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In this post: Rich Jones, who works in people operations at Google, is the host of a personal finance podcast called "Paychecks & Balances." He hopes his show can help people learn from his mistakes — and now he's helping others start podcasts, too.

Several years ago, Rich Jones was on the hunt for personal finance podcasts. But none were right for him. “It felt like every podcast that I listened to either made me feel dumb, or made me feel like I was being lectured by an old white guy in a suit,” he says. “Or it just was really boring.” So he decided to create his own. 

These days, his podcast, “Paychecks & Balances,” has been downloaded more than two million times and recently won an award from the Plutus Foundation, which highlights excellence in financial media. He often records episodes from his Mountain View home in the early-morning hours, then logs on for his job working in People Operations at Google. 

For years, Rich has turned to the internet to express himself. But even though his name is, well, Rich, he didn’t first think of money as a topic to talk about. In fact, he had first blogged about relationships for several years, and then co-hosted a podcast called “2 Guys, 1 Show,” that was about more general topics, including money. 

Rich realized that if he felt lectured by finance podcasters, other people like him — and possibly younger people learning about money for the first time — likely felt the same way. So he and his co-host decided to focus on finances and rename the podcast “Paychecks & Balances.”  They wanted to reach out to younger versions of themselves — and Rich also wanted to represent people like him as well as reach them. “Even now, you won’t find a whole lot of Black men in the personal finance space in particular,” he says. “I think it’s important to be out there as a Black male and show a perspective that you might not be getting elsewhere.” 

For the current season of the show, Rich is hosting the show solo, and he’s continuing to share his own financial progress while also teaching others. When he started the show, he was grappling with credit-card debt after treating his cards like “free money.” Because of his experience, he knows to talk about money in a way that’s relatable and simple, for people just starting to manage their finances. “I don’t call myself an expert,” he says. “Podcasting is a medium for me to talk about my experience. And not just my successes, but the mistakes I’ve made along the way as well.”

Rich is constantly surprised that he keeps getting the same questions over and over — like how to balance a budget, or why not to sign up for a credit card in exchange for a free T-shirt. And over the past year, he’s seen friends fall prey to get-rich-quick scams and even try to sign him up. Rich says this is a symptom of a lack of financial education. “The interesting problem to me is: How do we close that gap where this information feels accessible to everyone, and people are accessing that information a lot sooner?” he says. 

With the growth of his podcast, Rich says people have come to him asking for advice on starting their own podcasts. So this month he launched a YouTube page, The Show Starter, which breaks down advice for people who might not have a technical audio background. “It’ll be a combination of tutorials, reviews and some motivational content, but not the cheesy, corny kind,” he says. “It’s very similar to the approach I take with personal finance topics, where I try to simplify things as much as possible and take out the jargon.” He hopes to one day expand his work into a multimedia company, with multiple brands under the “Paychecks & Balances” umbrella. 

Rich says both his podcast and his YouTube channel have the same goal: helping others. “While the podcast is about money, for me this has never been about the money,” Rich says. “I love seeing people achieve freedom in their lives, whatever that means for them. I think continuing to focus on that is what has kept people along for the ride.”

1. HBCU Tech Advisory Board:The board will be involved in shaping “Pathways to Tech” efforts and will expand to include additional corporations in the future. 

2. HBCU Presidents’ Council: Dr. Michael Lomax of UNCF and Dr. Harry Williams of TMCF will lead an HBCU Presidents’ Council, which will advise the board and ensure that we’re creating and executing meaningful programming that meets the needs of HBCU students.

3. Joint Steering Committee: To set goals and drive this work forward, I will sit on a steering committee alongside Dr. Kamau Bobb, Global Lead, Diversity Strategy and Research at Google; Maria Medrano, Senior Director, Diversity Strategy at Google; Eric Hart, Chief Programs Officer at Thurgood Marshall College Fund; Chad Womack, Senior Director of STEM Programs and Initiatives; Angela Van Croft, Director, Corporations and Foundations at United Negro College Fund; and Alycia Onowho, Program Manager at Howard University.

4. Internal Advisory Committee:I will lead an HBCU Advisory Committee that consists of senior vice presidents across Google, including product leaders and executives across Talent Acquisition, Grow with Google, Google.org and Engineering Education, to organize our efforts across the company. 

As we deepen our work together, here’s a look at some of the areas we’re focused on.

Helping to build equity for HBCU computing education 

We’ll continue to invest in programs that help students develop skills and immerse themselves in tech, and help universities and faculty establish the infrastructure and tools they need to support these students. Our ultimate goal is to ensure that when HBCU students graduate, they’ll have the skills they need to succeed in tech. 

This year, our Tech Exchange program will host 114 computer science majors, providing them with the opportunity to immerse themselves in coding classes at Google. This first-of-its-kind program is now in its fourth year, and we’ve continued to update, broaden and improve the program over the years. Through our Google in Residence program, which sends experienced Google Software Engineers to HBCU campuses for a semester to teach introductory computer science classes, we’ve reached more than 4,000 students. Through this initiative, students gain practical knowledge about what it’s like to work in the tech industry. 

Our Faculty in Residence program is an immersive professional development program that brings CS faculty from HBCUs and HSIs to Google for a four week summer residency, where they design project-based, industry-informed content and implement that content back in their classrooms.

Since 2017, we’ve invited more than 50 faculty members from 30 HBCUs to join the program.

Helping students find jobs in tech

Creating a workplace where everyone belongs 

For students who choose to pursue a career at Google, we’re also accelerating efforts to ensure every Googler — and in particular Black students and those from other underrepresented groups — experience Google as an inclusive workplace and have the opportunity to accelerate their careers. 

We have a responsibility to help provide access and opportunities for underrepresented talent to join the tech industry. Many of the initiatives we’re working on are the first of their kind in our industry. It’s so important that we keep this momentum going.

Editor’s note: Welcome to A Matter of Impact, Google.org’s monthly digest, where we highlight what the team’s been up to and spotlight some of the incredible nonprofits and Google.org Fellows helping solve some of society’s biggest challenges through technology and innovation. 

It didn’t take long for the effects of COVID-19 to reveal a devastating, but predictable, truth: the pandemic has had an outsized impact on marginalized groups, especially people of color. At Google.org, we aim to bring the best of Google to support underserved communities. So when we made a $100 million grant and 50,000 pro bono hour commitment to support COVID-19 relief, we focused our efforts on addressing the compounding racial and social inequities of this crisis. 

As we join forces to fight this pandemic, we must put equity at the center of our response and lift up our most vulnerable communities. Here you’ll find updates about our work that’s at the intersection of COVID-19 relief and equity and two themes that remain top priorities for us.

Equitable distribution of vaccines and health information

Data shows that COVID-19 affects people of color at much higher rates: about 71% percent of Black Americans and 61% of Hispanic Americans know someone who has died or been hospitalized from the virus compared to 48% of white Americans. Yet data also shows that Black Americans are getting vaccinated at lower rates than their peers. That’s why we have a team of Google.org Fellows working full-time with the Satcher Health Leadership Institute at Morehouse School of Medicine to help create a Health Equity Tracker to map and contextualize COVID-19 health disparities in communities of color throughout the U.S. We’re also committing $5 million in grants to organizations addressing racial and geographic disparities in COVID-19 vaccinations.

Support for minority-owned small businesses

Turning to the economy, reports have shown that 41% of Black-owned businesses — about 440,000 businesses — have shuttered due to COVID-19 compared to 17% of businesses owned by white people. To support minority business owners through the pandemic, we’ve supported Common Future with grant funding to provide capital and technical assistance to 2,000 women and minority small-business entrepreneurs in the U.S. We’ll also provide opportunities for Google volunteers to assist them with skill-based coaching and mentoring. 

Read the rest of our Google.org updates below.

In case you missed it 

Hear from one of our grantees: Common Future

Rodney Foxworth is the CEO of Common Future, a network of leaders helping to build an economy that includes everyone. Last spring, Common Future received a $5 million Google.org grant to provide capital and technical assistance to women and minority small business entrepreneurs in the U.S. 

A few words with a Google.org Fellow: Colin Jackson

Colin Jackson is a product manager who recently completed a Google.org Fellowship with Satcher Health Leadership Institute (SHLI) at Morehouse School of Medicine. 

Radio reaches more Americans every week than any other platform. Public radio stations in the United States have over 3,000 local journalists and each day they create audio news reports about the communities they serve. But news audio is in a similar place as newspaper articles were in the 1990s: hard to find, and difficult to sort by topic, source, relevance or recency. News audio can not delay in improving its discoverability. 

KQED is the most listened to public radio station in the United States, and one of the largest news organizations in the Bay Area. In partnership with Google, KQED and KUNGFU.AI, an AI services provider and leader in applied machine learning, ran a series of tests on KQED’s audio to determine how we might reduce the errors and time to publish our news audio transcripts, and ultimately, make radio news audio more findable. 

“One of the pillars of the Google New Initiative is incubating new approaches to difficult problems,” said David Stoller, Partner Lead for News & Publishing at Google “Once complete, this technology and associated best practices will be openly shared, greatly expanding the anticipated impact.” 

What makes finding audio so much harder?

In order for news audio to be searched or sorted, the speech must first be converted to text.  This added step is trickier than it seems, and currently puts news audio at a disadvantage for being found quickly and accurately. Transcription takes time, effort and bandwidth from newsrooms — not something that is in abundance these days. Even though there have been great advances in speech to text, when it comes to news, the bar for accuracy is very high. As someone who works to make KQED’s reporting widely available, it is frustrating when KQED’s audio isn’t prominent in search engines and news aggregators.

The challenge of correctly identifying who, what and where

For our tests, KQED and KUNGFU.AI, applied the latest speech-to-text tools to a collection of KQED’s news audio. News stories try to address the “five Ws:” who, what, when, where and why. Unfortunately, because AI typically lacks the context in which the speech was made (i.e. identity of the speaker, location of the story), one of the most difficult challenges of automated speech-to-text is correctly identifying these types of proper nouns, known as named entities. KQED’s local news audio is rich in references of named entities related to topics, people, places, and organizations that are contextual to the Bay Area region. Speakers use acronyms like “CHP” for California Highway Patrol and “the Peninsula” for the area spanning San Francisco to San Jose. These are more difficult for artificial intelligence to identify.

When named entities aren’t understood, machine learning models make their best estimation of what was said. For example, in our test, “The Asia Foundation” was incorrectly transcribed as “age of Foundations” and “misgendered” was incorrectly transcribed as “Miss Gendered.”  For news publishers, these are not just transcription errors, but editorial problems that change the meaning of a topic and can cause embarrassment for the news outlet. This means people have to go in and correct these transcriptions, which is expensive to do for every audio segment. Without transcriptions, search engines can't find these stories, limiting the amount of quality local news people can find online.

A machine learning ↔ human ↔ machine learning feedback loop

At KQED, our editors can correct common machine learning errors in our transcripts. But right now, the machine learning model isn’t learning from its mistakes. We’re beginning to test out a feedback loop in which newsrooms could identify common transcription errors to improve the machine learning model. 

We're confident that in the near future, improvements into these speech-to-text models will help convert audio to text faster, ultimately helping people find audio news more effectively. 

Every year, approximately 40 million women undergo breast-cancer screening in the U.S. using a procedure called mammography. For some, this can be a nerve-wracking experience; many wait days or weeks before a radiologist can review their scan and provide initial screening results. Between 10 and 15 percent of women must return for a second visit and undergo more scans before receiving a final diagnostic assessment – drawing out the process further. 

Together with Northwestern Medicine, Google Health is working on a new clinical research study to explore whether artificial intelligence (AI) models can help reduce the time to diagnosis, narrowing the assessment gap and improving the patient experience. 

Women who choose to take part in the study may have their mammograms reviewed by an investigational AI model that flags scans for immediate review by a radiologist if they show a higher likelihood of breast cancer. If a radiologist determines that further imaging is required, the woman will have the option to undergo this imaging on the same day. This study will evaluate whether this prioritization could reduce the amount of time that women spend waiting for a diagnostic assessment.  Women whose mammograms are not flagged will continue to have their images reviewed within regular timeframes. 

“Through this study, Northwestern Medicine aims to improve the excellent care we deliver to our patients every day. With the use of artificial intelligence, we hope to expedite the process to diagnosis of breast cancer by identifying suspicious findings on patients’ screening examinations earlier than the standard of care,” says study principal investigator Dr. Sarah Friedewald, chief of breast imaging at Northwestern Medicine and vice chair for women's imaging in radiology at Northwestern University’s Feinberg School of Medicine. “Every patient in the study will continue to have their mammograms interpreted by a radiologist, but the artificial intelligence will flag and prioritize patients that need additional imaging, facilitating the flow of care.”

This research study with Northwestern Medicine builds on previous research which demonstrated the potential of AI models to analyze de-identified retrospectively collected screening mammograms with similar or better accuracy than clinicians. 

Artificial intelligence has shown great potential to improve health care outcomes; the next challenge is to demonstrate how AI can be applied in the real-world. At Google Health, we’re committed to working with clinicians, patients and others to harness advances in research and ultimately bring about better and more accessible care.