Gender inequality has afflicted and continues to afflict our civilisation for millennia. Women have had to battle for their fundamental rights, whether it be voting rights, the right to marriage and abortion, or the freedom to work and get financial aid. As more women enter the workforce, we are now confronted with issues such as wage disparity, a lack of female representation, and other deeply ingrained preconceptions. We are the ones who have constructed the gender hierarchies and inequities in our society. While perfect gender equality is still a long way off, artificial intelligence is being hailed as a promising solution. However, there is a common misperception that technology and AI are perfect and cannot have human-like defects. As a result, it’s critical to grasp what gender bias in AI entails.
While artificial intelligence is still in its infancy, it continues to have an impact on our daily lives in a variety of ways, including job recruitment, bank loan approval, search engines, and more. While AI currently rules just a small portion of our lives, it is poised to become increasingly essential in all aspects of our existence. As a result, understanding how AI impacts and could effect our lives, as well as its potential contributions to gender equality, is crucial.
Algorithms are a set of rules that Artificial Intelligence uses to function. Large data sets are used by these algorithms to find trends, make predictions, and make recommendations. Machine Learning is a process that occurs when AI improves organically over time through experience. As a result, while AI improves on its own, the foundations are built by the founders, whose gender prejudices, if any, would
influence the AI system’s evolution. This would result in a ‘AI system which makes biassed decisions’ about specific categories of people, necessitating further research. AI has a significant impact on gender equality, particularly for women, because to the algorithm developers’ inherent assumptions and biases.
The second point to consider is how today’s popular AI applications are disseminating and promoting gender stereotypes. For example, most AI-powered virtual assistants have female voices, and the world’s most powerful computer, Watson, is named after a man. On a worldwide scale, these gender biases risk further stigmatising and dismissing women. Given the rising prevalence of artificial intelligence (AI) in contemporary communities, such prejudices put women at risk of being left behind in all aspects of economic, political, and social life.
While we are aware that developing technology has the potential to contribute to a wide range of development goals, It’s crucial to remember that the benefits of these technologies are not always equal when we design, develop, and implement them. And that there are significant negative, often unanticipated gender consequences in some cases.
With the rise of AI, a number of gender biases have been discovered. Some have minimal impact on gender equality, while others significantly expand the gender gap.
- Reinforcing stereotypes: There have been numerous instances of AI reinforcing gender biases and stereotypes. For example, we’ve seen image labelling software that identifies gender associated with specific activities, such as baking or shopping, and categorises them as female. In male-dominated occupations, there are language translation softwares that systematically transform women’s voices into male voices. Machine learning software deployed in these applications not only reflects but amplifies existing prejudices in society.
- Discrimination based on gender: In addition to harmful stereotypes, AI has been shown to perpetuate discrimination in the past. Gender profiling has been duplicated in some situations by AI-based screening systems. As a result, it has developed gendered assumptions based on data provided to it, such as preferring male candidates for appointments in male-dominated industries.
- Lack of female representation: In addition to bias, we have technology that has been developed with little or no input from women, resulting in products that do not serve women. For example, there have been voice recognition technologies that have not been tested on female voices and hence are less effective in recognising female voices. For example, health applications that were released as all-encompassing profiling to track everything related to health ignored menstruation. This means that half or more of the users of these apps were not served.
- Women’s online safety: A growing number of issues related to women’s online safety are a major concern in a more digitalized society. Online harassment, stalking, sexual predation, and even false power have all increased in recent years. The face of one individual can be superimposed on pornographic photographs or videos using a variety of tools and apps. According to a 2018 study, deep fake technologies are used 96% of the time to create pornographic or pedophilic content, with women and girls accounting for 100% of the victims.
- Job automation’s dangers: Artificial intelligence (AI) has a negative impact on women’s economic empowerment and labour market position. According to recent IMF studies, women are more likely than males to lose their jobs as a result of automation. Women, on average, are 11 percent more likely than males to lose their jobs owing to automation.
While artificial intelligence (AI) poses a huge danger to gender equality, it is critical to understand its potential for good development.
1. Design evaluation- First and foremost, when developing an AI application, one must be acutely aware of the risks mentioned above. This allows you to detect and incorporate relevant checks and fixes at each stage. Second, product design and web design teams must be diverse and inclusive in order to incorporate recommendations and viewpoints from all groups, including women, in various stages, avoiding unexpected technological consequences. We can enlist the help of women and certain user groups or consumer groups while developing a product.
Before new technologies are deployed and put on the market, it is necessary to examine the trialling processes and effectively pilot them. The Design Justice Network is one such group that studies this topic. The organisation questions how design and designers might damage those who are disenfranchised by power structures.
2. Women’s education: As previously stated, some of these issues may be exacerbated by the lack of female representation in relevant fields. Increased female representation and a more diverse workforce can help to reduce the danger of biases in the development of these technologies. As a result, women’s education plays a critical part in this.
Women must have access to STEM education in order to enter the field of AI.
3. Enhance human capabilities: Rather than replacing workers, especially women, AI should be used to enhance their existing abilities. Artificial intelligence (AI) has the potential to eliminate occupations. However, AI has the ability to teach, train, and augment individuals to help them perform better in their jobs and activities. AI has the potential to improve the quality of a person’s work, resulting in better writing, design, healthcare, communication, education, and art.
4. Funding evaluation: It should be a requirement that if a company wants money to fund their research, they must demonstrate that their design is a developing and reliable technological solution. Part of that is ensuring they have a diverse team and meaningful stakeholder involvement. Governments may, for example, provide value-based financial assessments and link funding to gender equality standards. This means that companies who achieve these gender equality standards and incorporate women in the AI development process will be eligible for financial assistance.
5. Multidisciplinary groups: More multidisciplinary groups working on AI data problems are needed because problems are not solved by technologists alone. It is up to technologists, policymakers, economists, social scientists, anthropologists, behavioural economists, and others to really consider how they will build new models that are not simply digitizations of existing models. As a result, adding people from various backgrounds, particularly women, will aid in reducing AI biases.
6. Including men in the movement: Gender equality isn’t just a woman’s issue; we need men to join in and change their minds as well.
We need men from all walks of life to help us achieve social change. Again, it’s about diversity, but not just gender diversity. Age, cultural background, occupational history, and ethnicity are all factors. The AI feminist movement has made progress in recent years. Women Leading in AI is one such organisation lead by this movement, which is essentially a worldwide think tank for women in AI with the goal of addressing prejudice in algorithms caused by a lack of diversity and inclusivity in the field of Artificial Intelligence.
7. Investing in women-led or female-founded initiatives: More support for women-led or female-founded initiatives is needed because some of these initiatives are producing a lot of good ethical AI answers today. According to a survey, women-founded projects received only 2.8 percent of total startup funding. To encourage more women to take the initiative and assist diversify this area, increased investment is required.
8. Reframing AI and gender ethics: Women made up about 37% of the tech workforce in 1984, but only about 18% of computer science workers today. It has reduced even more with AI, to around 12%, and it has remained relatively unchanged since 1984. The first step in finding a solution to this problem is identifying the gender discrepancies. Women must have more chances in the tech industry, which must diversify and include gender equality in their organisational ideals. As a result, our panellists unanimously recommended rethinking AI ethics and principles. There is an urgent need to redefine AI ethics and principles in order to make women a larger part of AI and technology in general.
Way Forward
Overall, AI models and algorithms are portrayed as impartial and neutral, yet this is definitely not the case. As previously noted, the biases of those in charge of AI systems continue to have an impact on the system in multiple ways.
This sexist system must evolve and change for the better, and the first step is to incorporate more women in the process and diversify the teams participating. Furthermore, the data we feed into our systems must be handled with caution, and it must be updated on a regular basis to reflect changing circumstances. Women are gradually gaining prominence, and machines will not be able to learn about this on their own.
As a result, while feminist AI movements attempt to build a network of varied women in AI, promote fairness and equality, and eliminate bias in AI, design assessment teams can step in and check for biases. Such a framework will successfully aid in the investigation of who is responsible for technology design, who benefits from it, and who is likely to be hurt by it.
