“Bridging the Divide: Advancing Gender Equality in AI Adoption”
As artificial intelligence (AI) usage, particularly #generativeai, continues to grow globally, recent findings indicate a notable gender disparity in its embrace. Men are more inclined to incorporate AI into their professional lives compared to women, as revealed by a recent survey conducted by FlexJobs involving over 5,000 participants. The poll showed that over half of men use AI both personally and professionally, whereas only about a third of women do so.
AI tools like Open AI’s #ChatGPT have become increasingly popular for tasks such as job searching, resume building, and crafting cover letters. Surprisingly, men are more likely than women to have explored or considered utilizing artificial intelligence for these purposes. Moreover, men appear to express more concern about AI potentially displacing jobs, with 38% expressing this worry compared to 27% of women, as indicated by the survey’s results.
The unequal adoption of AI between genders can be attributed to various factors, with experts emphasizing the importance of narrowing this gap for optimal AI system performance. Achieving gender equality in the workplace is also contingent upon addressing this divide.
So, why is this happening?
A significant contributor to the gender gap in AI adoption is the differences in opportunities for STEM (science, technology, engineering, and mathematics) education. Many women worldwide may feel less familiar with AI because they weren’t encouraged to pursue careers in these fields during their earlier years.
A 2019 survey conducted by the UK Department of Education (KS4) illustrates this point. It found that female students were less likely to rank a STEM-related subject as their top choice for enjoyment, with only 32% doing so compared to 59% of their male counterparts. Similarly, fewer females considered themselves to excel in STEM subjects, with 33% claiming expertise compared to 60% of males.
Due to this lack of early exposure and encouragement, some women may approach AI technology with caution. Additionally, women tend to harbor more doubts about AI’s capacity to make impartial and consistent decisions in challenging scenarios.
To further underscore the gender gap in AI adoption and STEM education, let’s examine some additional noteworthy statistics:
- The Alan Turing Institute reports that only 22% of data and AI professionals in the UK are women, and this drops to a mere 8% of researchers who contribute to the pre-eminent machine learning conferences.
- According to the World Economic Forum, while 54% of men now use AI in either their professional or personal lives, this falls to just 35% of women.
- A report by Heinrich-Böll-Stiftung states that other studies have found that only 10–15% of machine learning researchers in the leading technology companies are women; less than 14% of authors of AI research papers are women3.
- An article on Towards Data Science mentions that over 41% of the AI papers in the Netherlands and over 39% of AI papers in Denmark had at least one female co-author4.
- An article on EPAM discusses the challenges and benefits of adopting AI in STEM education. It mentions that a major challenge in STEM is the limited number of problems, exercises, and tasks it can take for students to master a skill.
- Women make up an estimated 26% of workers in data and AI roles globally, which drops to only 22% in the UK1. In the UK, the share of women in engineering and cloud computing is a mere 14% and 9% respectively.
- In terms of AI utilization, 15% of women use AI in their personal life, compared to 21% of men. For use at work (with manager’s permission), it’s 5% for women versus 11% for men.
- A survey found that while 54% of men now use AI in either their professional or personal lives, this falls to just 35% of women.
These statistics suggest that there is indeed a gender gap in both STEM education and AI adoption. This could be due to a variety of factors, including societal norms and expectations, lack of encouragement for women to pursue careers in STEM fields, and biases in the workplace. Addressing these issues is crucial for ensuring diversity and inclusivity in these rapidly growing fields.
Potential Consequences
Biases in AI outputs are a major worry, and they may worsen if the gender gap widens. The present skills gap is encouraging more men to use technology to start businesses, which could lead to the development of even more biased AI models. Unconscious biases in AI systems may be perpetuated by biases in training data.
Because AI is ready to become a major decision-maker in a variety of areas, it is critical to guarantee that the data supplied into these models is equitable and fair.
Women’s career advancement and representation in leadership roles may be impacted by the AI skills gap. Understanding AI and its ramifications is becoming increasingly important for anyone aspiring to positions of leadership.
What Can Be Done?
While current data highlights a gender gap in AI adoption, experts predict that this gap will likely close over time as more women become adopters of AI technology.
Employers play a critical part in this process by providing employees with equal opportunities to learn how to use AI products efficiently. Using the power of modern technologies to improve job performance is a responsibility, and it is a responsibility to give all employees with the appropriate skills.
Companies should be aware that different groups with similar worries and reservations about using new technology might also require unique training and approaches.
Addressing Bias in AI
It is important to understand that AI technologies are not inherently unbiased or “magic.” They rely on information and theories developed by humans, which can introduce biases at various stages of development. These biases can cause significant problems:
1. Harassment and Violation of Privacy: AI systems are increasingly being used to generate non-consensual and offensive content, primarily aimed at women. Due to the rapid development of these technologies, it is now easier to create deepfakes, which can harm people’s psychological and social well-being.
2. Reality of Bias and Discrimination: Built-in biases in AI systems can lead to discrimination in various fields, including employment, loans, and criminal justice, thereby negatively affecting opportunities and rights.
3. Generative AI and Biases: Racist and gender stereotypes can be exacerbated by generative AI models. These models may reinforce and even amplify existing biases.
To address these issues, tech firms must thoroughly analyse their datasets, encourage diversity in their teams, and ensure that their algorithms work fairly for all genders and groups.
In addition to gender bias, AI faces challenges related to race, which has alarming implications for people of color. Efforts to address these biases should involve increasing diversity in the technology industry.
Perpetuating Gender-Based Cultural Stereotypes
The “feminisation” of AI tools can reinforce traditional gender roles through voice and appearance, which may perpetuate negative stereotypes. For example, virtual assistants with female voices may contribute to this issue.
Countering Biases in Artificial Intelligence
To counter some of the biases in AI, we need to:
- Identify areas where gender-based bias in AI is evident.
- Examine who defines the algorithms and instructions that artificial intelligence follows, because technology reflects the underlying beliefs and ideas of the programmers.
- Increase the participation of women in AI research, development and training of large language models, and decision-making.
- Develop support structures to address challenges faced by women in STEM professions.
- Implement strong guidelines and a legal framework for regulating AI use.
- Promote awareness and training on the ethical use of technology.
- Ensure adequate representation of women in decision-making and technological development.
- Adopt an “Ethical AI” approach that emphasises inclusivity and diversity.
- Encourage collaboration among various stakeholders to address gender bias in AI.
In conclusion, addressing gender bias in AI is essential for a more equitable and responsible future. It requires concerted efforts from all parties, including #technology companies, academics, government entities, civil society organisations, and the media. Ethical AI principles, diversity in AI development, and robust guidelines are crucial for eliminating biases and ensuring AI serves the greater good on a global scale.
So, before we wrap things up, I wanted to take a moment to introduce myself in case you’re just joining my newsletter community. I’m Ashlea Atigolo, and I’m not just the co-founder of INATIGO but also one of the brains and developers behind Finley AI — The first purpose-built large language model (#LLM) for personal finance.
I’m really passionate about a few things — technology, AI, ESG, and business. These are the areas that truly light me up, and I’m excited to share my insights and experiences with you.
My real goal of my articles is to provide you with as much valuable information and guidance in these areas that mean so much to me.
If you haven’t already, I’d like to personally invite you to please “follow” and share with others.
Warmest,
Ashlea
#AI #innovation #business #artificialintelligence #technews
