Digitized Brainstorms

Researchers and activists have been asking, “Is mass surveillance, the next mass incarceration?”  The expansion of mass surveillance throughout the 2000s appears to be following the same troubling trajectory as did mass incarceration in the 80s and 90s.  Both started with a nationwide policy effort to address a loosely defined public policy concern.  For mass incarceration, the War on Drugs drove the rate of imprisonment up; for mass surveillance, it’s the War on Terror.  At the same time, both tend to disproportionately target and mainly affect already marginalized racial and religious groups.  Thereby, making it a highly politicized and often partisan political issue to highlight and address.

Can we expect this pattern to continue?  Most certainly yes.  Particularly, as President Trump seeks to empower law enforcement and NSA with new surveillance technologies. 

What can we do?  We can start by having difficult conversations about the ways that data is currently enabling the criminalization of people and communities.  These issues must be elevated to a higher level of public awareness before we can even begin thinking about the government and corporate surveillance Goliath that we will all have to confront sooner or later.  

Here are some of the ways that data is currently being used to enable criminalization not only in the US but globally:

-          Immigrants and border enforcement: A variety of federal agencies currently collect information on individual’s immigration status.  For example, the Department of Health and Human Services has databases with refugees’ and unaccompanied minors’ data and data from the DACA and Dreamers database can be used to identify and locate undocumented people for detention, deportations, or “expedited removal” in the US.  Most recently, the White House is considering expanding border data collection by requiring that visitors provide access to social media and device data. Scholars have already found that data collection at the border has already been used to target journalists, Muslims, and immigrants.  

-          “Pre criminal” activity: Police departments all over the world are increasingly using computerized “predictive policing” to anticipate crimes and allocate officers. In some cases, police departments are creating a “heat list” that unfairly associates innocent people with criminal behavior.   They are also starting to incorporate social media data into their predictive models and to assign individuals “threat levels.” For example, in China the government is developing pre-crime technology that will assign every citizen a threat score.  

-          Sentencing: Municipalities are beginning to use data-driven and machine-learning methods for setting bond amounts and jail sentences.  The expanded use of algorithmic risk-assessments has also raised concerns regarding the accuracy of these tools to forecast someone’s future violence or criminality, as well as their potential to exacerbate racial disparities, particularly in the criminal justice system.

-          Social movements/protest and media practices: All around the world, governments and police are increasingly monitoring online spaces on popular social media for dissent and speech which challenges the legitimacy of a particular government. In the US and abroad, technology has been used to monitor political and social justice movements.

*This blog was originally written or Ford Foundation*

In our increasingly complex and interconnected world, innovations in technology and data are inching us closer to a reality driven by automation, prediction, personalization, surveillance, and the merging of our physical and digital lives. This is taking place at the same time that the world is experiencing transformative cultural, demographic, economic, and political shifts. In the face of so much uncertainty and change, the independent watchdog group Freedom House reports that globally, Internet freedom has declined for the sixth consecutive year, “with more governments than ever before targeting social media and communication apps as a means of halting the rapid dissemination of information, particularly during anti-government protests.” At the same time, Civicus has highlighted the many threats facing democracies, representative institutions, and civil society as a whole.

As we begin a new year and a new political administration takes office in the US, let’s take some time to consider some pressing issues that exist at the nexus of technology and social justice—and think about how we as social justice advocates can address them most effectively. Even amid so many unknowns, we can be certain that these issues are among those that will shape 2017 and the years and decades beyond it. And they will be central to the work of building a free, open, and transparent future.

1. Online hate crimes, harassment, and discrimination are increasing

In 2016, we saw an increase in the number of reported online hate crimes and harassment targeting Muslims, immigrants, and LGBTQ people, as well as groups working to protect reproductive rights and strengthen racial justice. At the same time, Internet and other communication technologies are being used to promote the online radicalization and organization of hate groups.

2. Fake news, campaigns of misinformation, bias, and propaganda are proliferating

During the 2016 election, the use of intelligent Twitter bots, targeted advertising, and search engine manipulation affected what Internet users saw on specific platforms and search engines. “Filter bubbles” and algorithms have been blamed for failing to separate real news from fiction, and researchers have noted an escalation in bias, propaganda, and misinformation online. Together, these factors contribute to increased polarization and hamper the free flow of accurate information that is essential for civil discourse, policy making, and ultimately democracy.

3. Trolling threatens democracy and free expression

During the 2016 US presidential election, online trolls were credited with influencing political discourse. This kind of trolling is also a global epidemic. Freedom House has reported that across the world, state-sponsored trolling operations (aka “troll farms”) attack government critics and independent media often by posting thousands of comments at a time, polluting online dialogue through hate speech and disinformation. Hampering free speech, trolling is frequently aimed at journalists and activists, along with those engaging in political protest and other forms of expression.

4. Reduced regulation creates risks for consumers

2017 may see the deregulation of the largest cable and phone companies, and the weakening of consumer protection laws. These reduced federal regulations could undermine a free, open, and secure Internet and undercut a variety of hard-won initiatives, protections, and services that benefit low-income individuals and households. These include net neutrality wins, federal guidance and regulation of the Internet of Things, Lifeline and broadband subsidies for low-income households, and efforts to regulate prison phone costs.

5. Governmental databases put pressure on minority groups

As technology and data processing power gets more sophisticated, government databases such as the National Security Entry-Exit Registration System, deferred action, or gang databases could be used to discriminate against or target vulnerable groups. At the same time, the expansion of biometric technologies in the immigration system and other areas may raise concerns about government monitoring and data sharing.

6. Increased surveillance puts vulnerable groups under a microscope

The expansion of legislation such as the Patriot Act, along with the use of high-tech surveillance tools, including drones, facial recognition software, automated license plate readers, stingray tracking devices, and the NSA’s PRISM program, can lead to increased surveillance of citizens. This raises particular concerns for vulnerable groups, among them Muslims, immigrants, and LGBTQ communities.

7. Less attention to issues of algorithmic bias and online discrimination creates opportunities for abuse

The White House’s Big Data initiative has studied big data’s potential to discriminate, especially when it comes to housing, employment, credit, and consumer issues.  Legislation such as the Consumer Privacy Bill of Rights Act of 2015 and an amendment to the Electronic Communications Privacy Act was intended to govern the collection, use, and distribution of consumer data and extend the standard of protection for online and digital content. But under a new administration, the fate of such rules and regulatory attention is unclear.

8. Advocacy groups need digital security protections

In 2017, advocacy groups and social movements may be subject to a greater degree of surveillance, harassment, or both. As a result, they will need a strong digital security infrastructure to help protect them against potential threats, as well to support them in developing strategies for responding to attacks as they arise.

9. Technology and data initiatives provide important support for social justice work

Increasingly, technology will play a central role in strengthening a progressive agenda. To achieve this, organizations might focus more on developing strategies for organizing and base-building across different sectors, building strong progressive narratives, and supporting open data initiatives at the state and local level to increase transparency and accountability.

10. Developments in open government and transparency affect advocacy work

Problems with typical network drawings

1)     too much information

2)     colors – old, clashing with each other, related concepts in different colors

3)     pixelated

We are good in processing complex things, high bandwidth information. Less so in sequential coding / processing.  Key in visualizations is the ability to show more than one variable (4 – 5) in ways that are easy to process.

In network vis  - 1) data analysis and explorations and 2) data presentation. 

Abstraction – taking away the reality underlying data (real world information), abstracted to make the picture nicer (easier to perceive and understand).  This is central to network visualizations

Criteria for Network Visualizations

show structure (in visualizing of larger networks, a lot of research energy is being spent to create algorithms)

Visione is an example of a tool that uses more of the space – optimize distribution on surface, which makes it nicer

Preattentive perception

your brain is capable is handling information without thinking about what you are seeing too much

the elements of preattentive perception are variables that we can use in visualizing networks (size, position, shape, color, saturation, texture)

In network data visualization – things that are on top or in the middle are more important

Color hue & Saturation – changing the color does not mean changing the saturation

Texture – visualizations that use different textures are not easy to perceive

What visual elements are good for visualizing what?

elements and data types must be considered

relevance of elements

quantitative information (shape, for example is last in McKinley’s list)

Lines can be used to create texture if, for example, you color the lines and they overlap in such a way that they create a texture that has information.

Think about what is the story? Choose elements to that will bring attention to the story and not take away from it. If substance is more important than the structure, then that should be highlighted.

Perceived and Actual Magnitude - Implications – if you just have small differences, it’s pretty difficult to show the differences. Others say that you should not use a 2D object to visualize a 1D variable/information. One solution is to use the power or the square root of something to highlight the differences.

The Munsell color scheme makes it possible to calculate differences in color – systematically, so that you can get a sense of uniform and non-uniform colors based on their distances / positions on a color scheme. (LAB in Photoshop and other Adobe products are similar to Munsell’s scheme).

PDF or EPS

stores vector graphics (geometrical objects (a square is a square rather than a set of pixels), scales infinitely); PNG and JPG stores raster graphics (pixels, scales infinitely). Post-processing is doable with PDF in Illustrator (for vector graphics)

**This post was written for BlueRidge Labs@RobinHood and is being reposted here.**

The idea of public interest law was first noted in a famous speech by Supreme Court Justice, Louis Brandeis in 1905. In that speech Brandeis argued that, “able lawyers have to a large extent allowed themselves to become adjuncts of great corporations and have neglected their obligation to use their powers for the protection of the people.” He essentially felt that lawyers had largely failed to utilize their skills in the protection and promotion of social good and that a significant refocus and cultural shift was needed.

His vision was not realized until the social instability and turmoil of the 1960’s. During this time, foundations, philanthropists, and community groups began to issue calls for fellowships and employment opportunities targeting “public-interest” law students. Likewise, law graduates also began to seek opportunities to make an impact and do meaningful work in the face of the complex social issues during the civil rights era. It’s my belief that we’re in that crossroads and social turmoil once again – but this time involving technology.

We stand at a moment of tremendous change, unpredictability, and ambiguity. In a few short decades, technology has grown to touch every issue in our society, from health, employment, and education to economic development, political engagement, civic life, etc. It has also touched the American civil justice system which faces both promising opportunities and uphill battles in the development and implementation sustainable tech-enabled interventions. The Blue Ridge Labs Fellowship has been especially insightful in both imagining how a broader public interest technology strategy could be implemented as well as highlighting some areas that will require a more nuanced and concerted effort.

Here were my five main takeaways:

1. Importance of integrating the various distinct silos of expertise and information: There is a fundamental lack of evidence-based research and practice in the access to justice context. At the same time, the civil justice community involves a variety of stakeholders, from the direct users, to courts, legal aid, social service groups, government agencies, etc., each with their own observations and interpretations of problem areas and solutions. The Fellowship has taught me that a way to find commonality is to focus observations around pain points of the various stakeholders and then making connections and groupings of common points that are worth exploring closer.

2. Managing the tension between complexity and solvability: The access to justice context is highly complex. Each issue area, whether housing, consumer, immigration, family, etc., warrants a certain degree of specialization and deep digging. Due to this complexity, there is a tendency to focus on areas which most personally appeal to you or are easiest to comprehend. However, one must ask whether those issues with the most appeal are the ones worth focusing limited energy and resources. Should public technologist be allowed to explore problems and solutions they want to take on, or should substantive experts help guide them to the areas that would have the greatest impact?

3. Creating a common culture in support of innovation: The introduction of new sector of public interest technologists into social justice issue areas often dominated by traditional stakeholders is easier said than done. There is a need for a deep cultural shift to take place alongside the development of a public interest technology sector. Traditional stakeholders may need to learn to shift legacy attitudes and find ways to develop trust with the technology community. This is why a key element of that challenge will be to implement more effective strategies for developing and integrating technologists into relevant organizations and projects. Likewise, public sector technologists should actively seek the input and attempt to build bridges of understanding and empathy when interacting with traditional grassroots stakeholders.

4. Don’t forget the broader context: In the area of access to justice, one has to realize that the legal aid community is largely isolated from broader discussions and communities focused on social services and even human rights law and interventions. This creates a fragmented social net ecosystem. Therefore, there is deep insight and valuable lessons that can be drawn by looking at these broader systems which are relational to the legal aid sector.

Word Lens is an augmented reality app for iOS, Android, and Google Glass, that translates printed text using your device's camera alone – no network connection necessary. Google recently announced that its bought the company in order to incorporate their technology into Google Translate's broad language coverage and translation capabilities.  So you can only imagine, there are still many great things to come in the world of augmented-reality translation. 

Are you willing to share your data with a third party?

Seems like a fairly easy question to ask.  Yet, the answer is much harder define and pinpoint.  Why is this you may ask? Well, it turns out our feelings towards sharing our personal information is highly contingent and context-dependent.   It depends how and for what it will be used.  It depends how we’re asked.  It depends how transparent and clear third parties are with us.  It depends how trustworthy they seem.  It depends what I will get out of this exchange in return.

It just depends.

During a January 2016 Focus Group, the Pew Research Center had similar findings in that they found that regardless of household income, education, gender, etc., - most of us have ambivalent and ambiguous feelings towards third party access of our data. 

Ambivalence and resignation, the new social reality of modern life. 

Notably, a lot of the ambivalence emerges from the simple fact of how difficult it is to access third party’s terms of service or how hard it is to understand and make sense of it (can anyone say plain language).  Lack of easy to understand and find terms of disclosure are the first step in empowering users to fight this seems of personal resignation that increasingly looms over our modern life.  Seems easy enough you would think? 

During the November 2014 midterm elections, the focus turned on the dangers of the possibility of rampant voter fraud. Studies attempted to put the issue in perspective, for example, finding that the documented in-person voting fraud rate was .0003% (the Brennan Center has , with only four cases of in-person voter fraud). Therefore, voting fraud was hardly a key public policy dilemma in the US. However, some politicians insisted on the severity of this problem and introduced bills aimed at “improving” voting in the United States.

In Georgia, Republicans have introduced a bill to reduce early voting from the present maximum of 21 days to 12 days. The goal, says Rep. Ed Rydners, a sponsor of the proposal, is “clarity and uniformity.”  But for a state which lists, education, public safety, and healthcare, as its top public policy issues, one can’t help but wonder if efforts on this bill are time well spent.  

If some groups continue to feel that despite statistics, voting fraud continues to be a major public policy concern, perhaps a simple solution would be to make election day a mandatory federal holiday or maybe requiring same day registration, or expanding online voter registration.  

Instead, lets make sure this election season, greater attention is paid to the main factors threatening to marginalize citizens and further undermine democracy in the US.  A recent analysis by the Electoral Integrity Project ran by Harvard’s Pipa Norris, found that the US ranks the lowest among all other Western democracies regarding election integrity.  In fact, we rank  in 42nd place worldwide, right between Micronesia (41) and Mexico (43).   

We need a strong network of technologists, hackers, activists, and scholars to band together and increasing the focus on the true election issues in the US:

Poor electoral laws

Voter registration concerns

Redistricting and;

Regulation of campaign finance

Currently enjoying this report, The New War Correspondents: The Rise of Civic Media Curation in Urban Warfare, by Microsoft Research Fuse Labs. 

Many of the findings discussed in this report can be used to better understand Twitter use during political protests.  Some of the more applicable findings make me wonder:

1. How do different age and income groups use Twitter during protests or movements? In Mexico, there is a younger age group, 18-34 using Twitter to protest urban warfare and share information.  In the Brazil protests, we have seen the emerging slightly older middle class voicing their concerns on Twitter. 

2. How do the ups and downs of sharing of information on Twitter help to encourage further acts of public demonstrations, violence, etc?  In both Mexico and Brazil, the use of hashtags rise and falls in correlation with large public events.  If these public events promote Twitter use, does Twitter use also promote more public events?  In other words, does Twitter only serve as an information-sharing tool while protests are going on or does it also help to encourage more protests, violence?

Information about our medical treatments and health are becoming an increasingly hot commodity.  IBM recently announced that it’s taking its Watson artificial intelligence technology into health care with powerful partners such as Apple, Johnson & Johnson, and Medtronic; and a pair of acquisitions.  Their ambitious agenda is to make use of health information gathered by millions of Apple devices and create a unit dedicated to providing data analytics to the healthcare sector.  Because these services generate millions of data points, IBM is hoping that with customers' consent, they can turn that anonymous data into meaningful information for the treatment of illnesses or rehabilitation.

Granted, that the "quantified self" movement provides a freebie for this initiative.  Plus, there is much promise in our ability to gain vital health insights by quantifying and merging the thousands of medical data points we produce during our lifetime. 

Still, we the public should heed with much caution.  

ProPublica reports that since October 2009, health care organizations and their business partners reported 1,142 large-scale data breaches, each affecting at least 500 people, to the U.S. Department of Health and Human Services. We can expect that breach issues will likely continue, as hackers are becoming increasingly more sophisticated at stealing and criminally exploiting data.

Yet, despite this threat, those companies most likely to be targeted continues to be lax on security. 

Why you may ask? 

Because despite this number, there’s a lack of public outcry that can force companies to spend more money and resources on data security. 

And should there be a public outcry?

Most definitely!

First, as those affected report - medical data breaches can have major identity theft and medical treatment ramifications.  After a healthcare data breach, such as the recent massive one at health insurer Anthem, victims reported altered medical history, imposters attempting to receive medical care using a patient’s name, and attempts to use medical data to access their financial records.  In response, some companies are providing free credit monitoring services for a select number of years for those affected.  But for many this is not enough because (1) issues can arise years after the limited free credit monitoring has expired and (2) credit monitoring services do not examine medical claims, where more of the life-threatening changes can arise. Second, there are vast problems with anonymized data. Artificial intelligence requires programmers who won't re-purpose data or violate privacy with proprietary software. However, there's no way to monitor or audit this.

Third, current HIPAA forms are inadequate in informing patients of the ways their medical data is being handled and the risks that can arise.  Therefore, where is the real consent from patients to release their data?

In this world of a multitude of new media and technology tools, its important to keep in perspective that these technologies are never the strategy.  Rather its (1) why and (2) how we use them that should always be the focus.

Below is a case study looking at mental health issues in New York state, which provides some tips on how visualizations can be used to promote change:

The report, Blueprint for a Public Health and Safety Approach to Drug Policy, by the Drug Policy Alliance and The New York Academy of Medicine provides a comprehensive set of recommendations for fixing a broken drug policy that is a “bifurcation between two different and often contradictory approaches - one which treats drug use as a crime and the other view, as a chronic relapsing health or behavioral condition.”

Anyone who has spent time working in human services knows that multiple programs (whether offered through community groups, nonprofits, churches, or government agencies at the local, state, and federal level), own a piece of the puzzle when it comes to helping and healing people and families. In the case of substance abuse treatment, there’s a myriad of actors in health/mental health, schools, substance abuse services, law enforcement, corrections, and departments of children and families who all need to be coordinating and working together. However, as the Blueprint highlights, this does not always happen. Rather, “without a united framework and better coordination, these actors and agencies often work at cross-purposes” (Blueprint Report, pg. 4). The themes of coordination, overlapping, and cross-purposes appear throughout the report, and these are what I highlight in the discussion of data visualization here.

Provoking Change: Your Data Can Tell a Story

Data visualizations can tell a clear concise story about why an issue is important and why change is needed. So, they are ideal tools for fostering greater awareness and supporting advocacy efforts.

Data visualizations are often associated with their popular counterparts, information graphics (aka infographics).  Although both allow you to use and transform your data into a compelling presentation or powerful story, there is a key difference between the two. While data visualizations take complex sets of data and display them in a graphical interface, like a chart or map, so users can gain insight into patterns and trends, infographics use data visualizations in concert with text and other tactics to tell a story, make a point or communicate a concept (“Data Visualization and Infographics: Using Data to Tell Your Story”).

Visualizations are especially effective for data advocacy because they:

Make your message more compelling: Let’s face it, visualizations are simply much better at stimulating thought and conversation than more traditional textual or numerical data.

Allow you to reach a wider and more diverse audience:  The reason for this is that visualizations allow you to convey complex data and abstract information in an easily digestible and shareable formats.

Visualize information, systems, networks and flows which can be valuable for highlighting social problems and need for policy changes.

Illustrate timelines and relationships that can help readers put the dots together in understanding a problem (“Data Visualization and Infographics: Using Data to Tell Your Story”).

Visualizing New York Drug Policy

This next section outlines step-by-step instructions to create your own data visualization. I searched NYC Open Data and Open Data NY Gov for the best data set that would help me highlight the idea of overlapping human services agencies that work on substance abuse issues in New York State. The best data set I found was one which provided information on Local Mental Health Program in New York State, broken by county and program subcategory.

Because of the geographic nature of this data, I opted to create a heat map.  Because I was also interested seeing the distribution of the types of substance abuse mental health programs in New York according to county, I found a histogram to be useful as well.  I then selected two free and easy-to-use data visualizations tools: Many Eyes and Tableau Public.

This brings me to the first lesson in creating data visualizations:

 (1) Don’t be seduced by the exciting and cool visualization tools: In creating visualizations for advocacy and social change, it’s critical to keep in mind your objective and to avoid visualizations which just offer eye-candy.   You want the reader to be attracted to your message, not your methodology or the cool visual tools you used.  So, ask yourself if you want your data to provide (a) description, (b) exploration, (c) tabulation, or (d) decoration (see Tufte’s “The Visual Display of Quantitative Information.” )   There is a lot you can accomplish visually with basic free tools such as the two that I used.  However, for a full list of all data visualizations tool available visit Bamboo DiRT.

(2) Prep your data: Every great visualization begins with a coherent and well-organized data set.  As a result, it’s important to clean your data and only leave the most essential variables organized in the best possible format to reveal the main relationships that you want to highlight between your variables.

Two free tools which can help you clean and prep  your data for visualization are:

Data Wrangler

Google Refine

For my data set of Local Mental Health Program in New York State, I filtered the data according to those that provided substance abuse counseling and then I created a frequency distribution with a pivot table.  Pivot tables (also called contingency tables and cross tabulation tables) are a powerful means of data visualization and data summarization.  You can download my pivot table here if you would like to experiment with it.

Mental Health Program Sub-CategoriesAssertive Community TreatmentCare CoordinationClinic TreatmentComprehensive PsychiatricEmergencyContinuing Day TreatmentCrisis Day TreatmentEducation ForensicsGeneral Hospital Psychiatric IP UnitGeneral SupportIntensive PsychiatricRehabilitationPartial HospitalizationPersonalized Recovery-Oriented ServicesPrivate Psychiatric HospitalResidential Treatment FacilitySelf-HelpState Psychiatric HospitalSupport ProgramTreatment ProgramUnlicensed HousingVocational

Many Eyes provides information on how to format your data according to the visualization that you chose.

After creating a pivot table of my data which adds up the total number of program subcategories according to county in New York, I am then able to upload the data onto Many Eyes.

  After uploading the data, I compared how the pivot data appears on Many Eyes versus my spreadsheet to ensure data accuracy.

To see the final interactive heat map designed on Many Eyes click on the image below:

   This heat map showcases the density of mental health programs that deal with substance abuse in New York State.  The heat map is interactive because the key allows you to select different sub-program categories to see which counties have the most programs and which don't.  

(3) Ensure Content Focus: The best visualizations are transparent about the data used.  As a result, in designing my interactive heat map, I also included drop down menus for people to see what types of substance abuse programs were available in which counties and which were not.  As a result, I wanted to keep the focus on the content of the data and not necessarily on the very cool heat map that I just made!

(4) Reveal the data at several levels of detail, from a broad overview to the fine structure:  Tableau Public offers much more customization features which allow you to showcase your data on many different levels.

Tableau dashboard features more options for organizing your data and highlighting specific trends geographically broadly or on a more granular level.  

(5) Avoid Distorting the Data: A good visualization should always showcase the data honestly.  As a result, things such as pie graphs and charts are frowned upon because they of their distortion of the data and lack of clarity.  This is what's often deemed as avoiding "chart junk" (Tufte).

For example, my pivot table histogram below does a better visual picture of highlighting consistencies and gaps in mental health services across program sub-categories and counties than the map using pie charts.  

Learn About Tableau

Pivot table histogram highlighting the distribution of each mental health program sub category by counties.  As a result, this visual quickly shows you the overlaps as well as gap in services.

Now look at my same pivot table data but this time using pie charts rather than heat map or histogram.  Although, somewhat visually appealing, the pie charts do not shows how the programs each make up a whole, thereby, disguising the potential problems of overlap.

Becoming a Data Visualization Expert: Final Tips and Resources

 (6) Make it memorable:  Studies have found that memorability alone can enhance the effectiveness of visualizations.   A recent study, which is the most comprehensive study of visualizations to date, found that visualizations that were most memorable had:

"Human recognizable objects”, these were images with photographs, body parts, and icons--things that people regularly encounter in their daily lives.

Effective use of color, specifically, visualizations with more than six colors were much more memorable than those with only a few colors or a black-and-white gradient.

Visual density, meaning that visuals that had a lot going on were more memorable than minimalist approaches.

For inspiration on data visualizations that promote advocacy and social change visit:

Progressive Policy Infographics

Using technology to increase access to justice for the poor and other marginalized communities is not new.  For over ten years various communities throughout the US and worldwide have been exploring ways that technologies can be used to educate communities about the rights, connect them to assistance in their neighborhoods, help them complete vital court forms, and help navigate the ambiguous and challenging civil and criminal court process. 

However, as with most things involving technology, as the tech and data capabilities become more and more sophisticated, so do the capabilities and risks from using these technologies. 

Therefore, now is the time for the various stakeholders to think about bringing greater unity and coordination in this work.  Along these lines, there is increasing interest in looking at how to best coordinate and organize the various tech and data initiatives happening across the country.  In the civil justice end, Illinois Legal Aid Online, have been the first statewide nonprofit technology support center that serves this role, and there so, has been a model for the country.

Another recent example, is one that Ive been happy to be a part of, has been the launch of the Florida Justice Technology Center (FJTC).  FJTC was founded last year and in this short time, there has been some key lessons learned about what’s needed to start and sustain an effective nonprofit technology center focused on using technology for promoting justice.

In the article, “Lessons Learned (So Far) from Establishing a Nonprofit Technology Center: The Florida Experience”, FJTC’s Executive Director, Joyce Raby outlines the lessons learned so far from having started FJTC. In a nutshell she outlines the following key lessons:

Lesson 1: The Need for a Shared Vision Lesson 2: Translating the Shared Vision into Specific Tasks Lesson 3: Stable Sustained Seed Funding Lesson 4: Expect Some Turbulence Lesson 5: Be Willing to Try Stuff, to Fail, and to Try Again.

There’s an increasing push for the use of predictive analytics, triage algorithms, expert systems, document assembly, etc., to deal with the access to justice gap which exists locally and nationally.  In the past year, civil justice groups and the legal aid community have been partnering with third party vendors to explore some of these technologies.   Being that these types of partnerships will only increase in the years to come, I have organized a training for the national civil justice community to address the ethical issues which arise from the use of data-driven technologies. Below is the info and consider joining us if you can!

Description:

What risks and benefits frameworks should legal aid groups and decision makers when designing data-driven interventions? How can data regulation, policy, and standards effectively govern and provide accountability? Where should legal aid programs turn to seek guidance on emerging data ethics, laws, and policies? Speakers:

Solon Barocas, Associate at the Center for Information Technology Policy at Princeton University.

Alethea Lange, Policy Analyst at Center for Democracy and Technology's Consumer Privacy Project.

Wilneida Negron, Florida Justice Tech Center/Data & Society Research Institute (moderator)

Time: 1 PM Eastern, Noon Central, 11 AM Mountain 10 AM Pacific.

Register: This training will be held on GoToWebinar you MUST preregister at: https://attendee.gotowebinar.com/register/2577523910409594884.

Excited for this data security training for the civil justice community.  As the civil justice community increasingly looks for data-driven interventions (i.e. expert system, predictive analytics, justice portals, etc.) to help poor people navigate our broken justice system, greater awareness of the data gaps that exist in the day to day work is needed.   Ive organized a great panel with speakers from AccessNow.org, ACLU, and Just-Tech.org.

Consider joining us!

Description: Don’t let cybersecurity become the next digital divide!  Learn about the privacy and security implications that arise when lower-income individuals increasingly have to use tech and mobile-enabled tools for accessing justice.   This presentation will outline key directives to guide your program when adopting data-centric technologies and strategies.

When: Tuesday, 4/26 at 1pm EST

How to register: Visit this site to get the Go To Webinar details.

I recently collaborated with the Neighborhood Crime and Drug Project, directed by John Jay Faculty in the Department of Anthropology, Ric Curtis, Josh Eichenbaum, and Ernest Drucker.

Using data they collected for their study, I decided to explore:

Differences in how genders perceive and experience guns and gun violence.

Difference in the experience of gun violence among age groups in New York City.

Neighborhood level experiences with guns.

I created my own graphics to supplement the findings presented by the Neighborhood Crime and Drug Project.   Below you will find the results.

Please note: These findings are based on a limited survey conducted throughout NYC neighborhoods, so it is not comprehensive.

Residents in neighborhoods in South Bronx and Central Brooklyn report having heard the most gun fire.

This interactive bubble chart which you can access here, breaks down reports of hearing gun fire by NYC neighborhood.  The blue bubbles in the center represent those neighborhoods that report hearing gun fire.  Then the size of the bubble represents the numbers of reports for that given neighborhood.  I used a bubble chart in this instance because it can quickly display hundreds of individual values at once.

Credit: Wilneida

2. The age group most likely to know someone with a gun is 18-29.  It peaks at age 21 and then drops accordingly by age. 

The second age group most likely to know someone with a gun is 30-44.  The age group less likely to know someone with a gun is over 60.  This hints at a possible social dimension to gun use and violence among NYC youth. Below are two simple charts which illustrate this.  As you can see in the second chart below, the instances of knowing someone with a gun peaks at 21 and then drops the older you get.

18-29 year olds have the highest instances of knowing someone with a gun. 

Credit: Wilneida

  21 year olds in New York City are the most likely to know someone with a gun.  

Credit: Neighborhood Crime and Drug Project at John Jay University

3. Men in NYC are more likely to know someone with a gun, but women in NYC are more likely to have heard a gun shot.

This chart highlights how experiences of gun violence can differ by gender in New York City; as men are more likely to report that they know someone with a gun.

Credit: Neighborhood Crime and Drug Project at John Jay University

Credit: JustPublics@365

Credit: Wilneida

4. African Americans are more likely to know someone with a gun.  However, Latino/as on average tend to know more gun owners.    

Credit: Neighborhood Crime and Drug Project at John Jay University

  Credit: Neighborhood Crime and Drug Project at John Jay University

5. Residents in Public Housing and High-Rises Are More Likely To Know Someone with a Gun 

Credit: Neighborhood Crime and Drug Project at John Jay University