There are a few challenges when devising a pre-assessment activity for students at the start of a school year. Here are a few that come to mind:

• If you teach CS in this era, you likely have a diverse set of learners with varying levels of exposure and skills. Devising a specific assessment that is useful for all can be challenging.
• Your CS class is probably an elective or optional, so you want to attract and retain students. An elaborate and challenging assessment at the start of the year can set the wrong tone, so I wanted to devise a more collaborative and engaging activity.
• While coding and CS languages are essential, they can obscure computational thinking, so it is important to devise interesting, engaging questions that can be answered without code.
• As much as these questions help me plan the class, I also hope they provide the students with a sense of their ownlearning and how they want to grow in the class.
• I also get a little impatient to start the curriculum and often breeze past this pre-assessment stage. This year will be different.

With this caveat, here is a pre-assessment activity I plan to use in my class. Here are the steps I am planning to follow as I use these questions this coming week with my students:

• I want to begin this pre-assessment with a conversation between students. I will pair students to discuss each category of questions and then rotate to a different partner. This portion will happen with little to no writing or note-taking, as I hope students engage verbally and are able to engage in back-and-forth conversation.
• I plan to give the verbal/oral student significant time, possibly one or two classes before we move to the written portion. I plan to break up the written portion over a few courses so the students can only write a response to one or two questions per class.
• After the written portion, I plan to discuss each question so students can share with the class and sense the consensus on the answer.

Secondary Computer Science Pre-assessment 2024–2025

(here is a GDoc version if you prefer. Feel free to copy and edit. Please give credit)

Directions: Discuss each question in the category with a partner. Then, write a response to one of the questions in each category. You do not need to use code in your response, but share what you know about computational thinking.

Algorithm Design:

Describe the steps you would take to sort a shuffled deck of cards into four piles based on their suits (hearts, diamonds, clubs, spades).

Explain how you would design an algorithm to find the shortest path between two points on a map. What factors would you consider, and how would you ensure the solution is efficient?

Decomposition:

Describe the steps needed to calculate the average of a list of 5 numbers.

Break down baking a cake into smaller, more manageable tasks.

Abstraction:

Explain the concept of a “variable” in computer programming. Please provide an example.

Describe how you would abstract the idea of a “bank account” in a program. What properties and methods would you include in your abstraction?

Logic:

Consider the statement, “If it is raining, then the ground is wet.” Explain under what conditions this statement is true and when it is false.

You have three boxes: one contains only apples, one contains only oranges, and one contains both apples and oranges. The boxes are mislabeled. You can pick one fruit from one box without looking inside. How can you determine the correct labels for all three boxes?

Data Representation:

You’re creating a music playlist app. What data structure would you use to store the songs in a playlist, allowing easy shuffling and reordering? Explain your reasoning.

You are designing a system to track students’ grades in different classes. What data structure would you use, and how would it facilitate the calculation of overall GPA and per-class averages?

Functions:

Write steps to determine if a given number is even or odd.

Write steps to determine if a given year is a leap year.

Debugging:

You have a program that’s supposed to count the number of vowels in a sentence, but it’s consistently returning a higher count than expected. How would you approach finding the bug?

You have a program that is supposed to calculate the area of a circle, but it’s returning incorrect results. Describe how you would go about debugging the issue.

Simulation:

Describe how you would simulate a simple traffic light system at an intersection. What rules would govern the behavior of the traffic lights?

Imagine you need to simulate the behavior of a vending machine. Describe the rules you would implement to ensure correct transactions, product dispensing, and returning change.

Problem-solving:

You have to make ten deliveries in Los Angeles, and you want to save time and gas. How will you devise the best route?

You must organize a large event with multiple speakers, ensuring no scheduling conflicts. How would you create a schedule that maximizes efficiency and minimizes downtime between sessions?

Personal Interest and Past Work:

Tell us about your favorite area(s) of computer science, any specific technologies or topics you’re excited about, and your long-term goals in the field.

Briefly describe any past projects, coursework, or experiences where you’ve applied your computer science skills, highlighting the technologies used, challenges faced, and lessons learned.

Thanks for reading. If you found this useful let me know at mrkhanatndv@gmail.com

Since 2016, I have hosted multiple virtual and in-person events for students, teachers, and others to engage with creative coding via Creative Coding Fest, aka CC Fest. You learn about past events here. During those events and elsewhere, many educators have expressed a desire to learn more about how creative coding can be taught in the classroom. To support that goal, I am launching a free virtual camp for teachers. If interested, please complete the application form by Friday, August 30.

Starting in late September, I will offer weekly two-ons where the cohort will work through lessons and practice coding. The classes will emphasize using code to make art and will cover the basics of coding. You can read more about the schedule and proposed classes here. There is no cost to the program, but participants are expected to join for the duration of the program. If you have any questions, please email me at mrkhanatndv@gmail.com.

Happy New Year! A group of educators and I are hosting Virtual CC Fest, a creative-coding event for students and teachers, this weekend, on Sunday, January 24. Virtual CC Fest is a beginner-friendly coding event for students and teachers to engage in workshops, share and hear about projects and curriculum, and listen to a couple of great keynotes about creative coding.

It’s happening on Sunday, January 24, at 3pm EST / 12pm PST.

You can learn more and grab a ticket here — http://ccfest.rocks/register

There will be 12 sessions covering topics including p5.js, Python, Hydra, Processing, ethics, AI, high school math, making games collaboratively, and computational photography. The opening keynote will be delivered by Cassie Tarakajian, creator and lead maintainer of the p5.js Web Editor, and the closing keynote will be Jordan Harrod, student, YouTuber, and advocate.

You can buy a ticket, donate, or grab a free ticket. The funds will be shared between the presenters and Processing Foundation.

In non-pandemic times we would be organizing CC Fests in New York, San Francisco, and Los Angeles around this time of year, as well trying to bring the event to more parts of the country. The pandemic has forced us to go virtual. In the summer of 2020 we hosted our first virtual CC Fest, and it went great! Being virtual gives us the opportunity to engage with teachers and students globally, and I hope we will see more virtual CC Fests.

If you are interested in learning more about participating in or hosting a virtual or IRL CC Fest, please get in touch with me at saber@processing.org.

virtual CC Fest — Jan 24, 2021 was originally published in Processing Foundation on Medium, where people are continuing the conversation by highlighting and responding to this story.

This is part of a series of documents meant to support a discussion and investigation of ethics and morals in relation to the impact of computer science on the world at-large. You can find the rest of series here:

Introduction to #ethicalCS, Abstraction, Algorithm, Programming Data, Networks Equity and Access Design, UX/UI Teaching, Learning Goals (coming), Algorithmic Discrimination (coming),

Introduction: In this document we will engage with the concept of ethics of bias in Computer Science education. Here is a helpful way to think about bias in computing:

…three categories of bias in computer systems have been developed: preexisting, technical, and emergent. Preexisting bias has its roots in social institutions, practices, and attitudes. Technical bias arises from technical constraints or considerations. Emergent bias arises in a context of use. Although others have pointed to bias in particular computer systems and have noted the general problem, we know of no comparable work that examines this phenomenon comprehensively and which offers a framework for understanding and remedying it. We conclude by suggesting that freedom from bias should be counted among the select set of criteria — including reliability, accuracy, and efficiency — according to which the quality of systems in use in society should be judged.

(Bias in Computing Systems, Freidman & Nissenbaum, 1996).

This document is generated from the #ethicalCS Twitter chat. You can find the highlights from the chat on bias here with Camille Eddy, Eric Meyer, Sara Wachter-Boettcher, and others.

Questions:

1. How is bias in real-life related to bias in computing? How does bias interact with technology to impact people?
2. How should we help students understand how bias function in computing and the impact it has on marginalized peoples?
3. How should we see and mitigate the harm that bias generated with data-driven technologies?

Ideas:

1. Data and data collection is never neutral and can easily be manipulated to confirm a bias.
2. While algorithms and bias affect our lives it can be hard to figure out how the hidden systems make decisions.
3. Agreed upon and documented definition of fairness along with deep understanding of bias can help us build better algorithms.
4. Bias in computing is a function of bias in society, but amplified by algorithms that are scalable and portable.
5. The technology industry has under-invested in understanding and mitigating bias.
6. Since the algorithms of technology companies are protected from scrutiny by intellectual property protection, it can be hard for the public to see how bias functions.
7. Developing standards, practices, and codes can help practitioners use best practices to mitigate bias.
8. Personal bias can develop through ignorance so seek challenging narratives outside our experience.
9. Technologies that are data-driven like artificial intelligence, predictive policing, etc. are especially susceptible to bias, but also to being inscrutable.
10. As more industries and sectors automate the effect of machine bias will spread to many areas of life.
11. Good models have feedback loops that challenge algorithms and seek to debias data.

Resources:

• Design for Real Life (book) by Eric Meyer & Sara Wachter-Boettcher
• Technically Wrong (book) by Sara Wachter-Boettcher
• How algorithms are pushing the tech giants into the danger zone (Guardian UK) by Sara Wachter-Boettcher
• The Tech Industry Is Clueless About People. Let’s Debug It (Mother Jones) by Kanyakrit Vongkiatkajorn
• Facebook treats its ethical failures like software bugs, and that’s why they keep happening (Quartz) by Sara Wachter-Boettcher
• Change Catalyst: Powering Inclusive Innovation
• Stock Photos of People of Color (POCIT) by Camille Eddy
• Recognizing Cultural Bias in AI (POCIT) by Camille Eddy
• Parable of the Polygons by Vi Hart and Nicky Case
• Biased Algorithms Are Everywhere and No One Seems to Care (MIT Technology Review) by Will Knight
• Weapons of Math Destruction (book) by Cathy O’Neil
• Algorithms of Oppression by Safiya Noble
• From Tech Blogger to Fog Creek CEO by Code Newbie

This is part of a series of documents meant support a discussion and investigation of ethics and morals in relation to the impact of computer science on the world at-large. You can find the rest of series here:

• #ethicalCS
• Abstraction
• Algorithm
• Programming
• Data
• Networks
• Equity and Access
• Bias (coming)
• Teaching (coming)
• Learning Goals (coming)
• Algorithmic Discrimination (coming)

Introduction: In this document we will engage with the concept of ethics in design. Here is a helpful way to think about ethics in design:

If ethics is about the question of how to act, and designers help to shape how technologies mediate action, designing should be considered ‘ethics by other means.’

Every technological artefact that is used will mediate human actions, and every act of design therefore helps to constitute moral practices.

(P. P. Verbeek, What Things Do, 2009, p. 69).

This document is generated from the #ethicalCS Twitter chat. You can find the highlights from the chat on Equity and Access here with Alisha Austin, Antionette Carroll, Ariel Kennan, Jonathan Shariat, Farheen Malik, and others.

Questions:

1. How do we support and protect vulnerable people when designing products and services?
2. What practices, processes, and tools can support ethical design?
3. How and why should we push for ethical design?

Ideas:

1. Remembering that users are humans and do not try to trick them.
2. Design with people to reflect diversity, produce justice and make accessible products and services.
3. Be intentional about co-designing with people of different races, class, gender identities, religious and/or spiritual affiliations, sexual orientation, ability status, etc.
4. Problem solving in our communities can help us engage in ethical thinking.
5. Measure outcomes, evaluate impact, and iterate from feedback.
6. There are different design paradigms, processes, and tools to help designers engage in ethical thinking.
7. Teams may benefit from appointing a rotating leader to advocate for ethics.
8. Make a case for ethical thinking with your team and leadership by gathering data, resources, user voices.
9. Be persistent, wear down doubters, find allies, don’t be afraid of making it political.
10. Have a good understanding of your biases and engage in diversity, inclusion, and equity training.

Resources:

• Social Attitudes Implicit Association Test by Project Implicit
• Access NYC by New York City
• 18F GSA
• Center for Humane Design by Tristan Harris
• An Incomplete List of Resources for Equity-Centered Designer by Isabelle Yisak
• Tragic Design by Jonathan Shariat and Cynthia Savard Saucier
• How Bad UX Killed Jenny by Jonathan Shariat
• Ethics for Design
• Microsoft Design
• Civic Creatives
• Designing Justice
• Rafael Sergio Smith
• Creative Reaction Lab’s Equity-Centered Community Design Field Guide
• Liberatory Design
• Civic Service Design Tools + Tactics by NYC Mayor’s Office of Opportunity

This is part of a series of documents meant support a discussion and investigation of ethics and morals in relation to the impact of computer science on the world at-large. You can find the rest of series here:

• #ethicalCS
• Abstraction
• Algorithm
• Programming
• Data
• Networks
• Design, UX/UI (coming)
• Bias (coming)
• Teaching (coming)
• Learning Goals (coming)
• Algorithmic Discrimination (coming)

Introduction: In this document we will engage with the ethics of equity and access in Computer Science education. Here is a helpful explanation equity and access in the context of math education:

Creating, supporting, and sustaining a culture of access and equity require being responsive to students’ backgrounds, experiences, cultural perspectives, traditions, and knowledge when designing and implementing a mathematics program and assessing its effectiveness. Acknowledging and addressing factors that contribute to differential outcomes among groups of students are critical to ensuring that all students routinely have opportunities to experience high-quality mathematics instruction, learn challenging mathematics content, and receive the support necessary to be successful. Addressing equity and access includes both ensuring that all students attain mathematics proficiency and increasing the numbers of students from all racial, ethnic, linguistic, gender, and socioeconomic groups who attain the highest levels of mathematics achievement. (National Council of Teaching Mathematics).

This document is generated from the #ethicalCS Twitter chat. You can find the highlights from the chat on equity and access here with Dan Shiffman, Saron Yitbarek and others.

Questions:

1. How do we remove barriers so other can contribute, especially to open source projects?
2. How do we see the ethics and morals of tools, practices and platforms?
3. How do we provide meaningful education to a diverse student body?

Ideas:

1. Show your mistakes so that beginner can feel welcome to take risks.
2. Build tools that increase accessibility by making it easier to understand code.
3. Provide high-quality and easily accessible learning materials on varied platforms.
4. Representation matter so show a variety of identities, experiences, and outcomes.
5. Make yourself accessible to others by going to events, meetup, and helping out.
6. Highlight creativity, ethics, and identity to create more inclusive classroom projects.
7. Focus of solving problems that matter instead of just vocational training.
8. Diversify how we teach, who teaches, and where we teach.

Resources:

• CodeNewbie podcast on the ethics of coding by Saron Yitbarek
• Base.cs by Vaidehi Joshi and Saron Yitbarek
• Coding Train by Dan Shiffman
• Accessible p5 IDE by Mathura Govindaraja with Cassie Tarakajian and Claire Kearney-Volpe
• Coding in the Wild by Jeremy Keeshin
• Making art with Raspberry Pi by Stephanie Nemeth
• Computer Science is not the Answer by Shawn Van Every

(This essay was written with critical feedback from Erica Corbin, JP Connolly, and Matthew Reininger)

1. Teachers

Ten years ago I was teaching math and science at a K-8 Quaker school in San Francisco. It was an exciting time in my career. I put lots of energy into my classes, trying challenging curriculum with lots of support and appreciation from students and their families. This was the first time in my career laptops were available for student use during class and I took advantage of opportunities for collaboration on digital documents. In the evenings I was working on my Masters in Special Education at San Francisco State University. In those classes I learned about how to use data to support students with different learning needs. For the first time in my career I felt I was a successful teacher.

While I was ready to redesign my classroom and work with a diversity of learning styles there was much that remained hidden and missing from my education and classroom. I did not understand my identity as a South Asian man. As a middle class immigrant from Bangladesh I was expected to be an engineer or doctor. Someone like me would be welcome in Silicon Valley and most other industries accessible to White and Asian men in the United States. As an educator I received no training in how to support the identity development of my students. There was no person assigned the job of pushing students and teachers to think and learn about racial identity development at the school.

For all schools, identity development, equity, and inclusion are key challenges in a diversifying country. And most fail to reflect the diversity of their locality and fail to help their students learn about their racial and cultural identities. Private schools which are generally whiter and richer have been drivers of segregation as they offer those with privilege an opportunity to opt-out of the public school system. Many private schools, especially those in the South started as “segregation academies” to thwart court-ordered desegregation in Brown v Board of Education. While these institutions are no longer explicitly racist they enforce a loose form of de facto segregation in the South and in the rest of the US.

In New York City, which has one of the highest densities of private and charter schools, school choice has lead to a resegregation of the public school system according to the New York Times.

Despite the structural challenges a dedicated group of diversity practitioners seek to transform private schools into equitable and accessible institutions.

2. Schools

The incorporation of technology into the classroom has been one of biggest trends in the evolution of school over the past few decades. From how we communicate to how we assess learning has been augmented by the use of computers and digital technology. New roles, job titles, companies, and philosophies have created a new industry, educational technology to support this transformation. This trend has happened while schools have sought a much deeper and meaningful transformation. Educational institutions seek to be inclusive of historically underrepresented identities and equitable in opportunities for all community members. Let’s take a moment to reflect on these two trends and make connections between the two. Drawing these two efforts together can make both more effective and bring them closer to fulfilling the mission of a school.

Diversity and technology are two of the most quickly evolving areas in education. As some schools have made technology and equity a priority they have hired staff, financed budgets, and devoted time to those efforts. For technology integration, schools have followed each other in one-to-one device programs, maintaining digital services, and working with students and teachers. According to a 2007 National Association of Independent Schools (NAIS) report about 80 percent of schools had a high-level staff position, with 60 percent reporting to the head of school. A quarter of the schools reported having a tech staff of at least 5 members. And most report that 1 to 2 % of the school’s operating budget is spent on the technology department.

The heavy investment in technology reflects one of the ways that private schools extend and compound privilege. Private schools prefer to be called independent schools, implying a level of independence from the public system and other schools while denying the exclusive and exclusionary nature of the sector. But in practice, this independence is hard to identify and often means they are independent of scrutiny from outsiders. Most private schools have adopted 1:1 digital devices for their students and teachers, finding space on the schedule and staff to teach Computer Science or Maker education, trained faculty in the use of digital tools for school work, and embraced a host of digital platforms. This reflects an understanding by those in power that the future of work is digital. But the past, present, and future are more diverse than these institutions, while diversity effort remain modest.

About five years ago I began working in technology education when I moved back to New York City. I was fortunate to land at a progressive private school in the West Village. One of the best parts of the school was a deep commitment to diversity and identity work, something that was entirely missing from my career previously. Under the leadership of diversity practitioners, a full-time role at the school, I was able to participate in a faculty of color group, support students of color, and engage parents of color. The school sent me and others to the People of Color conference organized by NAIS. In these groups, I was able to see myself as a person of color and how I could seek and offer support to other people of color, especially those that have been excluded from these institutions. By seeing my racial identity more clearly I could see better how others saw me and my role as a teacher. I consider this an essential part of who I am and a part of my job at a school even though it is not part of any job description or assignment.

But this level of commitment and support for diversity work is still rare in the private school community. In contrast, the role of the diversity and equity staff are in much fewer number, loosely defined, and under-funded according to a NAIS survey of diversity practitioners from 2017:

“The job role is still often imprecisely defined; only 31 percent say that their job responsibilities are extremely well-defined or very well-defined. A little more than half of the diversity practitioners report to the head of school (55 percent), and less than half (47 percent) are part of their school’s senior administrative team. Almost three-quarters (73 percent) do not have anyone reporting to them in their diversity role.”

3. Unpaid Labor

There a few metrics available to us to compare diversity and technology directly and to judge their proximity to power at a school. As non-profit institutions, private schools have file documents to the government explaining their mission and finances. These 990 forms can be found on Guidestar. The form includes names, titles, and salaries of the highest paid employees. I looked at technology and diversity positions and salaries in ten leading private schools in New York from 2016, the last year for which data is available. Of the ten schools, five have titles that include the word “technology” compared to only one that includes the word “multicultural”. One school has two titles that include the word technology in the top salary. This is out of a total of 90 jobs.

Of the 6 technology jobs the average salary is $199,227 compared to $170,364 for the single diversity job in the top salaries list from those ten schools. These are some of the oldest and richest educational institutions in the country where they can spend large sums of money and resources on priorities. Both departments are at the periphery of the goals of schools, according to the count of jobs. From this brief look, it is easy to see the discrepancy between the two departments. Missing from this analysis is data on race, gender, sexuality and other known categories of identities that can explain gaps in pay and power. But it is worth remembering that diversity is often done by women of color and technology integration is often done by white men. One project seems to feel like an essential innovation while the other is left at the margins.

In schools that do not fully support diversity work or have a Director of Diversity, most schools, the work is often undertaken unofficially by a faculty member of color, a group that is often marginalized in overwhelmingly white and wealthy private schools. This unpaid labor comes with a great cost too because you are often called upon to offer help in situations related to diversity by the leadership when the time and effort could be spent on labor that will get a reward:

New research backs up previous studies that suggest professors of color may be imperiling their tenure and promotion prospects by performing service work to help their institutions become more racially inclusive. It’s common knowledge in academic circles that publishing papers and acquiring research dollars are the most valuable activities for moving up the higher education ladder. Scholars who excel at research tend to get academic tenure.

On the contrary, professional work tied to improving diversity — such as building opportunity pipelines, recruiting and mentoring — are discountable. A study out this year, found that marginalized professors spent twice as much time mentoring, recruiting and “serving on various task forces,” than their White male counterparts. Higher education experts say that these activities take away time that could be applied to the more career-accelerating work of publishing. (How Faculty of Color Hurt Their Careers Helping Universities with Diversity, Diverse Education)

The damage that the sidelining of a faculty member of color causes should be considered in light of data on the diversifying student body and overwhelming whiteness of the teacher staff.

4. Taking Back Innovation

A broader vision of innovation that goes beyond the technology industry and accounts for identity and equity work can help bring more support for this unpaid but essential labor. Instead “innovation” in the public school system, such as vouchers, charters, and school choice, has led to a rapid resegregation of the public school system. We have to study outcomes and take them seriously and be willing to change our approach if needed. While there are many threads worth looking at in comparing technology and diversity departments in private schools, we need to focus on the power and language of innovation.

With the resources devoted to technology in staffing, it is not surprising that technology usage feels ubiquitous in many schools. But we have not seen a similar commitment to diversity department staffing. And as equity, diversity, and inclusion work remains essential to how schools function in society this blind spot creates a large deficit in what schools can and should be. Ideally, the tools of technology can aid the work of diversity by creating a caring community with the critical mass to enact change. Ultimately if schools want to home to innovative thinking and practice they have to commit to diversity work a way that will allow themselves to be transformed by it. One the best avenues for this change is to redirect technology staff to engage directly with diversity work as they are well placed to have large scale impact on a school. While this may seem like a sudden change in mission and scope technology practitioners and diversity practitioners, are often asked to change directions, take on new projects, and serve the mission of a school in innovative and challenging fashion. To borrow a term from tech and re-framing equity and inclusion as innovation offers avenues to work towards to justice.

Toward that end I recommend:

Short term

1. Have all technology staff take diversity and equity training.
2. Support and encourage technologists, especially those of color, to engage in diversity work.
3. Redesign technology curriculum to take ethics, identity, and creativity seriously.
4. Study and propagate diversity practices and innovations — affinity groups, privilege walks, White Anti-Racism, implicit bias, etc.
5. Reward diversity work with compensation and promotions.
6. Reform hiring practices to be transparent, especially about compensation and roles.

Long term:

1. Study how digital technology can aid diversity work as in #blacklivesmatter, Color of Change, GLSEN, and Teaching Tolerance
2. Create a deeper understanding how technology, especially the internet, allows hate and bigotry to spread in new ways.
3. Set public goals for diversity that matches the diversity of the locality.
4. Diversity of the teaching staff should also match the diversity of the student body.

Ayesha A. Siddiqi on Twitter

I'm an immigrant and I still feel an obligation to America's moral debts because part of becoming American was joining the systems of oppression this country functions on.

A. Questions

Despite being born in Belgium to Bangladeshi parents there was little question that we would not stay in Europe. Like most countries in Europe and the world, it is incredibly difficult to immigrate and stay in Belgium. While it is possible to move and study or work if you are skilled, as we did in Saudi Arabia, it is incredibly difficult to move, live, build a family, and stay on our own terms. But the United States, through its mythology, culture, and laws, has always seemed different. It is one of the few large diverse democracy that is not built around a single ethnic group, a single culture, a single language. For example, the US still offers birthright citizenship, where newborns are offered citizenship, in an era where many countries have abandoned it. When one gets here, lives here, and learns a bit of the history one learns that the story is much more complicated. And what seemed like settled questions are being argued anew.

B. Liberty Island

Through most of its history, the United States pursued an explicitly racist immigration policy. The government’s policies sought to restrict immigration from Asia and Africa and helped to cement a white-majority country with an underclass of African American and Native populations. Those policies changed dramatically in 1965 when Lyndon B. Johnson signed the Immigration and Nationality Act of 1965. The legislation eliminated race and national-origin restrictions, gave preference to family reunification and skills, and restricted immigration from the Western Hemisphere. The Act passed with overwhelming support in the House and the Senate. Before signing it LBJ made the following remarks:

This bill says simply that from this day forth those wishing to immigrate to America shall be admitted on the basis of their skills and their close relationship to those already here.

This is a simple test, and it is a fair test. Those who can contribute most to this country — to its growth, to its strength, to its spirit — will be the first that are admitted to this land.

The fairness of this standard is so self-evident that we may well wonder that it has not always been applied. Yet the fact is that for over four decades the immigration policy of the United States has been twisted and has been distorted by the harsh injustice of the national origins quota system.

Under that system the ability of new immigrants to come to America depended upon the country of their birth. Only 3 countries were allowed to supply 70 percent of all the immigrants.

Families were kept apart because a husband or a wife or a child had been born in the wrong place.

Men of needed skill and talent were denied entrance because they came from southern or eastern Europe or from one of the developing continents.

This system violated the basic principle of American democracy — the principle that values and rewards each man on the basis of his merit as a man.

It has been un-American in the highest sense, because it has been untrue to the faith that brought thousands to these shores even before we were a country.

Today, with my signature, this system is abolished.

We can now believe that it will never again shadow the gate to the American Nation with the twin barriers of prejudice and privilege.

The ideas in the Act, broadening access to US immigration, support for family reunification, and encouraging skilled immigrants to come, grew out of the Civil Rights movements and the Cold War. The Civil Rights movement had made the idea of the US as a white-identity nation a toxic idea that lost much of its mainstream political support. The US was heightening its disastrous engagement in Vietnam, seeking allies in the Cold War, and ready to view Asians, Africans, and other groups differently.

But baked into the new immigration system is a dangerous and toxic idea, that hard-working, highly skilled immigrant helping to make this country better and assimilating deserve to be here and others do not. This “Model Minority”, often highly skilled Asian immigrants, stood in contrast to the existing minorities already here, Black and Native, and others elsewhere who would not be welcome. The Model Minority Myth is damaging to people of color, Asian or otherwise, and helps perpetuate the myth of meritocracy and shield White Supremacy from criticism and dismantling.

C. Outside In

When I moved to the United States in 1996 with my family we landed in Catonsville, MD, a wealthy white suburb just outside Baltimore. My aunt, my mother’s sister, had come to the US with her husband a decade before thanks to the demand for doctors. She was able to sponsor my mother’s visa via the family reunification program. We would spend a year here while my father finished up his job in Saudi Arabia and looked for a job in the States. Hearing that the public schools were weak my parents enrolled the high school-aged offspring, my older sister and me, in private schools. I was sent to an all-boys college-prep Catholic high school located in the city. Until that year most of what I understood the US came from large Hollywood productions like Apollo 13, Forrest Gump, and Pulp Fiction. The school prided itself on its athletics program, religious instruction, and traditional perspective. The school was nestled in a bucolic campus in the West-side of Baltimore City. The neighborhood was largely African American while the school was overwhelmingly white.

D. Inside Out

In that same summer of 1996 when we moved to the US, Bill Clinton signed the Personal Responsibility and Work Opportunity Act, a cornerstone of the Republican Party’s Contract with America and fulfillment of Clinton’s promise to “end welfare as we know it.” Passing with bipartisan support, although both the Democratic senators from Maryland voted against it, the bill contained racist and sexist ideas crouched in the logic of “deserving poor”. For example, before this law, the federal government sought to protect mothers from having to work but now full-time mothering became a luxury for those that could afford it. The most toxic idea contained was the idea that people on welfare had become reliant and were no longer looking to work, an idea that had gained bipartisan support over past few decades. So the legislation placed limits on aid a person could receive, restricted eligibility rules, and lets states decide how to use the aid. For example, legal immigrants would now be excluded. The logic seemed to be that immigrants and the undeserving poor were a burden on productive members of society. In his signing statement Clinton stated many of his objections that should be fixed in future legislation:

First, while the Act preserves the national nutritional safety net, its cuts to the Food Stamp program are too deep. Among other things, the Act reinstates a maximum on the amount that can be deducted for shelter costs when determining a household’s eligibility for Food Stamps. This provision will disproportionately affect low-income families with children and high housing costs.

Second, I am deeply disappointed that this legislation would deny Federal assistance to legal immigrants and their children, and give States the option of doing the same. My Administration supports holding sponsors who bring immigrants into this country more responsible for their well-being. Legal immigrants and their children, however, should not be penalized if they become disabled and require medical assistance through no fault of their own. Neither should they be deprived of food stamp assistance without proper procedures or due regard for individual circumstances.

Those objections were not fixed, of course. An era of helping the poor in the United States was coming to an end. Now our government provides the least amount of help to the fewest number of people since 1971. Coincidentally, the poverty rate has not been meaningfully affected by the legislation while placing greater hardship and red-tape on those seeking support. In fact, the number of families with children living in poverty has increased twenty percent in the twenty years since the legislation.

E. Two Zipcodes

The segregation that I saw in Baltimore in 1996, a poor black neighborhood in the city and rich white suburbs, is so common that they are taken as a given in most of the United States. They can seem like the natural outcome of tough but fair capitalist system that rewards those that work hard. Take the two zipcodes we landed on in 1996, 21229 in the Baltimore City where the school is and 21228, the suburb in Baltimore County where we lived. Demographically the two neighboring zipcodes are inverse reflections of each other, 21228 is 70% white and makes about $80,000 per household per year and 21229 is close to 80% black and makes $45,000 per household per year.

This dichotomy in the zipcodes here and elsewhere is a result of an active effort to segregate the races in Baltimore and elsewhere through actions of the city, state, and federal governments. As Richard Rothstein has shown in the Color of Law African Americans faced significant housing discrimination via federal, state, and local laws and policies. In 1910 Baltimore was one of the first to adopt an ordinance prohibiting African Americans from buying homes in blocks where whites were a majority and vice versa. This took a previously integrated city and over time made it segregated. Rothstein writes:

Exclusionary Zoning Ordinances could be, and have been, successful in keeping low-income African Americans, indeed all low-income families, out of middle-class neighborhoods.

But to create white-majority suburbs something greater was needed:

First, the government embarked on a scheme to persuade as many white families as possible to move from urban apartments to single-family suburban homes. Then, once suburbanization was under way, the government, with explicit racial intent, made it nearly impossible for African Americans to follow.

F. Choices

In 1996, as a sophomore in high school, all this seemed normal but one incident sticks out. Through the first few months of school, I knew to keep my mouth shut in class. One day in science class the teacher asked a question which seemed to stump everyone. I knew the answer and to help move the class along I raised my hand. When called on I said the word the teacher was looking for, aluminum. My classmates started laughing hysterically. I was confused and being new I did not know anyone who could explain what happened but I knew it was mean-spirited. Now I think it was a mix of my accent, the pronunciation, being new, being brown. If it were to happen now to me or my students I know I would act differently.

Anil Dash on Twitter

I'm in favor of immigrants who come to America to not do anything remarkable, who just hang out & sometimes spend time with their friends and family, who are flawed, regular people. I'm in favor of immigrants who don't have to be superheroic or economic martyrs to justify access.

Since the US only allows “deserving” immigrants to move and stay, mostly middle-class, skilled, or those following family, many land in the suburbs and adopt a ‘suburban’ perspective on the country. But that perspective forgets or chooses not to remember how our immigration system was made less racist. It also forgets internal policies that continue to hurt communities of color via the legacy of housing discrimination or the denial of governmental aid. Continuing to broaden the base of immigrant beyond those that fit into a ‘deserving’ model and preserving family reunification offers a more equitable opportunity to becoming US citizens. Commitment to that goal combined with paying back our debt to communities of color is essential in our current political debate about immigration, welfare, and the future of this country. The choice before us is not just to reject the hate being spread but to create a vision for a more inclusive country.

Sydette on Twitter

We're the same immigrant/black/brown/liberal city you hated yesterday/10/30/59 years ago. We also still here #NYCStrong

This is part of a series of documents meant support a discussion and investigation of ethics and morals in relation to the impact of computer science on the world at-large. You can find the rest of series here:

• #ethicalCS
• Abstraction
• Algorithm
• Programming
• Data

Introduction: In this document we will engage with the CS concept of Networks which

Networks, like the Internet, allow computers to interface with other computers through a set of rules, or protocols, that define how computers send and receive data. Protocols and standards are created and agreed upon by groups of people. (CSforAll NYC Blueprint).

This document is generated from the #ethicalCS Twitter chat. You can find the highlights from the chat on Networks here with Ingrid Burrington, Dan Phiffer, Jenn Schiffer, and others.

Questions:

1. How do we see and understand the complex the invisible infrastructure for the internet?
2. How do we build better networks that can serve those who do underserved?
3. How do we work to make the internet free and accessible to all?

Ideas:

1. Technology exists to solve human problems so it requires an ethical perspective that cares about identity.
2. Learning about technology holistically is essential.
3. There is little accountability in the technology industry.
4. The networks that enable the internet was developed by small, homogenous group of people.
5. Networks is a force multiple for good and bad.
6. The networks are governed by law and politics and requires a social contract.
7. While the internet is digital it requires massive amount of physical space, energy and infrastructure.

Resources:

• Where the Clouds Rises From the Sea by Ingrid Burrington
• Networks Land by Ingrid Burrington and Surya Mattu
• Documentary on Detroit’s Grassroots Internet Network by Motherboard Vice

This is part of a series of documents meant support a discussion and investigation of ethics and morals in relation to the impact of computer science on the world at-large. You can find the rest of series here:

• #ethicalCS
• Abstraction
• Algorithm
• Programming
• Networks

Introduction: In this document we will engage with the CS concept of Data which

Computers can be used to collect, store and analyze massive amounts of data quickly and reliably. Computer programs can use data to make decisions or to automate tasks. (CSforAll NYC Blueprint).

This document is generated from the #ethicalCS Twitter chat. You can find the highlights from the chat on Data here with Robyn Caplan, Surya Mattu and others.

Questions:

1. How do we use and protect users data within tools we build?
2. How we see the humans that lie behind the data?
3. How do we see all the data snooping that is happening on the internet?
4. How do we use data thoughtfully to see the world more clearly?

Ideas:

1. Ethics can be thought of how a group or institution looks at a data to understand a problem.
2. Harm can come from impassivity so ethics need to be active.
3. Many voices, experiences, and perspective are missing from the data which creates a bias.
4. Ethics and data can be a critique of existing power dynamics in our society.
5. Marginalized communities usually suffers the most when technology companies seek efficiencies.
6. Combining study of the humanities with the study of technology can be helpful.
7. If a tool is free then data is the product.

Resources:

• When J. M. Coetzee Secretly Programmed to Write Poetry in the 1960s by Colin Marshall
• Big Data in Education by Ben Williamson
• Networks Land by Ingrid Burrington and Surya Mattu