Paper:

Bica, M., Palen, L., & Bopp, C. (2017). Visual Representations of Disaster. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1262–1276). New York, NY, USA: ACM.

Discussion Leader:

Tianyi Li

Summary:

This paper investigated the representation of the two 2015 Nepal earthquakes in April and May, via images shared on Twitter in three ways:

1. examine the correlation between geotagged image tweets in the affected Nepali region and the distribution of structural damage
2. investigate what images the Nepali population versus the rest of the world distributed
3. investigate the appropriation of imagery into the telling of the disaster event

The authors combined both statistical analysis and content analysis in their investigation.

The first question aims to understand if the photos distributed on social media correlates to the actual structural damage geographically, and if such distribution can measure the disaster accurately. They found that the distribution is significantly correlated statistically, however, a more in-depth analysis revealed that the geotags mean relatively little in relation to the finer aspects of geography and damage in Nepal: the images are less frequently photos of on-the-ground activity, and more frequently infographics and maps that originate from news and media.

The second question aims to understand how local and global audience perceive and relate to the disaster. They defined the boundary between “local” and “global” by the usage of Nepali, and pided the tweets into two dimensions: time (after first vs. after second) and geolocation (local vs. global), resulting in four categories (globally-sourced-after-first, globally-sourced-after-second, locally-sourced-after-first, and locally-sourced-after-second). The analysis results from hand-coding the Top 100 most retweeted image tweets in each of the four categories show a different diffusion of content, with locals focusing more on the response and the damage the earthquake caused in their cities, and the global population focused more on the images of people suffering. After the second earthquake, the results suggest some disaster fatigue for those not affected by the events and celebrity attention becomes the new mechanisms for maintaining the world’s gaze upon Nepal.

The third question studies the two competing expectations: journalistic accuracy and drawing a collective gaze of photography. They found four images in the full dataset of 400 image tweets that were confirmed to be appropriated from other times and/or places in globally-sourced-after-first, and an additional image in locally-sourced-after-first that has ambiguous origins. The fact that those images are appropriated is acknowledged either through replies or the originaltweets “as an honest mistake”, as “another way of visually representing disaster and garnering support through compelling imagery”.

Reflections:

This paper investigated imagery contents on tweets about the two earthquakes in Nepal in 2015, as a example to analyze disaster representation on social media. I appreciate the thorough analysis taken by the author. They looked at the tweets with image contents with three different research questions in mind, and studied different dimensions of such tweets as well as their correlations:  between geotags and contents, between local and global audiences, and between actual pictures of the disaster and appropriated images from other time and/or locations.

Their analysis is comprehensive and convincing, in that they combine complete and strong statistical analysis and in-depth content analysis. This well structured analysis revealed that despite the strong statistical correlation between geotags and images, the pictures posted are mostly infographics from news media which doesn’t really tell much about the “objective” depict of the on-the-ground situations of those affected places. It is a good example of the importance of content analysis and future researchers should not settle with superficial statistical phenomena and should try to look at the data closely.

Especially, I found their way of defining the boundary of local vs. global by language smart. In spite of the acknowledged limitation mentioned in the paper, languages can effectively distinguish the audience of the tweets. Taken into consideration that Nepali is the only official language, it’s an efficient and smart way of taking advantage of this.

Questions:

1. Do you agree with the authors that “…therefore, to watch is to witness. It is a responsibility to watch, even when watching is nevertheless entangled with problems of objectification and gratitude for not being a victim oneself.” is true in a global, geographically distributed online community? In other words, what do you think of the value of a “collective gaze” in the Internet era?
2. What do you think of image appropriation? Is it acceptable to draw wider range of attention from a broader, global community, or is it journalistically inaccurate and irresponsible thus not acceptable?
3. What do you think of the authors’ way of distinguishing “local” and “global” via language? Can you come up with a better boundary?
4. In their hand-coded categories of tweet contents, they acknowledged that “relief & recovery rescue” also contains “people suffering”. Why do they not merge these two categories? What do you think of their coding schema?

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Technology: Wiibrew.org

Demo Leader: Lawrence

Disclaimer: Doing actions such as this can void the warranty on your system and allow you to do things which are not legal under The Digital Millennium Copyright Act (DMCA). I do not endorse nor encourage these actions be taken and would advise against any of it if you are not sure what you are doing.

Summary:

Wiibrew is a special hack you can do to a Nintendo Wii Game System. First, you run one of several available exploits the the exploit crashes the Wii and runs unsigned code in the form of a .dol/.elf file on the root of your SD Card. All you need is an SD card which is bigger than 256MB and a game title from the list provided at the wiki page or you can run a sequence called letterbomb to do that hack if you do not have one of the listed games. The website makes sure to discourage against piracy and will not troubleshoot issues when trying to use a pirated game to do an exploit. The act of installing Wiibrew and using the intended apps is not inherently illegal, the issue comes when people use the unlocked systems to attempt to play ROMs. It is currently illegal to play non authentic copies of games even if you own the original copy and got it legally.

Reflections:

Wiibrew is a way to unlock the full potential of your Wii system and even allows you to play DVD movies (can not on a standard system). The problem is once this is done your Wii is now technically a computer system and has the same capabilities as any other home computer for the most part. You can even load a special version of Linux which is compatible with all Wii peripherals as well as a standard mouse and keyboard while also maintaining the dvd drive functionality.

Quick comparison to super portable computers.

	Nintendo Wii	Intel Compute Stick	Acer Chromebook
Price	$79.99	$258.99	$249.99
CPU	Power-PC	Core m3	Intel Celeron
# of Cores	1	2	4
CPU Speed	729MHz	1.6 GHz	1.6 GHz
GPU	ATI Hollywood	Integrated	Integrated
GPU Speed	243 MHz	N/A	N/A
RAM	2GB	4GB	4GB
Disk Drive	512MB	64GB	32GB
Disc Drive	Yes	No	No
Card Reader	Yes SDXC	Yes SDXC	Yes
LAN	No	No	No
WLAN	Yes	Yes	Yes
USB 2.0	2	1	1
USB 3.0	0	1	1

How to install using Letterbomb:

1. Go to please.hackmii.com
2. Select your region
3. Enter your MAC Address from the Wii into the boxes
4. Make sure the HackMii installer is bundled with your download (check box)
5. Cut the red wire to download the .zip file and unzip it
6. Copy “private” folder and “boot.elf” to your SD Card (recommended 4GB non SDHC)
7. Insert the SD card to your Wii and go to Wii Messageboard
8. You should see a red envelope

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Article: “Journalists as Crowdsourcerers: Responding to Crisis by Reporting with a Crowd:” https://link.springer.com/article/10.1007%2Fs10606-014-9208-z

Summary

This article is about professional journalists in a community deeply affected by Hurricane Irene, and how their role developed from that of typical journalists to leaders of a crowdsourced communication and self-advocacy movement among a population in crisis. This process was shaped by the rural surroundings (the Catskill Mountains of upstate New York) of the journalists and the community they served. In this case,  the extenuating problems of uneven IT infrastructures were deeply implicated in the way they developed ad-hoc, on-the-fly content, as well as the development of provisional ethical guidelines about journalism and reporting. A major innovation from this team is their conceptualization of “human powered mesh network,” which introduces humans as “nodes” in a peer-to-peer mesh IT infrastructure.

Social media became central to the emphasis on humans in the network. In their explanation of the role of social media in emergency situations, it becomes clear that the concept of “human infrastructure” — of which the human-powered mesh network is a subcategory — could not exist without social media. Platforms that connect individuals across the Internet and gain power vis-á-vis network effects create the conditions for this “human infrastructure.”

The authors give a detailed account of how Hurricane Irene affected the Catskills region before turning to an exploration of how local journalists used the Internet during this time. They describe the use of live-blogging — real time reporting and data-sharing by journalists who had started an online local news site called The Watershed Post. The platform that developed in the wake of Hurricane Irene out of a real-time news feed on the Watershed Post — powered by software called CoverItLive and simply called “the Liveblog” — became essential to the dissemination of emergency-response information. In their methodology section, which followed the situational description, they explain that they arrived at a blend of qualitative analyses of journalist interviews and the digital record of the Liveblog.

In both the Liveblog and through speaking with the journalists, the importance of social media and amplification of the message via popular platforms like Twitter is evident. The Watershed Post editors established a presence for their Liveblog on Twitter At one point, this message is posted out on the Liveblog:

 If we lose power during the storm, this live feed will continue to automatically pull in Twitter updates from other local newspapers, but we’ll be unable to post (for obvious reasons). Cross your fingers.

And since they did indeed lose power, this became critical. The redundant, always-on chain of communication supported by social media infrastructure allowed the Liveblog to balloon out. Guest comments on the blog also rose to prominence as a major source of information. It got to the point that moderating these comments became a task in and of itself, and moderators began to assume the role of public-facing authorities and gatekeepers in a situation where accuracy is of the essence.

At the end, the authors note a discrepancy between the presumptions of HCI researchers and the way information flowed organically in this case (and, by extrapolation, how it might flow in similar situations — they note the Virginia Tech tragedy in 2007 as an analogous example). Resisting strong fidelity to one side or another led them toward the hybrid concept of the human-powered mesh network: insights from both can inform our thinking on this in our roles journalists, citizens, technologists, and (more often than not these days ) some blend of the three.

Analysis

Prefatory note: this hit pretty close to home for me, because I was living in the Catskill Mountains when Hurricane Irene happened, and my roommate was working on building mesh networks. He was constantly frustrated by the geographical barriers to peer-to-peer networking (i.e., mountains!). Also, his work was disrupted when we had to evacuate our apartment…

This isn’t just a gratuitous anecdote. The human/”meatspace”  (including geographical/topographical/old-infrastructure concerns) factor in IT innovations often gets left out of the conversation when we look at their revolutionary potential. Yet in this case (as is often the case with non-IT inventions), where crisis was the mother of invention, the need to design for complex and worst-case scenarios gave traction to the development of a concept (human-powered mesh networks). This has a wide span of applications — and will possibly become more important as mobile technology proliferates, and facility with disaster management and response may become more important  (if we believe the science on climate change).

That’s why I think crowdsourcing is a rich topic: it is, basically, a technological concept that needs human actors en masse in order to work. With that will always come required considerations that emphasize multiplicity. Although the developers behind some major softwares don’t have to design their tech to be as accessible as possible, we can’t talk about (for example) mTurk without discussing ethics and accessibility, since it needs to work for lots of different kinds of people. Likewise, human-powered mesh networks place unique requirements on the way we think about human/physical-world factors. In emphasizing the crowd, the multiple, it necessarily becomes a more just conversation.

In a case where accuracy could mean life or death, trust is absolutely essential. The authors indicate this toward the end of their discussion. Although we have, in this class, looked toward automated means of fact-checking and verification, I’d like to propose something a bit different. The writers make this observation:

“Human infrastructuring in this case also included developing, adapting and communicating shared practices, and establishing a shared sense of ownership within the collaboration.”

With a shared sense of ownership comes (at least in theory) a shared responsibility to be accountable for the impact of the information you spread. The human-powered mesh network, and those who adopt roles of relative power within it — as comment moderators, contributors to maps, or those who Tweet/post a lot on blogs, etc — runs on the assumption of ethics and good faith among those who participate. Automating fact-checking and information accuracy is one thing, but in focusing on how we can give this role to computers, perhaps we forget that networks of humans — both before and after the digital turn — have a decent track record in spreading reliable information when it really matters.

Questions

• How does infrastructure topography change the way we think about the spread of information? Does the fact that access to Internet necessities (like electricity, working computers, and of course Internet itself) varies globally make it impossible to have a global conversation about concepts like the ones proposed in this article?
•  In times of crisis, would you rather automate fact-checking or rely on humans?
• Since they discuss how useful the crowdsourced map was — are some forms of data representation better suited to crowdsourcing than others? For example, is it better (broadly construing “better;” it can  mean easier, more efficient, more accurate, and so on) to crowdsource a map, or other imagistic data representations, than textual information? Does this change based on the context — e.g, it may be more effective to crowdsource textual information not in a time of emergency?
• What do we think of “infrastructure as a verb,” (“to infrastructure”), the notion that infrastructure is a constant, active process rather than a static object? What implications does this reframing have for HCI?

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Paper:

Gang Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. 2012. Serf and turf: crowdturfing for fun and profit

Discussion Leader:

Lawrence Warren

Summary:

Remarkable things can be done with the internet at your side but of course with great power comes great criminal activity. Yes it is true that crowd sourcing systems pose a unique threat to security mechanisms due to the nature of how security is approached and this paper points out the existence of malicious crowd sourcing systems. Due to the crowd sourcing systems astroturfing (refers to information dissemination
campaigns that are sponsored by an organization, but are obfuscated so as to appear spontaneous) nature, they are referred to as crowdturf systems and more specifically they are defined as systems where a customer can initiate a campaign and users receive money for completing tasks which go against accepted user policies. There are two types of crowdturfing structures described in the paper which are distributed and centralized and both of these structures need three key actors in order to operate (customers, agents, workers).

Distributed structure is organized around small groups hosted by the group leader and is resistant to external threats and are easy to dissolve and redeploy but is not popular due to the fragmented nature and lack of accountability.

Centralized structure is more like Mechanical Turk and is more streamlined and popular because of this. It is also because of the open availability of system which allows for infiltration.

Well known crowdturfing sites are running strong and is a global issue  and several have adopted Paypal which extends their reach to more countries than the one they are run within.

Reflections:

This paper shows the darker side of crowd source work and the results are astonishing. Millions of dollars have been spent by companies in order to bypass spam filters in order to get more exposure and crowdturfing seems to be the new way to spread information. Sites like this are not afraid of legal backlash and have increased in popularity despite threats from law enforcement. Large information cascades have been pushed and have gone around most filters designed to catch automatized spam meanwhile it is the clicks from users which is the end goal.

Questions:

• Will machine learning need to be advanced in order to filter out spam from crowdturfing?
• Is there any fault of users when it comes to the success of the such systems?
• Why do you think Weibo and ZBJ was used as opposed to twitter and Mechanical Turk (other than research location)?
• In this growing industry are there any negative results which can be seen from a company’s point of view?

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Paper:

Lasecki, W. S., Teevan, J., & Kamar, E. (2014). Information Extraction and Manipulation Threats in Crowd-powered Systems

Discussion Leader:

Lawrence Warren

Summary:

In automated systems there is sometimes a gap which machine learning has not had the ability to overcome as of current technology standards. Crowd sourcing seems to be the popular solution in some of these cases since tasking can become cumbersome and overwhelming for a single individual to handle. Systems such as Legion: AR or VizWiz use human intelligence to solve problems and can potentially share sensitive information. This could lead to several issues if a single malicious person is used in a session. They can have access to addresses, phone numbers, birthdays, and in some cases can possibly extract credit card numbers. There is also the possibility of a user attempting to sway results in a specific way in the event the session is not incognito. This paper describes an experiment to see how likely it is that a user would have malicious intent and how likely is it that a person will pass on information they should not and another to see how likely a user would be to manipulate test results using a few Mechanical Turk surveys.

Reflections:

This paper brought up a few good issues as far as information security involving crowd sourced information. My biggest criticism of this paper would be that there were no innovative mitigations created or even possibilities mentioned to protect against the attacks. Machine learning was mentioned as a method to blank out possibly sensitive information but other than that this paper makes it seem as if there is no way to stop it other than removing the information from the view of the user. Finding reliable workers was mentioned as a solution but that entails interviews and finding people which removes the benefits of crowd sourcing the work. This paper though informative, in my opinion did not make any headway in providing an answer, nor did it actually dig up any new threats, it just listed ones which we were already aware of and gave generic solutions which are in no way innovative.

Questions:

• This paper describes 2 different types of vulnerabilities to which a crowd powered system is vulnerable. Can you think of any other possible threats?
• Is there any directions crowd sourced work can take to better protect individual’s information?
• Crowd sourcing work is becoming increasingly popular in many situations, is there a way to completely remove either of the 2 two potential attack scenarios listed in this paper aside from automation?

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Demo: Global Database of Events, Language, and Tone (GDELT)

Summary:

The Global Database of Events, Language, and Tone (GDELT) is an open-sourced service supported by Google Ideas. It is a massive index of news media from 1979 to today, including real-time data from across the world. Articles are machine-translated into English (if not already) and then algorithms are applied to identify various events, sentiment, people, locations, themes and much more. The coded metadata is then streamed and updated every 15 minutes.

To work with the data, GDELT Analysis Service provides various ways to visualize and explore the data, such as EVENT Exporter, EVENT Geographic Network, EVENT Heatmapper, EVENT Timeline, EVENT TimeMapper, GKG Network, GKG Network, GKG Country Timeline, GKG Exporter, GKG Geographic Network, and many others. Datasets can also be moved into Google BigQuery, a cloud data warehouse, to run SQL queries, or downloaded as raw data files in CSV formats.

One of the main advantage to GDELT is the collection of real-time data from around the world. This data is coded and openly available for all to use. Not only that, but GDELT Analysis Service provides easy-use visualization tools for people not as familiar with programming. However, GDELT, like many other applications, can be used for nefarious ways. For example, regimes could track and record political protests and potentially use GDELT’s data to predict future protests. This would be particularly problematic in countries that would otherwise lack the data collection capacity to do such monitoring on their own.

Demo:

As mentioned above, GDELT is capable of so many different things. The following demonstration will be for only one of its services.  Let’s explore a geographic heat map of protests that happened in Canada in the past year …

1. Head to GDELT Analysis Service
2. Click EVENT HeatMapper
3. At this stage, you need to fill in your email address (so the results can be forwarded on to you) as well as the information of interest. For this example, let’s choose a start date of 10/24/2016 and an end date of 10/24/2017. Then we choose civilian for ‘recipient/victim (Actor2) Type’. The event location should be specified to Canada and the event code should be ‘protest’. We also want the number events as the location weighting because we are interested in number of unique events rather than number of news articles. Lastly, let’s choose interactive ‘Interactive Heatmap Visualization’ and ‘.CSV File’ for the output. Then click submit.
4. Now you wait until the results show up on your metaphorical door step…
5. And, magic! It appears. Now you can see the results as a CSV or the Heatmap by clicking either link. Let’s look at the HeatMap first. The slide bar is to adjust display thresholds. If we zoom in, we can see protests occurred in Southern Ontario, Toronto, Ottawa, Montreal, outside Quebec City and even some in Newfoundland and Labrador.
6. The CSV provides us with the longitude, latitude, and place name. In addition, it provides the number of events. For example, Marystown, Newfoundland had four protests. I did a simple Google search to see what was happening and it appears that fishermen were protesting at the union office.

Again, this is only one of the many tools available on GDELT.

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Technology: Truthfinder.com
Demo leader: Md Momen Bhuiyan

Summary:
Truthfinder is a commercial website for people search in the US, especially for finding information about “long lost friend” or “scammers”. This is a fairly new website started on March, 2015. Anyone can search the website using a person’s name or a phone number. They claim to have millions of public records from local, state and federal databases as well as independent sources. Due to the Freedom of Information Act these public records are available to anyone but collecting these information from multiple sources is hard. So truthfinder like websites make it easy to search. There are two types of membership in this site: one is regular and the other is premium. Premium users get more information about a person like: Educational information, Current and former roommates, Businesses and business associates, Voter registration details, Possible neighbors, Traffic accidents, Weapons permits etc. Most of these information are just possible match and doesn’t guarantee the correctness.

Reflection:

The site has a list of purposes it can or can’t be used. But anyone can misuse this for the purpose of screening candidate, stalking etc. During search it shows the records might have various type of information which could be a way to scam people into registering in the site. The average review of the website is below 3 [1]. Some suggested the information was either not accurate or several years old or can be gathered from google search. Only thing that was better was the opting out option which is mandatory by the FTC [2]. Still this site can be used by the journalists for verification purpose.

How to use:
1. The website has 5 options for searching: People Search, Criminal Records, Court Records, Reverse Phone Search, Deep Web Search.
2. Other than the reverse phone lookup option all them require the name of the person along with an optional location information.
3. After the search button is clicked it will ask the gender of the individual.
4. Then shows some graphics with a list of things its searching and ask if the relevant person’s location information.
5. After that a list of possible matches are shown along with location, age information of the match.
6. If the option of open report is clicked for a matched name it will show some further processing, an alert that the information might potentially be embarrassing and agreement on usage policy
7. After that it will go to the page for user registration where you have to give your name, email address and payment information

The whole process takes longer than five minutes.

[1] https://www.highya.com/truthfinder-reviews
[2] https://www.ftc.gov/news-events/media-resources/protecting-consumer-privacy/enforcing-privacy-promises

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Article: Davis, J. P., Jansari, A., & Lander, K. (2013). ‘I never forget a face!’. The Psychologist, 26(10), 726-729.

Summary:

The authors summarize the existing literature about super-recognisers, or those individuals scoring high on face-recognition tests, into three broad areas: general information, police officers, and the general population. In the first section – general information – the authors provide information about super-recognisers and prosopagnosia. Prosopagnosia, or face blindness, is when someone loses their ability to recognize familiar faces (including their own). This disorder can either stem from brain damage or genetic inheritance, and about two percent of the population has it.

At the other end of the face recognition spectrum – or Gaussian distribution – are those that are superior at recognizing faces, with an uncanny ability for facial recognition. Davis and colleagues comment that the belief that the super-recognisers’ ability is limited to face recognitions indicates support for the face recognition spectrum (prosopagnosia at one end and super recognizers at the other). They continue to note that facial recognition by humans can be superior to machines because faces are dynamic which can trip up machines.

In the next section, Davis and colleagues focus more closely on the police and super-recognisers. The bulk of the research in this section stems from the Annual Conference of the British Psychology Society presentation “Facial identification from CCTV: Investigating predictors of exceptional face recognition performance amongst police officers” (2013) by Davis, Lander, & Evans. These authors asked police officers who were deemed super-recognisers various questions and tested them on their recall ability. Some of the findings indicate that officers’ families do not share the similar super-recogniser skills and that these officers based their identifications on distinctive facial features.  In addition, the officers that provided broad strategies for facial recognition did worse than those with more narrower strategies. Lastly, the super-recogniser officers did well on celebrity recognition tests.

In the section on the general population, the authors pull from an unpublished study by Ashok Jansari that deployed the Cambridge Face Memory Test at London’s Science Museum. Their preliminary findings indicate that face recognition falls within a Gaussian distribution and fewer than 10 people had super recognition scoring.  Davis and colleagues then presents individual differences that influences facial recognition tasks, such as introvert/extrovert personality, as well as different processing difficulties for some (holistic processing/ inverted faces/ whole-part effect).

Reflection:

I recommend taking the Cambridge Face Memory Test to see where you fall on the face recognition distribution. This is the same task that was given to the officers and general population to take in some of the mentioned studies.

Interestingly, since the article was published, the Metropolitan Police actually formed a team of super-recognisers. This team complements the millions of CCTVs scattered across London.  Although these recognizers are human, it adds another dimension of surveillance to the streets of London, something some are trying to pull back on.

The authors did mention the uniqueness of humans versus technology in facial recognition. In particular, humans might be better than machines at identifying dynamic changes in human faces. However, with increasing advancements in technologies (and specifically biometrics), I wonder, will more advanced AI facial recognition software make this new unit obsolete in the next ten years or so? Or, do humans have some unique ability to notice dynamic changes that machines will not be able to mimic?

Lastly, it was interesting that, in the presented research by Davis, Lander, & Evans (2013), mostly white male officers thought they identified more black than white offenders. The authors attempted to attribute this finding to extensive contact with certain minority groups based on their policing jurisdiction. However, it makes me wonder the role that racial discrimination plays in their identifications and if some in the super-recognizer units simply reinforce policing discrimination? I am curious on their verification methods. How do they ensure relatively accurate recognitions as well as to eliminate any potential for bias and discrimination?

Questions:

• How does facial recognition surveillance by humans differ from facial recognition surveillance by machines?
  • And, is there one that is preferable?
  • Is one better at deterrence (both general and specific)?
  • What are the privacy/ethical implications with humans vs. machines?
• Is there potential for reinforcing discriminatory policing practices with super-recogniser policing?
  • What verification methods would need to be put into place?
• How can the general populations’ super-recogniser skills play into crowd-sourcing?

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Article: The CSI Effect: The truth about forensic science

Author: Jeffrey Toobin

Presentation Leader: Ri

Summary:

The article covers several points about investigative forensic science exploring its factual sides with the comparison to its fictional representation. The article tries to reference one of the then most popular CBS television series named “CSI: Crime Scene Investigation” [2], and 2 of its spinoffs “CSI: Miami” [3] and “CSI: New York” [4]. While doing so, the author describes how the real-life crime investigations are much more tedious and fallible in contrast to its fictional representations. The article starts with giving a real-life criminologist Lisa Faber’s statement in the courtroom where after analyzing hundreds of hair and fibers she could come to the conclusion that the evidence might have originated from the source.

The author points out this cautious description in conclusion compared to the much more confident assertion shown in the fictional TV shows. Additionally, the author mentions how the general public nowadays believe that science can, with definite certainty, identify the criminal based on the limited amount of evidence.

The author then focused on the analysis of bite marks, blood spatter, handwriting, firearm and tool marks, and voices, as well as of hair and fibers – which are popular forensic-science tests depicted on “CSI”. Many of these forensic techniques are outdated and somewhat obsolete in present courts. Some of these techniques also show errors in their follow-up examinations as well.

The author tries to find the origin of these forensic techniques in the early 19^th century and its successful usage in the early 20^th century. The author mentions an incident where a New York doctor, Calvin Goddard, analyzed bullets to successfully identify which submachine guns they were fired from. This might be revolutionary at that time, however, the techniques remain quite the same to this date without much improvement.

The author explores more on Lisa Faber’s journey at how she first came to choosing forensic investigation as a profession. The article also describes briefly the process of analyzing hair in the criminal cases. For DNA testing with hair, the author found out, hairs whose roots are intact only have nuclear DNA, which is unique to each person. However, the hairs collected from the crime scenes often lack hair roots, to begin with. The author then learns from Lisa Faber the complicated and tedious process hair analysis – starting from hair color to its chemical composition.

The author then briefly describes the 2 types of DNA testing – nuclear DNA and mitochondrial (mt) DNA. Although mtDNA testing is more frequent and can eliminate many suspects after testing, it is greatly prone to errors. The author explores the perception of different professional experts about the use of mtDNA and its fallibility. It divides the experts into 2 groups where one group believes in the credibility of using mtDNA to further establishing findings from generic hair analysis and another group believes it should be excluded because of its flaws.

Later in the article, the author describes the recent steps taken by Faber in her lab combining traditional and modern technology for hair and fiber analysis. It is called Biotracks – a burglary program – which analyzes tissue, hair, fiber samples dropped at the crime scene by the criminal while using rubber gloves, wiping tissue, soda bottle, etc.

On the final note, the author hinted how the fictional TV representation makes the forensic profession popular and quite hipster in the eyes of public, despite having a somewhat stretching representation from the real-life scenarios.

 

Reflection:

I found the article to have an interesting way of exploring the forensic investigation while establishing a sense of dissimilarity with its fictional representations. As the television series like CSI, tend to serve the purpose of entertainment, they often seem to build their fictional world based on some real-life existence. One of the topics that are strongly existent in the fictions is that they need to have a definite finding at the end of the show. This leads them to force feed some conclusions which might not be used with such confidence in the real-life.

Additionally, in the real-life, the process of investigation can be a long, complicated, and monotonous process. Contrast to this, the fiction episodes are usually less than one hour long. Hence, many of the criminologists in the fictions possess much higher confidence in the accuracy of their findings. As described in the article – an air of glamour, an aura of infallibility. This is done mostly to make the show more interesting and entertaining to the audience. The shows, after all, are meant to serve as an entertainment, not documentary.

It was very interesting for me to learn the intrinsic details of the investigative process and the margin of error for them. I was unaware of how much effort and time are spent in collecting suitable evidence, analyzing them, and trying to come to a conclusion keeping potential errors in mind. I was really intrigued to find out that even at times follow up examinations might come to the same false conclusion. Also, the incident of the convicted suspect, Jimmy Ray Bromgard, in the rape case of an eight-year-old, where he was later found innocent. The initial trial had the manager of the Montana state crime lab, Arnold Melnikoff, testify stating that the odds against the suspect were one in ten thousand. However, the later DNA testing proved that the initial conclusion was wrong. It made me realize how difficult it is to come to a definitive conclusion in the forensic medicine sector. It also justifies how Lisa Faber phrased her conclusion in a very carefully picked words in the earlier part of the article.

The thing that intrigues me further is that the public misbelief about the accuracy of the scientific findings. I was also intrigued by the impression the jury had over Faber’s fiber analysis, as the article states “The prosecutors liked the idea of fibre evidence… it was more ‘CSI’-esque.” It raises the question in my mind whether we are mixing up real-life facts with fictional enticements. Like Michael J. Saks states, “It’s the individualization fallacy, and it’s not real science. It’s faith-based science.”

 

Questions:

• Is fictionalizing investigative forensic science exoterically a good approach?
• Many experts hold different opinions when it comes to mtDNA as a subsequent test to hair analysis. What are your thoughts on using mtDNA (which has a higher error rate) in the court?
• Do you think making the general public aware of the potential error rate in forensic science might actually decrease the credibility of the whole sector?
• With the relevance of this class, can the crowd be trusted in crime investigations given that even the experts are at times fallible?

 

References:

[1] https://www.newyorker.com/magazine/2007/05/07/the-csi-effect

The CSI Effect: The truth about forensic science by Jeffrey Toobin

[2] http://www.cbs.com/shows/csi/

CSI: Crime Scene Investigation

[3] http://www.cbs.com/shows/csi-miami/

[4] http://www.cbs.com/shows/csi-ny/

 

 

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Article:

Chapter Six: Doing Something Good in So You’ve Been Publically Shamed by Jon Ronson

Summary:

In the sixth chapter of his book, the author opens with an account of developers Hank and Adria at Pycon. During a conference presentation, Hank and his colleague were joking together about ‘dongles’ and ‘forking,’ terms that had a clear sexual connotation.  In the row directly in front of them, Adria turned around and snapped a photo of the two developers. Not much time passed before Hank and his colleague were pulled out of the room by the conference organizers to discuss their sexual comments. After explaining the situation, not much more came of the situation during the conference.

However, when leaving the event, the two developers soon discovered that Adria posted their photo and the subject of their jokes on Twitter: “Not cool. Jokes about forking repo’s in a sexual way and ‘big’ dongles. Right behind me #pycon” (pg. 114). To further explain her tweet, she produced a more extensive blog. In conversation with Ronson, Adria further detailed her feelings of danger after hearing the comment from the men behind her.

The repercussions for Hank was termination from his work. Nothing was mentioned about the consequences for his colleague. After being terminated, Hank turned to Hacker News to publicly apologize for his lewd remarks. In his statement, he mentioned the outcomes of his actions – his termination. Adria asked to remove this portion of the apology.

The public jumped into the conversation to both defend and further shame both Hank and Adria. Adria was received rape and death threats while her work was hit by a DDoS attack. The DDoS attackers threatened to continue until Adria was fired. She was shortly fired. Hank was defended and then later insulted by men’s right bloggers. These bloggers focused on Hank’s masculinity – or lacking masculinity. Both the shamer and shamed were harmed by the actions of the crowd.

In the latter half of the chapter, Ronson switches gears slightly by writing about his interview with a 4chan user accused of DDoSing PayPal. According to the author, her motivation for shaming was “the desire to do good” (pg. 123) and stems from a place of powerlessness: “on the internet we have power in situations where we would otherwise be powerless” (pg. 123). This place of powerlessness is apparently rooted in violations, namely stop and frisk, of others constitutional rights.

Later, in their conversation about Adria, the 4chan user defends Hank claiming that Adria infringed on his and his colleague’s freedom to speech. And, that in the case of Sacco (another victim of shaming mentioned in Chapter Four), she became the symbolic enemy – rich, white person. The 4chan user claims that some “crimes” –like these – cannot be handled by the courts but by shaming.

Reflection

As we learned, 4chan/b/ is ephemeral, with threads lasting no more than a few minutes and most disappearing from the front page within hours.  Because of this, Ronson’s comment that “somebody inside 4chan was silently erasing me whenever I tried to make contact” (pg.121) seems like a misunderstanding of 4chan/b/. If so, a tad more research would have been beneficial instead of misleading readers about the nature of 4chan/b/ users or the level of administration/moderation that occurs on the forum.

In addition, the author’s connection between stop and frisks and online activism seems relatively weak. In the connection to police, Ronson makes the statement that “one by-product of [stop and frisks] was that some repeatedly frisked young people sought revenge in online activism – by joining 4chan” (pg. 126). This statement is based on conversations with only two individuals from New York City. And, in his conversation with Troy, there is no mention that Troy even engages in online activism; his activity on 4chan could simply be to have a free space without interference instead of seeking revenge.  Although the evidence supporting the association between NYC stop and frisks and online activism isn’t particularly strong in this book, the notion that powerlessness can translate over into bullying –or shaming more broadly-  does make sense.

The conversation about Hank and Adria could have been bolstered with a conversation about guilt. Shame and guilt are different. The former leads to painful feelings about our identities (we feel bad about ourselves), and the latter leads to empathic views about how we behaved poorly as well as the consequential harm.  The discussion of guilt/shame could help pull out the issues with shaming a little better to demonstrate why guilting someone might be a better alternative than shaming.

The shameful rather than guilty response can be seen for both Hank and Adria. In Adria’s response, she states: “no one would have known he got fired until he complained” (pg. 129) … and it was “his own actions that resulted in his own firing, yet he framed it in a way to blame me” (pg. 130). The result of her shaming appears to be defensive and angry rather than empathic and remorseful. Something similar can be said of Hank. In his response to Hacker News, he might not have appeared furiously angry. But his description of distancing from female co-workers shows some alteration to his self-worth. He notes that with female developers, “I’m not as friendly. There’s humor, but it’s very mundane. You just don’t know. I can’t afford another Donglegate” (pg. 130).

For both Hank and Adria, shame seems counterproductive.  I have not finished the book yet, but I hope there is a discussion about shame versus guilt.  And, in particular, how the public can elicit guilt rather than shame to help change people’s behaviors for the better.

Questions:

1. Is shaming inherently bullying?
2. If not, when does shaming become bullying?
3. Is shaming justifiable for the greater good?

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