workings and over-explain (in which case Appropriate Magic might be deployed).

Appropriate Magic

leighbryant — Tue, 15 Sep 2020 00:00:00 +0000

Appropriate Magic

Application

Behaviour

leighbryant Tue, 09/15/2020 - 00:00

Topic

Transparency & Trust

Problem: 

A user wants to be awed, wowed, and amazed by a piece of software. Sidetracking to explain its workings can disrupt the flow of the experience and spoil the fun.

The "WoeBot" encourages the user to think of it as human-like, to encourage a more natural, personal rapport, without diving into detail about how the AI that supports that persona works.

Solution: 

When appropriate, the system should obfuscate the underlying algorithm and instead use playful language to suggest that the system is more than just a math machine— it's magical!

Discussion: 

This (obviously) is contrary to all other patterns around user education and transparency and so should be handled with care. That said, whatever we're designing we should be making deliberate choices about the presentation layer of an AI app. Even if the UI does little to try and steer the user toward a certain conceptual framework, the user builds a mental model regardless, which may be very different to the designer's intent. Whether it's transparent, magical, or otherwise, designers should see the opportunity to take the initiative in facilitating this mental model and not leave it to chance.  

Before & After Comparison

mattiealston — Fri, 15 May 2020 00:00:00 +0000

Before & After Comparison

Application

System Feedback

mattiealston Fri, 05/15/2020 - 00:00

Topic

Transparency & Trust

Problem:

The user has submitted an artifact for processing, be it an image, sound clip, or video file, and wants to understand fully what effect the processing has had.

This demo from Nvidia is so convincing that you might not even believe the "After" image had been edited if you couldn't compare it to the "Before"

Solution:

The system presents the user with their initial artifact beside the processed version for comparison. On a desktop app, this may mean a simple split display. On a mobile app where screen real-estate is more of a concern, this could take the form of a slider that allows the user to swipe between before and after images, or a similar interactive device.

Discussion:

Although a very straightforward pattern, it is one that can be overlooked by designers. Losing sight (or sound) of the original content can present serious problems for the user in evaluating the effectiveness of the processing, or, if the effect is subtle, to even verify that processing has correctly taken place. The immediacy of comparison is key to reducing the cognitive load required of the user. In many cases even when the original content is presented with the processed version, the user is required to click in and out of each version via preview thumbnails, which makes direct comparison problematic. Likewise for sound clips or video, being able to quickly swap between before and after clips as they play (without restarting) aids the immediacy of comparison.

Confidence Status

leighbryant — Sat, 15 Feb 2020 00:00:00 +0000

Confidence Status

Application

System Feedback

leighbryant Sat, 02/15/2020 - 00:00

Topic

Transparency & Trust

Problem:

Users want to know how accurate results are when the system presents the results of a calculation.

The wording “Seats may be available” was chosen carefully and deliberately to suggest there is a degree of uncertainty in the train busy-ness prediction.

Solution:

The system provides a visual indication of the confidence status of the calculation (as a percentage, by graphical indicator, or through another format).

Discussion:

When the operation of an algorithm is opaque to the user, it is hard for that user to assess how much trust to place in the calculation. Transparency in the algorithm is vital for the user to build a relationship of trust in the system. This transparency includes not just details of how the algorithm arrives at its results, but also honesty about its shortcomings. Displaying a confidence status allows the user to differentiate between reliable and unreliable outcomes, and to make more informed choices. It also works to mitigate the negative impression around inaccurate results and to amplify the feeling of success around accurate ones.

Other Examples:

A lookalike app displays a confidence status with each potential match to show how similar the system thinks its AI prediction is.

Another image recognition AI app displays a score for certainty of assessment.

Data Being Shared Flag

mattiealston — Tue, 15 Oct 2019 00:00:00 +0000

Data Being Shared Flag

Application

Input Data

mattiealston Tue, 10/15/2019 - 00:00

Topic

Transparency & Trust

Problem:

When the system communicates data to third parties, the user wants to know what data is being shared and what is not.

As part of the set-up process, Youper flags what data is being shared.

Solution:

The system flags inputs to show this or otherwise tells the user what data is being shared and to whom.

Discussion:

Transparency shouldn't end with a vague story about data generically being stored and shared, rather it should be explicit about what particular data is being used for any particular purpose. Similarly, when data is not being shared, the user will also be reassured to know this.

Dataset Explorer

mattiealston — Thu, 15 Aug 2019 00:00:00 +0000

Dataset Explorer

Application

Display

mattiealston Thu, 08/15/2019 - 00:00

Topic

Transparency & Trust

Problem:

The user wants to understand how the underlying data used in a system is structured, how extensive it is, and comprehend how it is used by the system.

The "Infinite Drum Machine" provides a playful way to explore the data (in this case different percussive sound samples clustered by audio similarity) .

Solution:

Alongside the core functionality provided by the system, there is an extra feature that allows the user to dive into the data itself and explore it from different angles, using filters and other controls to manipulate it. This may utilize an attractive Dataset Visualization pattern as part of this interface, that can provide a playful interaction in its own right. Alternatively, it might be a simple list or table, which holds less appeal for casual explorers (and indeed may be very daunting for them to deal with) but empowers serious users to deepen their understanding of, and expertise with, the system.

Discussion:

For some applications, exploring a dataset can be the very point of the system, and plentiful examples of these can be found. For others, where there is a dataset “under the hood” powering operations, it might not increase the utility of the application to allow users to explore the data, but it can be very effective at building trust in the system by exposing its inner workings. In addition, it can provide a compellingly pleasurable “bonus feature” that can drive additional engagement.

More Examples:

In addition to the core font finding feature, Fontjoy provides a dataset explorer that enables a deep dive into everything the system knows about fonts.

Dataset Visualization

mattiealston — Mon, 15 Jul 2019 00:00:00 +0000

Dataset Visualization

Application

Display

mattiealston Mon, 07/15/2019 - 00:00

Topic

Transparency & Trust

Problem:

The user wants to quickly understand the general shape of the dataset used by the system, the extent of it, and what patterns might exist within it.

The "Living Archive" visualization from Google is an insightful and attractive way to explore the catalogue of dance moves from choreographer Wayne McGregor.

Solution:

The system provides an attractive visualization that takes all the underlying data or a subset of it, and renders it into an easily comprehendible visual. This may be a static image or, if interactive, it may form part of a Dataset Explorer.

Discussion:

In general, users struggle to understand what it means when data powers a system, especially when the reality is that that data is just a list of abstract variables. Using a visualization to illustrate the nature of that data is an incredibly effective tool for helping users build a mental model of the system. In contrast to the Dataset Explorer pattern, where users want to understand and validate the data on a granular level, here even if that visualization is a big picture simplification or only portrays a small subset of data, it can still be a very valuable asset to share.

Demonstrating Thinking

leighbryant — Sat, 15 Jun 2019 00:00:00 +0000

Demonstrating Thinking

Application

Display

leighbryant Sat, 06/15/2019 - 00:00

Topic

Transparency & Trust

Problem: 

The user wants to know that the system has conducted a complex calculation. If the calculation occurs too quickly, they might not believe that the calculation has been completed correctly or has been extensive enough in considering all the variables.

The Articoolo app uses a progress bar and explanatory text to demonstrate the AI is "at work", so the user is given the sense that "thinking" is happening.

Solution: 

The system uses artificial wait times and progress messages to demonstrate that it is "thinking" and applying satisfactory effort to the calculation.

Discussion: 

While simplicity in an interface is often desirable, in some cases the appearance of complexity may be beneficial, even to the point of simulating some of that complexity. While the reality of a system's intelligence may result in effortless calculations and speedy response times, the perception of a system's intelligence is ironically often associated with the appearance of effort and slower responses.  

Dreamlike Novelty

mattiealston — Wed, 15 May 2019 00:00:00 +0000

Dreamlike Novelty

Application

Display

mattiealston Wed, 05/15/2019 - 00:00

Topic

Transparency & Trust

Problem:

Sometimes the user wants to be surprised, to be hit with a joyous wave of weirdness that disrupts their everyday existence and makes them question whether they are awake or are dreaming the strangest of dreams. They also want to laugh at bizarre notions, such is the nature of absurdist humour.

Artbreeder combines images of everyday objects to produce dreamlike creations.

Solution:

Machine learning apps can often output strange results, whether accidentally or by design — the interface should embrace this freakiness and share this with the user. This can be in the form of generative text, imagery, or video.

Discussion:

Many of us long to be tourists in the world of the weird. Now we can be confronted with and delighted by such alien creativity as emerges from machine minds. We have constructed a gateway into the domain of dreams!

The reality is that any machine learning tool is going to take odd paths regardless, so, where appropriate, make it a feature not a flaw to expose the oddities. Beware, though, that what one person considers to be charming Dreamlike Novelty, another may consider horrible Nightmare Fuel.