Paper:  Goyal, N., & Fussell, S. R. (2016). Effects of Sensemaking Translucence on Distributed Collaborative Analysis. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 288–302). New York, NY, USA: ACM. https://doi.org/10.1145/2818048.2820071 (Links to an external site.)

Discussion Leader:  Annie Y. Patrick

Summary:

Goyal and Fussell focus on the concept of sensemaking translucence.  Sensemaking is a process utilized by crime investigators in which numerous pieces of information are collected to form multiple hypotheses that are then further examined to either confirm or disconfirm the initial hypotheses.  However, a challenge in this process is biased perception of the investigators, confirmation biases, and groupthink.  Sensemaking translucence is the process to bring awareness of the sensemaking process to analysts.

To address the challenger of cognitive biases in sensemaking, the authors created a sensemaking translucence interface of two parts:  a hypothesis window and a suspect visualization.  The hypothesis window is to facilitate the exchange of ideas of suspects’ means, motives, and alibis.  The suspect visualization provides automatic feedback of suspects via the hypothesis window, the group chat window, and a digital sticky note feature. The authors predicted that ta sensemaking translucence interface woult perform better on a collaborative analysis task than those using a standard interface (H1), would rate the tool as more useful than the standard interface (H2a), would report a higher level of activity (H2b) and would rate a higher collaborative experience (H3).

To conduct this study, 20 pairs of remote participants role-played as detectives to solve a crime.  The pairs were randomly assigned to either the standard interface or the sensemaking translucence interface. Each participant was given a set of documents about 3 cold murder cases with information about 7 murders, with 40 potential suspects, hidden in approximately 20 documents divided between the pairs. The pairs were to share their information to find the name of the serial killer in 50 minutes.  The study was analyzed by using the participants’ final reports and post-task report.

Upon analysis the use of the sensemaking translucence interface revealed that more clues were identified and the serial killer was identified in a less time than the standard interface users.  However, the interface was rated less helpful in providing support, hypothesis generation, and viewing multiple suspects.

Reflection:

This is a research article detailing a study of a sensemaking translucence interface to examine the challenges in collaborative sensemaking.  The authors validate their study by discussing the challenges of biased perception that investigators hold that at the least could delay justice or at the worst place the wrong in prison.

Though this study provided an initial platform to compare and contrast how a more collaborative sensemaking translucence interface can aid in the criminal case sensemaking, there were areas that could have strengthen the study.  This study used 40 participants ranging from 18-28 years old that were either undergraduate or graduate students.  This reflects a very limited sample that does not represent the general or the professional users of this type of data. Also, the participants were placed in pairs, I would guess that investigative situations would have information from multiple sources, thus complicating the situation.  The researchers do address these need for field research, however, changes in this study could have sufficed too.

1. Would incorporating a more diverse sample have affected the study differently? Why or why not?
2. How does the concept of teammate inaccuracy blindness (analyst treat all information from a partner as valid and useful, regardless of its actual quality) apply within the context of crowdsource data and information parsed through online and social media outlets?
3. Does this sample reflect the population that would be most likely using this type of interface? How could this sample/study have been done differently?
4. What are other areas (other than criminal investigations) that could use the sensemaking translucence interface technology?
5. The pairs that used the interface identified more clues and solved the case in a greater proportion of the time than those using the standard interface. However, the users of the sensemaking translucence interface rated it as less helpful in providing support, hypothesis generation, and monitor multiple suspects.  Why do you think this was the case (in other words, why were there not positive responses for all the hypotheses of the project)?

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Mapillary is a startup founded in Sweden in 2013 by Jan Erik Solem, the founder of the facial recognition company, Polar Rose that was eventually acquired by Apple (Lunden, 2016).  The vision behind Mapillary was to create a more open and fluid version of Google’s Street view.  To remedy the limitations of a solo team with a camera rigged to a vehicle, Mapillary created an open platform utilizing the concept of crowdsourcing to build a better, more accurate, and personal map.  In addition to creating a better map, the company analyzes the photo to collect geospatial data.   Mapilliary uses a technology called Structure from Motion to create and reconstruct places in 3D.  By using semantic segmentation on the images, the company seeks to understand what is in the image such as buildings, pedestrians, cars, etc and build that into AI systems.  As of May 2017, their database held over 130 million images all through crowdsourcing that is also being used to train automotive AI systems (Lunden, 2017).

 

Mapillary is determined not to use advertising, but instead will focus on B2B platform to provide information for governments, business, and researchers.  Though researchers may use the data at no charge, commercial entities can purchase Mapillary’s services from $200 or $1000 a month dependent on the amount of data used.  The site list several institutions that have successfully used Mapillary.  The World Bank Transportation and ICT group utilized Mapillary to capture images for a rural accessibility project to evaluate the environment and road conditions remotely.  Westchester County in New York state has used the service to capture their trails to create interactive hikes with their park systems.

To date has mapped over 3 million kilometers of over 170 million images on all seven continents.

 

 

To explore Mapillary:

 

1. Go to mapillary.com
2. Create an account by clicking on the “Create Account” button on the lower left side of the page.
3. Choose to create a Mapillary login or use either Google, Facebook, or OpenStreetMap login.
4. Once signed in you may explore maps or create maps.
   1. To explore maps:
      1. Zoom in on an area on the map-a green line indicates that area has been mapped.
      2. Go to the magnifying glass on the upper left side of the screen and enter a location.

• When you have located your area, place your curser on the line and a photo will pop up and the image will locate on the lower left screen. Click the forward or back arrows to move through the images or the play arrow.

 

To contribute to Mapillary:

You may upload an image to the webpage:

1. Click on the menu arrow by your login name on the upper right screen
2. Click on Uploads
3. Click on “Upload Images”
4. Upload your image according to the options and instructions
5. Click “Review”
6. You may click on the dot to see the image.
7. Zoom into the location and place the dot on the map

Or use your smartphone:

1. Download the Mapillary app on your smartphone from either Google Play or the App Store.
2. Sign-in or create an account.
3. Tap the camera icon
4. Position the camera so that it is level with the horizon and nothing is obstructing view
5. Choose your capture option: The automatic capture option will automatically capture images as you move every 5 meters OR use the manual option to capture panorams, objects, and intersections
6. Tap the Red record button and move either by walking, driving, biking, or whichever means of movement and transportation you prefer.
7. When done, tap on the exit arrow
8. Tap the upload icon (the cloud icon)
9. Upload your images-your images will be uploaded and deleted from your device
10. Images are then processed by Mapillary
11. You will receive a notification for when images have been uploaded, edits accepted, comments, or mentions
12. You’re done!!

 

https://techcrunch.com/2016/03/03/mapillary-raises-8m-to-take-on-googles-street-view-with-crowsourced-photos/

https://techcrunch.com/2017/05/03/mapillary-open-sources-25k-street-level-images-to-train-automotive-ai-systems/

 

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