Scheduling meetings, sending follow-ups, tracking conversation threads across both Outlook and Teams… Sellers are spending too much of their time on tedious, low-value tasks.
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Check out this article to see what this can look like!
What would your day look like if you had a digital assistant helping you with the tedious tasks you manage every day as a sales professional? Offload the busy work of drafting emails, summarizing communications, and updating materials so that you can stay focused on building strong relationships with customers.
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As an HR professional, you should be spending your time getting to know candidates, not mired in admin work or buried in data.
When you bring Copilot for Microsoft 365 into the productivity apps you use every day, you have a virtual teammate who can take on your most time-consuming tasks. As a Microsoft Partner, we can assist your team in optimizing Copilot for a more efficient workday.
Generative AI has the potential to not only boost productivity but fundamentally shift the way we work – and HR teams have a unique opportunity to lead that change.
As a Microsoft Partner, we are here to provide HR teams with the support they need to embrace the adoption of Copilot for Microsoft 365 and implement lasting, transformational change. Check out these insights on how HR can help their organizations embrace the AI revolution.
The
Disparity Of Black Women In The AI Industry
The
AI industry, like many other tech sectors, faces significant challenges
regarding diversity and inclusion. Black women, in particular, are vastly
underrepresented, which has far-reaching implications for both the industry and
society at large.
Underrepresentation: Black women make up a
small fraction of the tech workforce. For instance, in the UK, Black women
constitute only 0.7% of those working in technology. In major tech
companies in the US, less than 2% of employees in technical roles are Black.
Bias in AI
Systems: We often assume that
machines are impartial, but they are not. My research has revealed significant
gender and racial biases in AI systems developed by major tech companies like
IBM, Microsoft, and Amazon. When tasked with identifying the gender of a face,
these systems performed much better on male faces than female faces. For
lighter-skinned men, the error rates were below 1%, while for darker-skinned
women, the error rates skyrocketed to 35%. AI systems from these leading
companies have even failed to correctly identify the faces of prominent figures
such as Oprah Winfrey, Michelle Obama, and Serena Williams. When technology
misidentifies even these iconic women, it is crucial to re-evaluate how these
systems are designed and who they are truly serving.
The underrepresentation of Black
women in technology is a multifaceted issue rooted in historical, social, and
economic barriers. Here are some key factors that have contributed to this
disparity:
Historical Barriers
Educational Inequities: Historically, Black women have faced significant barriers
to accessing quality education. Segregation and underfunded schools have
limited opportunities for many Black students, particularly in STEM fields. The
events of 2020 drew renewed attention to longstanding inequalities in the
invention and innovation ecosystem and Black Americans’ complex relationship
with technology. In the United States, Black men and women are less likely than
other demographics to earn STEM degrees, receive patents, or commercialize new
products and services. Black scientists and engineers experience unconscious
bias and outright discrimination in the high-tech employment sector, while
Black inventor-entrepreneurs face persistent difficulties in gaining access to
venture capital, intellectual property protection, and commercial networks.
With Black technologists largely absent from the invention process, supposedly
neutral apps and algorithms are encoded with racist assumptions that perpetuate
negative stereotypes and deepen social inequality. And yet, Black Americans
regularly invent, tweak, and deploy technology in the course of cultural and
political expression and develop new products and services with global reach.
Economic Disparities: Economic inequities have also played a crucial role. The
digital divide was first recognized in the mid-1990s.1 Three decades later, due
in part to long-standing economic inequity and the economics of broadband, it
remains an impediment to inclusive economic growth, particularly in Black
American communities. Approximately 40 percent of Black American households—as
opposed to 28 percent of White American households—don’t have high-speed, fixed
broadband. In dense urban areas such as
Chicago and Baltimore, Black households are twice as likely as their White
counterparts to lack a high-speed internet subscription. In the rural South, 38
percent of Black households don’t have broadband, compared with 23 percent of
White households.
Stereotypes and Bias: Persistent stereotypes, such as the “angry Black woman”
trope, and systemic biases have created hostile environments in both
educational and professional settings. Lower levels of digital readiness are
both a consequence and an ongoing driver of large gaps in Black American
representation in jobs that require digital skill sets. Although Black
Americans comprise approximately 13 percent of all workers, they make up only
7.4 percent of digital workers.
The stereotypes and
biases faced by Black women in AI technology are significant and multifaceted.
Here are some key aspects:
Racial and Gender Bias in AI Systems: Persistent stereotypes, such as the “angry Black woman”
trope, and systemic biases have created hostile environments in both
educational and professional settings. Lower levels of AI digital readiness are
both a consequence and an ongoing driver of large gaps in Black women
representation in jobs that require digital skill sets. For example, facial recognition technologies
have been shown to have higher error rates for darker-skinned individuals,
particularly women. This can lead to misidentification and exclusion from
various services and opportunities.
Lack of Representation and
Mentorship: The absence of Black women in AI tech
leadership roles means fewer role models and mentors for young Black girls
entering the field. This lack of representation can perpetuate feelings of
isolation and imposter syndrome. Many Black women face challenges in accessing
quality education in STEM fields. Schools in predominantly Black communities are often
underfunded, which limits access to advanced courses and resources necessary
for pursuing AI careers.
1.
Intersectionality
of Race and Gender: The unique challenges faced by
Black women arise from the intersection of racial and gender biases. This
intersectionality means they often experience compounded discrimination that
can hinder their career progression moving forward. Black women face unique challenges due to the
intersection of racial and gender biases. This means they can experience
compounded discrimination that is not simply the sum of racial and gender
biases but a unique form of bias that affects them differently.
Representation in AI Development: The lack of diversity in AI development teams can
exacerbate these biases. When AI systems are developed without considering
the diverse experiences of different groups, the resulting technologies can
perpetuate existing inequalities.
Conclusion
Diversity in AI is crucial because it ensures that the technology we develop
is inclusive, fair, and representative of all users. When diverse perspectives
are included in the creation of AI systems, it helps to mitigate biases and
prevents the perpetuation of existing inequalities.
Without diversity, AI systems can unintentionally reinforce stereotypes and
exclude marginalized groups, leading to unfair outcomes. For example, facial
recognition technology has been shown to have higher error rates for people
with darker skin tones due to a lack of diverse training data.
Moreover, a diverse workforce in AI fosters innovation and creativity, as
different viewpoints and experiences contribute to more robust and effective
solutions. By prioritizing diversity, we can build AI systems that better serve
society as a whole and drive positive social change.
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