December Changemaker – Dalip Singh Saund

This season we celebrate holidays from many different religions. The rich diversity of these beliefs adds valuable perspectives to our conversations and to our government. In honor of this diversity of cultures and religions, we honor the first Indian-American and first Sikh member of Congress, Dalip Singh Saund, as our December Changemaker.

Saung was born in 1899 in the Punjab province of India, and immigrated to the United States to attend graduate school at the University of California, Berkeley. There he studied farming (and mathematics) and stayed in the U.S. to become a farmer in southern California. He wrote in his autobiography “My guideposts were two of the most beloved men in history, Abraham Lincoln and Mahatma Gandhi” Like these idols, Saung advocated for equality and independence in speeches and in his writings. He was very active and involved in politics, campaigning for candidates and attending political meetings, but in 1923 the Supreme Court had ruled that ruled that immigrants from India were not eligible for U.S. citizenship, and so despite his passion for politics, Saund couldn’t even vote. He decided to change that.

Saund helped found the India Association of America and was elected its first president in 1942. Along with this new organization to gather the power of Indian Americans, Saund began his tireless work to secure the rights for Indian Americans and allow them to become citizens. He and other Indian Americans were able to convince Republican Clare Booth Luce and Democrat Emmanuel Celler to introduce a bill in Congress allowing citizenship to Indian Americans. But it was an uphill battle to get the bill passed. When a friend discouraged him from the battle, saying it was almost impossible that the United States would pass such a law, Saund responded “I have faith in the American sense of justice and fair play.”

Finally, after fighting racism (the original Supreme Court ruling was based on the idea that although Indians were considered “Caucasian,” they were not “white persons” and were therefore ineligible for citizenship) and discrimination for 4 years, Harry Truman signed the Luce-Celler Act into law in 1946.

Saund then became a naturalized citizen in 1949 and ran for  judge in Imperial County California. During the campaign, someone asked him in the middle of a restaurant Doc, tell us, if you’re elected, will you furnish the turbans or will we have to buy them ourselves in order to come into your court?” “My friend,” Saund responded, “you know me as a tolerant man. I don’t care what a man has on the top of his head. All I’m interested in is what he’s got inside.” On Election Day 1950, Saund won by 13 votes. In 1957, he then became the first Asian-American, first Indian-American, and first Sikh (or any non-Abrahamaic faith) to serve in Congress. Often just called “The Judge,” Saund staunchly supported the 1957 Civil Rights bill and other civil rights legislation, continuing his legacy of advocating for equal rights.

You can read Saund’s own words in the pamphlet “What America Means to Me”

And find out more about Congressman Saund with these resources:


Challenges Using Machine Learning Classification on the News

News isn’t as easy as circles and squares…

At Public Good, we provide tools that let people take action on the news. Whether it’s the refugee crisis, climate change, or a gang shooting, our goal is to allow people to help make the world better when they are motivated by good journalism. It’s not a completely new idea, but we revolutionized it by bringing machine learning to the party. In today’s overtaxed newsrooms, it’s not possible for reporters or editors to take on another responsibility. So for taking action to flourish, it needs to be automated and as simple to implement as a social media button.

For us, this begins with the problem of classifying news content. Until you know what a story is about, you can’t recommend actions. Unfortunately, the idea of a semantic web has failed us and there are no consistent tags or metadata to point in the right direction, and even if there were it’s likely the taxonomy used for navigating a news site (e.g. sports, entertainment, lifestyle, news) would differ from the taxonomy needed for taking action (e.g. violence, poverty, natural disasters) with no obvious mapping between them. So the most obvious first approach is to use machine learning classification algorithms. While we still use these methods as part of our overall system, we discovered they underperformed our expectations (how we expected them to perform on arbitrary textual data given our large training set and relatively small number of classes) and we’re beginning to understand why.

Most major classification algorithms make use of word frequency (or phrase frequency) as part of their analyses. When we first looked at the total number of unique words and phrases (high) this made us optimistic that these algorithms would perform well on our content. But a closer look reveals that the distribution of terms is highly imbalanced. A relatively small vocabulary of common words makes up the vast bulk of content (which we might have expected given that news content tends to be written for a wide audience with varying language skills and reading levels), while the big variety of words is substantially made up for proper nouns and other types of entities.

In retrospect, this isn’t surprising. News tends to be about people, places, and organizations. And unlike other kinds of textual data, these entities tend to enter the news vocabulary suddenly (e.g. “Hurricane Irma”) and often leave it just days or weeks later as the news cycle moves on. To a human brain, we often need only say a phrase like “Harvey Weinstein” to immediately register “sexual harassment”, but classifiers looking at months or years of historical data are proving less effective at making that determination — while the term is highly relevant, it’s only in a very small sample and, just weeks before it came to mean sexual harassment, it would have been a ringer for TV and movies.

While working to optimize our core classifiers, we’ve seen that some mitigation strategies can help a lot. First, online learning gets breaking news terms into the algorithms more quickly than batch training. Second, retiring old content from a training corpus as quickly as possible reduces the chance that a term that has become meaningful in breaking news will be associated with a previous meaning. And third, tweaking tolerances for stop terms makes a lot of difference for the bulk of language.

Most importantly, we learned that classifying the news accurately is a lot more complicated than setting up an off-the-shelf classifier and feeding it a bunch of data. Operational optimizations can help, but to be accurate on breaking news, ensemble ML methods and a breaking news team are critical.

Unilever launches Right to Dignity on Public Good

We’re proud partners of Unilever in their effort to bring dignity and hope to our homeless neighbors by raising awareness around the important role hygiene plays in the opportunities afforded them.

According to the U.S. Department of Housing and Urban Development, homelessness is on the rise for the first time since 2010 with more than 554,000 people experiencing homelessness in the United States each day. Oftentimes, when we think of the homeless population, shelter and food are the most urgent needs to come to mind, but we know that a lack of access to basic hygiene practices – like the ability to take a shower – can have an immense impact on a person.

Today Unilever is launching a Public Good campaign to both raise awareness about how many Americans experience homelessness and help raise money and locate volunteers for Lava Mae, Streetside Showers, Project Outpoor, Brooklyn Community Services, and Hope on Wheels; all nonprofits working to make a difference in this area.


What story would Charles Dickens tell this Christmas if he visited New York City in Trump’s America?

It is 176 years since Charles Dickens, the journalist, social reformer and novelist, was horrified by the depth of poverty when he came to visit the Five Points Section, of “Gangs of New York” fame, in lower Manhattan.

‘Sesame Street’ introduces homeless muppet

For the first time, a resident of “Sesame Street” is experiencing homelessness — and the hope is that her story can help sweep the clouds away for the growing number of young children in the United States without homes to call their own. Lily, a 7-year-old bright pink Muppet, was introduced to the world in 2011.