Part 2: Digital technologies and social inclusion (Research Frontiers)

Summary

The script discusses the gendered history of the computing industry and its impact on inequality. It highlights the economic benefits of technological jobs, especially for marginalized groups, and the potential of crowd work for women's economic emancipation. However, it also points out the exploitation and discrimination in gig and crowd work. The script emphasizes the societal consequences of gender inequality in knowledge production, such as biased algorithms affecting health outcomes, and the importance of diversity in teams for innovation and better outcomes in software development. It concludes by stressing that technology is not neutral and that technologists have a responsibility to design inclusive digital systems.

Takeaways

• 💼 The computing industry has historically been gendered, shifting from a female-dominated field to a male-dominated one as it became more prestigious and lucrative.
• 🌐 The script emphasizes the importance of striving for gender equality in technology not just for ethical reasons, but also for its economic and societal benefits.
• 💡 Technological jobs and technology-mediated jobs can provide opportunities for marginalized groups, including women, to enter the workforce through flexible work arrangements like crowd work.
• 🔄 However, crowd work can also lead to exploitation and discrimination against gig and crowd workers, particularly on the basis of gender or age.
• 📊 A Eurostat survey highlighted a shortage in recruitment for ICT professionals, indicating that gender balance in the IT sector could help fill more jobs and contribute to economic growth.
• 🧐 Gender inequality in technology leads to unequal knowledge production, which can reinforce biases and exacerbate the digital divide.
• 🤖 Biases in data collection and algorithm design can have serious consequences, such as misdiagnosing health conditions based on gender, as illustrated by the Babylon app example.
• 🔍 The script calls for a more inclusive approach to technology design that considers a variety of users and their needs to avoid perpetuating stereotypes and biases.
• 👥 Diversity in teams leads to better and more innovative outcomes, improving software quality and development productivity.
• 🌟 Inclusion not only enhances team flexibility and adaptability but also increases organizational economic returns by avoiding groupthink and defensive behaviors.
• 🌱 The script concludes by highlighting that technology is not neutral and that the way technologists design and participate in the digital economy has far-reaching consequences at individual, organizational, and societal levels.

Q & A

How has the computing industry changed in terms of gender representation historically?

Historically, the computing industry was a low-paying sector that employed women, but it has structurally changed to become a high-value sector dominated by men.

Why is striving for gender equality in technology important beyond just a moral imperative?

Gender equality in technology is important for its economic prospects, societal effects, and the potential to reduce biases in knowledge production and digital technologies.

What is crowd work and how does it offer flexibility to marginalized individuals?

Crowd work refers to work done through a crowdsourcing platform that matches workers with tasks required by organizations. It offers flexibility in terms of when and how to work, which is crucial for women and marginalized individuals to enter the labor market.

How can crowd work potentially help women become economically emancipated?

Crowd work can help women get better jobs and become economically emancipated by providing flexible work opportunities that allow them to participate in the labor market from home.

What are some of the issues faced by gig and crowd workers that may hinder their economic emancipation?

Gig and crowd workers may face exploitation or discrimination based on gender or age, which can hinder their economic emancipation despite the flexibility offered by such work.

Why is gender inequality in the IT sector problematic for economies?

Gender inequality in the IT sector is problematic because it leads to a shortage in recruitment, with many firms facing difficulties in filling ICT professional positions, resulting in unfilled jobs and economic inefficiency.

How does inequality in knowledge production reinforce biases and affect society?

Inequality in knowledge production can reinforce biases, leading to unequal outcomes and a digital divide. It affects who produces knowledge and what kind of worldviews are encapsulated by that knowledge, potentially leading to biased and harmful digital technologies.

Can you provide an example of how biased data can have real-world consequences in health information systems?

An example is the UK National Health Services Babylon app, which used AI to advise patients but was found to give incorrect recommendations to women due to biased data, leading to misdiagnoses and potentially life-threatening consequences.

What is the importance of diversity in teams for innovation and productivity in software development?

Diversity in teams leads to better and more innovative outcomes, as diverse perspectives can improve software quality and development productivity, making teams more flexible and open to change.

How can digital systems amplify and reproduce gender inequality and stereotypes?

Digital systems can amplify and reproduce gender inequality and stereotypes by using biased algorithms and data, which can lead to unequal consequences for different genders, particularly affecting the well-being and health of people.

What is an intersectional approach and why is it important in addressing digital health issues?

An intersectional approach considers multiple aspects of identity, such as gender, race, and class, to understand and address issues comprehensively. It is important in digital health to ensure that technologies are inclusive and do not perpetuate biases or discrimination.

Outlines

00:00

💻 Economic and Social Impacts of Technological Inequality

This paragraph discusses the importance of striving for gender equality in the computing industry, not just as a moral imperative, but for its economic and societal benefits. It highlights how technological jobs can provide opportunities for marginalized individuals, particularly through flexible crowd work, which can empower women economically. However, it also points out the exploitation and discrimination that can occur in gig and crowd work environments. The paragraph emphasizes the negative economic consequences of gender inequality, such as a shortage in recruitment of ICT professionals, and the broader implications of unequal knowledge production, which can reinforce biases and exacerbate the digital divide. It provides an example of how biased data in health information systems can lead to incorrect medical advice, affecting women and minorities disproportionately, and calls for a more inclusive approach in technology design to avoid these issues.

05:01

🌟 The Power of Diversity and Inclusion in Innovation

The second paragraph explores the advantages of diversity in teams, suggesting that it leads to better and more innovative outcomes for both teams and organizations. It argues that inclusive environments can improve software quality and development productivity by fostering flexibility and openness to change. The paragraph also touches on the broader impact of digital systems, which can amplify and perpetuate stereotypes, affecting the well-being and health of individuals. It concludes by emphasizing that technology is not neutral and that the design and participation of technologists in the digital economy have significant individual, organizational, and societal consequences. The paragraph sets the stage for the next part, which will introduce ideas to counteract exclusion and promote equality in the tech industry.

Keywords

💡Gendered

The term 'gendered' refers to the social and cultural construction of gender roles and expectations, which can influence various aspects of life, including the workplace. In the context of the video, the computing industry has been described as 'gendered', implying that it has historically been structured in a way that affected the participation and roles of men and women differently. The script discusses how this industry shifted from being a sector that employed women to one dominated by men as it became more prestigious.

💡Equality

Equality, in this video, is the state of being equal in rights, opportunities, and treatment. It is a central theme because the video argues that striving for equality in technology is not just a moral imperative but also has practical benefits for society and the economy. The script emphasizes the importance of gender equality in the tech industry to avoid perpetuating biases and to promote a more inclusive and innovative work environment.

💡Economic Prospects

Economic prospects refer to the potential for economic growth or improvement. The script mentions that technological jobs, including those mediated by technology, can have positive societal effects by providing employment opportunities, especially for marginalized groups. The video suggests that the flexibility of crowd work can help women enter the labor market, which is crucial for improving economic prospects.

💡Crowd Work

Crowd work is a type of employment where tasks are distributed through a platform that matches workers with jobs posted by organizations. The script highlights the flexibility of crowd work, which allows individuals, particularly women who may have been historically excluded, to participate in the labor market from home. However, it also warns of the potential exploitation and discrimination within this type of work.

💡Digital Divide

The digital divide refers to the gap between those who have access to modern information and communication technology and those who lack it. The video script discusses how inequality in access to technology and knowledge production can reinforce biases and exacerbate this divide. It implies that addressing the digital divide is essential for promoting equality and inclusive knowledge production.

💡Biases

Biases in the context of the video refer to the systematic errors or prejudices that can affect decision-making, particularly in algorithms and data collection. The script warns that biases can be embedded in digital systems, leading to unfair outcomes for certain groups, such as women or minority populations. An example given is the Babylon app, which showed biased data affecting health recommendations for women.

💡Inclusive Knowledge Production

Inclusive knowledge production means creating knowledge that takes into account diverse perspectives and experiences. The video argues that inequality can lead to unequal and biased knowledge production, which is problematic because it may not represent or serve the needs of all people. The script suggests that a more inclusive approach to technology design can help avoid reinforcing stereotypes and biases.

💡Predictive Policing

Predictive policing is a method of law enforcement that uses data analysis to predict where and when crimes are likely to occur. The script mentions predictive policing as an example of how algorithms can perpetuate discrimination, particularly against black people, due to biases in the data and assumptions used in these systems.

💡Diversity in Teams

Diversity in teams refers to the presence of individuals with different backgrounds, experiences, and perspectives within a group. The video script suggests that diverse teams can lead to better and more innovative outcomes. It argues that inclusion not only improves the quality of work but also increases organizational economic returns by fostering a more open and adaptable environment.

💡Digital Health

Digital health encompasses the use of digital technologies in healthcare, such as electronic health records, telemedicine, and health apps. The script uses the example of the Babylon app to illustrate how digital health systems can perpetuate gender biases if not designed with an intersectional approach, potentially leading to incorrect health advice and outcomes.

💡Algorithms

Algorithms are a set of rules or processes used in computing to solve problems or perform tasks. The video script discusses how algorithms used in decision-making can be biased if they are trained on biased data, leading to unfair or discriminatory outcomes. It emphasizes the importance of awareness and careful design to prevent algorithms from amplifying existing inequalities.

Highlights

The computing industry has historically been gendered, shifting from a female-dominated sector to one dominated by men as it became more prestigious.

Equality in technology is important not just as a moral imperative, but also for its economic and societal benefits.

Technological jobs and technology-mediated jobs can provide opportunities for marginalized individuals, particularly women, to enter the workforce.

Crowd work offers flexibility, which is a significant factor for women's participation in the labor market.

Despite the potential of crowd work, studies show that gig and crowd workers face exploitation and discrimination based on gender or age.

Gender inequality in the IT sector leads to a shortage in recruitment and a loss of potential talent.

Inequality in knowledge production can reinforce biases and contribute to the digital divide.

Unequal data collection can lead to biased health information systems with potentially devastating consequences for certain demographics.

An example of biased data is the UK National Health Service's Babylon app, which misdiagnosed a female smoker's heart attack symptoms as a panic attack.

Data collection often overlooks the diversity of users, leading to incomplete and biased algorithms.

Inclusive design in technology can lead to more person-centered approaches and better adaptation to diverse user needs.

Biases in algorithms can result in discriminatory practices, such as predictive policing and risk assessment scores.

A feminist approach to women's health, as suggested by a 2023 Lancet report, could save 800,000 lives by addressing biases in healthcare.

Digital systems can amplify and scale gender inequality, cementing and reproducing stereotypes with wide-ranging impacts.

Diversity in teams has been shown to lead to better and more innovative outcomes in software development and organizational performance.

Inclusion increases organizational economic returns by reducing defensive avoidance behavior and promoting adaptability to change.

Technologists have a responsibility to consider the consequences of their designs and participation in the digital economy at individual, organizational, and societal levels.

The history of computing has been gendered from the beginning, with exclusion and inequality having negative consequences that need to be addressed.

Transcripts

the previous part we have looked at how

the Computing industry has been gendered

structurally changing from a low

rewarding sector that employed women to

one dominated by men when it became seen

as providing for valuable careers in

this part we will look at the reason why

you should care about equality as a

technologist obviously equality is

something that we should strive for in

and for itself but it remains important

to understand the consequences of having

such

inequality the first reason is economic

prospects there are clear individual ual

but also societal effects from

technological jobs or jobs mediated by

technology technology mediated jobs can

help to get more people to work

particularly those suffering from

marginalization there were a lot of

hopes placed on digital Technologies and

in particular on crowd work which refers

to work done in a crowdsourcing way In

Crowd workor a platform matches a worker

with tasks that are required by an

organization that kind of work is

flexible allowing women who have

historically been deprived red of

entering the labor market to join it

from home so crowd work gives you a lot

of flexibility when and how to work and

this is identified by economists as one

of the most important factors for women

to enter the labor market as Churchill

and Lynn argue crowd workk can help

women get better jobs and become

economically

emancipated the key word here of course

is can because there are very good

studies that show that gig and crowd

workers are exploited or discriminated

against in different ways for example on

gender or age basis we've looked at how

it mediator jobs can improve

individual's lives but it is also

important for societies gender

inequality is really problematic for

economies since there is a shortage in

recruitment a survey by eurostat

indicated that 41% of firms had

difficulties recruiting ICT

professionals balancing the it sector

would mean more jobs being filled and IT

jobs in general tend to be good and well

paid exclusion and inequality cause an

issue we need to avoid inequality

generates unequal knowledge production

which has negative

consequences inequality reinforces

biases which strengthen the digital

divide inequality is not only about

access it's not only a matter of jobs

you hold it's also an issue about who

produces knowledge and what kind of

worldviews are encapsulated by that

knowledge what kind of knowledge and

values are being produced and designed

into our digital

Technologies information systems that

are built with unequal data can affect

people with devastating consequences

let's take this example from a paper

that suggests an intersectional approach

to digital Health it reads for example

the UK National Health Services Babylon

app released in 2017 used artificial

intelligence to advise patients on the

probability of a diagnosis based on

their selfreported symptoms the app also

advise a course of action contact a

doctor visit the emergency room or no

action a female smoker aged 59 years

with symptoms of a heart attack I.E

chest pain shortness of breath and

anxiety

received a diagnosis of depression or

panic attack whereas a male user with

the same background and symptoms was

informed of a possibility of a heart

attack the health information system had

biased data but gave incorrect

recommendations to women how does this

happen we often collect data without

really thinking about who we collect

data from goes a bit like this

schematically we collect data making

assumptions about the general user but

we think is universal we do not see the

issues or the conditions that different

users may suffer from and fail to treat

those conditions in time because we have

not seen them we have not identified

them they're not part of our data or

even knowledge so they cannot become

part of our

practice when we do have treatment it's

not adapted to the variety of users and

their varieties of bodies gender

differences here matter if we were to

include more people and have a more

person centered approach to the design

of Technologies we would necessarily

become more inclusive there are other

examples like predictive policing which

tend to discriminate against black

people or risk scores for receiving

benefit payments that negatively make

assumptions against women or single

parents so this is a wide issue and this

happens because biases can be designed

into the algorithms used for decision-

making the data on which algorithms take

decisions are themselves biased and this

affects women and particularly women

from Minority backgrounds this can have

large scale consequences a report in the

health journal lanet in 2023 suggested

that taking a feminist approach to

Women's Health could save 800,000 lives

it's not just data but the way that we

think about medicine or how our digital

systems come to cause unequal

consequences to people that we should

have been more attentive to as un

Secretary General Antonio gutes said

rather than presenting facts and

addressing bias Technology based on

incomplete data and badly designed

algorithms is digitizing and amplifying

sexism with deadly consequences of

course medicine did not wait for digital

technology to be gendered but now gender

inequality is reproduced at scale

digital systems amplify and scale

inequality they can cement and reproduce

stereotypes which affect the well-being

and health of people the third reason is

that there are many studies that suggest

that diversity in teams lead to better

and more Innovative outcomes for teams

and

organizations inclusion also increases

organizational economic returns by

helping teams refrain from defense

avoidance behavior patterns and

outwardly suspicious group think teams

are more accepting of unexpected

challenges and worldviews diverse teams

can just be more flexible and more

readily accept change this matters for

software development it has been

suggested that diverse teams can improve

the software quality they produce and

improve development productivity so to

conclude we have seen that technology is

not neutral it creates inclusion we've

seen in the introduction how it has

allowed social movements to make an

impact on the political agendas in many

countries but we we have also seen how

Computing can exclude or create

inequality the history of computing has

been gendered from the beginning we have

seen an intersectional view of exclusion

when we talked about gender imbalance we

saw how that imbalance was connected

with issues of class we have also seen

that digital inequality and exclusion

have very negative consequences as

technologists the way that you design or

participate in the digital economy has

consequences at the individual

organizational and societal levels in

next part we will introduce a few ideas

to counter mitigate or address exclusion