How Diversity is good for creating Value but also great for the Bottom-line

Photo by Womanizer Toys on Unsplash

Let’s start with definitions — Diversity refers to the range of differences and similarities among individuals in a group or organization. This includes differences in characteristics such as race, ethnicity, gender, sexual orientation, age, religion, ability, and more. Diversity also extends to differences in experiences, perspectives, and backgrounds that can lead to a variety of perspectives and approaches to problem-solving.

It is also well known that a diverse workforce can bring a variety of perspectives and approaches to value creation, leading to more innovative and effective solutions. Additionally, a diverse and inclusive workplace can also create value by improving employee engagement, productivity, and satisfaction, leading to a more successful and profitable organization.

There are many examples of companies who have created products without input from women or people of color. This is obviously bad for business while it’s also unethical. Women alone, for example, control about $20 trillion in consumer spending. The most interesting part? Women report that they feel majorly underserved. This means there is a huge opportunity for companies who make diversity in tech a focus.

When a company has a diverse workforce, they can understand their customers better. Consumers today have higher expectations of products and services that meet their specific, and diverse, needs and preferences. At the same time, employees have higher expectations for workplaces that are inclusive of their needs and value the diverse perspectives, skills, and experiences they bring to the table.

The world itself is overflowing with diversity. Without diversity in tech, our tech-based world can’t tap into the full range and richness of that diversity.

“The two main sources of AI bias are similar to those behind human cognitive bias: bias in the inputs … and bias in the methodology of looking at the data … As economists might obtain different results depending on their methodological preferences when looking at the same or different data, robots will also obtain different results depending on the literature or information they are fed and the models based on which they are supposed to look at … This bias can be managed or limited, but it cannot be avoided completely.”

Unfortunately, currently only 20% of data scientists in the US are female, and 5% are Black, according to studies. Data science, the underpinning of AI and ML can also be better gender or ethnically diverse. As artificial intelligence (AI) is quickly getting more robust and gaining capabilities, everyone is looking more closely at why it’s important to have diverse representation — both in the data that is fed into these algorithms, and in the teams of people who work on them. Having a more diverse workforce means diverse opinions, backgrounds, and perspectives. With a team of diverse employees, you will have access to more creativity and wider skill sets. Plus, more diverse ideas for solving business problems, which will help your company grow. Teams creating AI need to be diverse to deliver unbiased and empowering algorithms, products and services.

A report by the AI Now Institute of New York University (profiled in Forbes by Maria Klawe) found that 80% of AI professors are men and only 15% of Facebook researchers, and 10% of Google researchers, are women. The same research found that less than 25% of PhDs in 2018 were awarded to women or minorities.

Jim Boerkoel, a grant applicant interviewed in the piece, talks about a lack of diverse thought and how that impacts AI:

“One of the challenges is that when there’s a lack of diversity, there’s a lack of diverse thought,” Boerkoel says. “If the population that is creating the technology is homogeneous, we’re going to get technology that is designed by and works well for that specific population. Even if they have good intentions of serving everyone, their innate biases drive them to design toward what they are most familiar with. As we write algorithms, our biases inherently show up in the decisions we make about how to design the algorithm or what and how data sets are used, and then these biases can get reified in the technology that we produce.”

Great examples where diversity made a difference?

1. Microsoft Xbox: The Xbox gaming console was developed by a diverse team of engineers, designers, and marketers from various backgrounds and countries. The team’s diversity allowed them to bring a range of perspectives and ideas to the project, leading to the creation of a successful product that appealed to a wide audience.
2. Airbnb: Airbnb was founded by a diverse team of individuals from different backgrounds, including technology, design, and hospitality. This diversity of perspectives and experiences helped the team understand the needs of both travelers and hosts, leading to the creation of a successful platform that has transformed the travel industry.

So, does diversity makes a difference in a company’s bottom-line?

Sure, one example of the value of diverse teams can be seen in a study conducted by McKinsey & Company. In their report, “Diversity Matters,” they found that companies in the top quartile for ethnic and racial diversity are 33% more likely to have higher-than-average financial returns compared to companies in the bottom quartile.

Additionally, companies in the top quartile for gender diversity are 21% more likely to have above average profitability compared to companies in the bottom quartile. These findings were based on data from 366 public companies across eight countries and industries.

Another study, conducted by the Center for Talent Innovation, found that companies with more diverse leadership teams had 19% higher revenue due to innovation. They also found that diverse teams are better at problem-solving and decision-making, as well as attracting top talent from a wider pool.

In conclusion, these studies show that diversity in teams can lead to better financial performance and increased revenue through improved problem-solving, decision-making, and innovation.

So, how do you cultivate retention and a great culture for a trajectory for your team?

1. Recruitment: To build a diverse pool of candidates, it’s important to have a recruitment process that reaches out to underrepresented groups in the AI and ML fields. This can be done through partnerships with organizations that focus on diversity and inclusion, attending events and conferences targeting diverse communities, and utilizing job boards and websites that cater to underrepresented groups. Additionally, the job description and language used in job postings should be inclusive and not include biased language.
2. Screening: The next step is to screen candidates fairly and objectively. This can be done by using structured interviews that assess skills and experience relevant to the job, and by using unbiased evaluation criteria. It’s also important to eliminate any unconscious biases in the selection process by having multiple people involved in the decision-making process and ensuring that diverse perspectives are represented.
3. Onboarding: Once candidates are selected, it’s important to have a robust onboarding process that helps new hires feel welcome, supported, and integrated into the team. This includes providing clear expectations and setting them up for success, as well as ensuring that they have access to resources and support to succeed in their role.
4. Cultivating a great culture: Creating a positive and inclusive culture is key to retaining diverse talent. This can be done by fostering open communication, promoting a sense of belonging and respect, and providing opportunities for professional development and growth. It’s also important to address any issues of discrimination or bias when they arise and to have policies in place to support diversity and inclusion.
5. Retention: To retain diverse talent, it’s important to regularly check in with team members to ensure they feel supported and valued, and to provide opportunities for professional growth and advancement. Providing flexible work arrangements and a supportive work-life balance can also help retain diverse talent.

Photo by Alexander Grey on Unsplash

Diversity and inclusion are important in all industries, including data science, as it leads to better decision-making, more creative solutions, and improved overall performance. Here are some steps that organizations can take to ensure diversity in data science teams:

1. Develop a Diversity and Inclusion (D&I) Strategy: This should include specific goals and metrics to measure progress, as well as a plan for how to achieve those goals. The strategy should be reviewed regularly to ensure it is having the desired impact.
2. Recruitment and Hiring: Organizations should make a conscious effort to reach out to and attract diverse candidates. This can be achieved through partnerships with organizations that support underrepresented groups in technology and using inclusive language in job advertisements.
3. Employee Development and Advancement: Provide opportunities for professional development and career advancement for all employees, including those from underrepresented groups. This can include training programs, mentorship, and sponsorship opportunities.
4. Foster an Inclusive Work Environment: Create a culture that values diversity and inclusiveness. This can be achieved through regular communication and training, encouraging open and honest discussions, and actively addressing incidents of bias or discrimination.
5. Monitor and Evaluate Progress: Regularly track and evaluate the diversity of the data science team and the organization as a whole, as well as the impact of D&I initiatives. Use this information to adjust strategies as needed and celebrate successes.

It is important to note that achieving diversity and inclusion in data science teams is an ongoing process and requires continuous effort and commitment from everyone in the organization. It’s a journey and we’re all part of it, doing our share, advocating, coaching and creating awareness and allies for representation in data science-

Happy women’s history month!