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Enhancing AI for Gender Equality: A Multi-stakeholder Approach

Presenting Thai and European case studies that have successfully designed methods to educate attendees to think analytically and systematically about the entire value chain of artificial intelligence to enable a gain a broader perspective.

Published onMay 08, 2024
Enhancing AI for Gender Equality: A Multi-stakeholder Approach

Enhancing AI for Gender Equality through the Multistakeholder Approach

Theetach Kaewtubtim


The application of Artificial Intelligence (AI) technology in today's world is widespread, covering almost every aspect of human life and spanning various economic fields. In sales and marketing, AI aids in data analysis, providing valuable insights that facilitate business growth by enhancing learning capabilities and enabling analysis of large datasets, thereby enabling companies to make better business decisions. Additionally, AI technology creates opportunities to find new customers for companies, leading to organizational growth and expansion. In the manufacturing industry, AI is applied in production robots, while in the medical and health sector, it plays a role in analyzing data and providing nutritional and exercise advice to meet customer needs effectively. Moreover, AI plays a significant role in the game industry, influencing game design, development, and maintenance processes. In transportation, AI contributes to the development of autonomous systems, as seen in investments by companies like Uber and Google in self-driving cars and trucks. This automation creates employment opportunities for AI engineers and machine learning experts. In the financial services sector, AI is used to detect suspicious loan applications or transactions and manage security systems in financial markets. Furthermore, AI is transforming the education system from traditional offline learning formats to online learning formats, developing intelligent content suitable for individual learning needs. Moreover, AI is widely applied in the entertainment industry, particularly in video streaming services like Spotify, Amazon Prime, Netflix, and Hulu, where it analyzes consumer behavior to recommend appropriate media.

Nowadays, AI technology's application spans across almost every facet of human life. While its adoption yields positive effects, notably enhancing efficiency in various activities, it also inevitably carries negative implications for certain groups. One prevalent issue is AI's gender bias, which complicates decision-making processes, often favoring males over females and detrimentally affecting women. This bias is exemplified in studies such as UNESCO's "I'd blush if I could: closing gender divides in digital skills through education," revealing how digital assistants are predominantly female, reflecting stereotypical perceptions of women as compliant helpers. This bias stems partly from the male-dominated engineering teams behind AI development, leading to systems that perpetuate gender discrimination, such as digital assistants prone to verbal sexual harassment. For instance, VoiceTV (2019) reported instances where AI systems were designed to tolerate such harassment, representing a troubling manifestation of gender discrimination in AI. Moreover, Techsauce (2021) highlighted global social media platforms utilizing gender-biased algorithms, evidenced by image cropping systems focusing on women's figures rather than their faces, reflecting a male gaze and perpetuating gender discrimination beyond sexual harassment. There persists an issue of gender bias, particularly evident in recruitment practices. Numerous instances in contemporary society reveal AI-driven decisions favoring men over women in hiring processes. For instance, as reported by The Standard (2018), Amazon established a team of engineers in Edinburgh, Scotland, to develop hiring practices using artificial intelligence assistants. These assistants employed over 500 computer models to analyze job applicants' work history and resumes, assessing them based on a star rating system ranging from 1 to 5 stars. However, the system's training data predominantly consisted of resumes from male applicants submitted over the past decade, resulting in a bias toward male candidates. Consequently, the AI system tended to screen out resumes from female applicants and graduates of all-female colleges, as well as disqualifying applicants who didn't meet specific qualifications without further consideration, as dictated by the system's parameters.

For this reason, the international community has recently begun to recognize the dangers of gender-biased AI, which exacerbates societal inequalities. In response, a social movement has emerged to accelerate the creation of awareness and change in this area. This movement includes participants from various sectors. The government sector plays a crucial role in designing policies and laws to regulate AI. The business sector aims to foster a positive image, while the civil society sector acknowledges the potential dangers if no changes are made. Consequently, various initiatives have been launched with the goal of developing AI technology in a more equitable manner. This includes promoting fairness across different demographic groups, with a particular focus on gender equity. These initiatives are integrated into educational programs, offering guidelines for project implementation that enhance the development of AI technology with a focus on gender fairness. The aim is to raise awareness, advocate for change, and derive lessons that can be applied to future projects for more effective implementation.

Theoretical Framework

This research study will examine the role of AI, drawing on various definitions such as John McCarthy's, which describes AI as 'the science and engineering of creating intelligence in machines.' Alternatively, AI can be defined as the science of endowing machines with intelligence, particularly within computer systems, enabling them to calculate, reason, and learn akin to the human brain, and respond to various situations. This capability significantly enhances the efficiency of computer systems in performing human roles. The term 'AI' can be dissected into two components: 'Artificial', referring to inanimate objects that are created or synthesized by humans, and 'Intelligence', meaning the calculated thinking that leads to successful outcomes, which is found in humans, animals, and certain types of machinery (John McCarthy as cited in Nat Arun, 2010).

The Electrical and Electronics Industry Insights Center (2021) describes AI as a branch of science and technology grounded in disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. The objective is to develop computer systems that mimic human behavior, including the replication of human ingenuity. The functions of artificial intelligence can be categorized as follows:

  1. Cognitive Science focuses on research aimed at understanding how the human brain functions and how humans think and learn. The basis of information processing in human-like form encompasses various systems, including Expert Systems, Neural Networks, Papnet, Fuzzy Logic, Genetic Algorithms, Intelligent Agents, and Learning Systems

(2 Robotics combines engineering and ergonomics to develop intelligent robots that are controlled by computers but can move in a human-like manner.ษ

(3) The Natural Interface represents a significant area of work within artificial intelligence, developed on the foundations of linguistics, psychology, and computer science. It encompasses various domains, including systems capable of understanding human language (Natural Language), virtual reality systems (Virtual Reality), hybrid artificial intelligence systems (Hybrid AI Systems), expert systems (Expert Systems), and Geographic Information Systems (GIS).

In addition, the Electrical and Electronics Industry Insights Center (2021) offers other definitions of AI. These include programming computers to reason, learn, and function similarly to the human brain, and developing computer systems that operate akin to human processing systems. AI aims for computers to efficiently perform tasks traditionally done by humans, such as those involving robotics. Moreover, AI encompasses efforts to develop both hardware and software systems that mimic human behavior. These systems are designed to understand human language and coordinate various components. For instance, in robotics, devices are equipped to recognize and respond using both visual and verbal cues, emulating human expertise and decision-making processes (as in expert systems). Such systems must also demonstrate the capacity for logic, reasoning, intuition, and the application of common-sense principles at a level comparable to human capabilities.

The concept of gender fairness is a crucial element of sustainable development, specifically addressed in Goal Number 5: Achieve gender equality and empower all women and girls. This goal encompasses objectives such as ending discrimination and eliminating all forms of violence against women and girls. It also includes recognizing and valuing unpaid care and domestic work, ensuring full and effective participation and decision-making in leadership at all levels, and universal access to sexual and reproductive health. From a policy perspective, this goal underscores the necessity to implement reforms that grant women equal rights to economic resources. Moreover, it advocates for an increased use of technology that empowers women and girls at all levels (United Nations).

As a final point, this research study will focus on examining the project implementation model within a Multi-stakeholder Approach (or Multi-Stakeholder Engagement). This method ensures that all involved groups are invited to participate in the project, with each group playing a role in the decision-making process from planning through to actual implementation (UNDP). Building on this idea, the study will develop a conceptual framework that examines the project implementation guidelines designed to advance the development of artificial intelligence technology. This focus on gender fairness employs a multi-stakeholder approach aimed at raising awareness and driving changes in AI development to ensure equity among various groups, including gender fairness. The goal is to derive lessons learned, make improvements, and effect changes that can be applied to operations and future projects to enhance their effectiveness.


This research study will employ various document review methods, encompassing books, tables, articles, and research. It will specifically focus on case studies of projects aimed at advancing the development of artificial intelligence technology with an emphasis on gender fairness, utilizing a multi-stakeholder approach. These case studies will include international examples and those implemented in Thailand, selecting only the most compelling cases from which to draw lessons. The findings will be synthesized to formulate policy recommendations for future operations.


Over the past decade, many countries, including Thailand, have recognized the importance of projects aimed at enhancing the potential for AI development. This recognition aligns with the goals of Thailand's 4.0 policy, prompting both government and private sectors to support advancements in the industry. Various projects have been implemented to bolster AI development potential. For example, the National Science and Technology Development Agency (NSTDA) promoted technology through the Software Park's Demo Day activity, which provided opportunities for candidates to develop prototypes of artificial intelligence innovations in the AI Innovation JumpStart project (National Science and Technology Development Agency, 2019). Most recently, the Thailand Artificial Intelligence Entrepreneurs Association was officially launched last year at the Office of the Digital Economy Promotion. This event gathered Thai AI entrepreneurs from across the country and included representatives from both government and private sectors, aiming to support and advance Thai AI technology businesses to stay abreast of current trends (Techsauce, 2021).

"This reflects that both the Thai government and private sectors recognize the significant potential for AI development. However, while many projects have been implemented in Thailand to enhance this potential, historically there has been a lack of focus on projects that address changing attitudes towards the development and use of AI in a manner that promotes gender equality. Often, the issue of gender equality is merely a minor concern, hidden within various practices based solely on ethics. Previously, government initiatives have primarily concentrated on designing regulations to guide the ethical regulation of AI. For example, in 2018, the Ministry of Science and Technology, through the Center of Excellence in Biological Science, collaborated with the Office of the National Science, Technology, and Innovation Policy Committee and the Artificial Intelligence Association of Thailand to organize a seminar titled 'Artificial intelligence, Big data, robots and IOT, the heart of new innovation...that is not unethical.' The objective of this seminar was to foster knowledge and understanding, and to facilitate the exchange of opinions on ethics in the development of artificial intelligence, robotics, and Big Data technologies. These discussions were intended to inform guidelines for the country's Big Data, AI, and robotics technology development policies (InfoQuest Limited, 2018). Additionally, in 2019, the Ministry of Digital Economy and Society, in collaboration with technology academics from Mahidol University and Microsoft, released draft ethical principles and guidelines for Artificial Intelligence (AI Ethics Guideline) to govern the development and utilization of artificial intelligence on the basis of responsibility.

This is considered the first step in creating transparency, reliability, and security for the system. In 2019, Thailand's artificial intelligence initiatives were documented (Techsauce, 2019). In 2021, the Science and Technology Development Agency and the National Bank set goals to advance Thailand's AI strategy, one of which focused on creating guidelines for data governance in AI applications (National Science and Technology Development Agency, 2021). Most recently, in 2022, the National Science and Technology Development Agency developed concrete ethical guidelines for artificial intelligence. These guidelines are intended for use in research, design, development, application, and transfer of AI and data science technologies that utilize data-driven algorithms. They are aligned with international principles on AI ethics, emphasizing the medical context, societal implications, and relevant laws. Additionally, these guidelines address and aim to mitigate potential impacts or damages, including preventing and reducing biases that may arise from AI decision-making processes, with specific provisions for oversight to prevent such biases (National Science and Technology Development Agency, 2022).

Considering the issue of gender equality arising from AI decision-making processes, it is evident that Thailand has not yet established cooperation among various sectors to accelerate and drive clear change. Despite initiatives to enhance AI development capabilities within both the public and private sectors, there is a lack of focus on addressing the gender bias inherent in AI. Although there are public sector ethics guidelines, there has yet to be a collaborative brainstorming session among different sectors to identify and address the hidden weaknesses in practices that are obstacles to achieving gender equality. This oversight may partly stem from the fact that Thai society has not fully recognized the importance of the gender bias problem caused by AI, as the issue is not currently perceived as severe enough by many to warrant attention.

Meanwhile, European countries are actively working to enhance their AI development capabilities, with a focus on creating more gender fairness. It is recognized that gender bias in AI could pose a formidable threat in the future. Previously, projects have been implemented using what might be described as a multi-stakeholder approach, aimed at improving AI behavior to ensure a fair decision-making process that does not discriminate, particularly in employment. The methods to increase potential in this area vary. In some projects, the emphasis is on implementing regulations. The government sector collaborates to analyze and plan ways to enhance these regulations to better control AI and prevent gender bias. Other projects focus on collaborative case study analysis to identify causes of problems and design guidelines for improving AI. Additionally, some initiatives support the private sector in developing AI systems for startups that do not perpetuate gender bias. An interesting case from the Netherlands exemplifies this approach; it not only involves seminars to facilitate learning and exchange but also uses identified problems and opportunities to develop more concrete methods for improvement and problem resolution.

Case study: Project Operation Model in Netherlands

Countries in Europe have taken the initiative through the online Mutual Learning Seminar project, which aims to foster in-depth knowledge and understanding of the issues and opportunities in developing AI that supports gender justice, particularly concerning the recruitment process. This project has garnered support from representatives across various sectors, including government agencies, the private sector, and field experts (European Commission, 2020).

The Netherlands is one of the countries participating in the project, and it employs an interesting method of implementation. A study of project implementation in the Netherlands revealed that in 2020, a webinar titled 'Artificial Intelligence and Gender Biases in Recruitment and Selection Processes' was conducted. The main source of support for this webinar was the Ministry of Education, Culture, and Science. Additionally, the seminar included participants from various sectors, such as academics from educational institutions and the private business sector, along with representatives from international organizations.

The content of the seminar was divided into two parts: (1) a case study on the problem of gender bias arising from AI algorithms, and (2) initiatives to raise awareness and create solutions for addressing gender bias in AI algorithms. In the case of the Netherlands, speakers presented a study on actual gender biases in the labor market, highlighting that the AI candidate selection process was skewed towards male job applicants. The analysis identified the main cause as the use of imported data that referenced applicants’ work histories going back 10 years, which lacked diversity due to the predominance of male candidates in past applicant pools. The lecturer emphasized the need for designing solutions, such as amending supervisory regulations. Subsequently, seminar participants discussed ways to improve the quality of datasets by ensuring the information was diverse and representative of broader population characteristics. They also explored strategies for creating diversity in the dissemination of information to various demographic groups, especially those that are underprivileged.

Furthermore, there was also a discussion on the issue of amending legislation to prevent gender bias resulting from the application of AI in the labor market. From a joint analysis, it was determined that existing laws are not fully enforceable in regulating AI behavior due to many challenges, such as the difficulty in proving faults. Therefore, to address this issue, seminar participants proposed solutions. They suggested that it is necessary to design specific regulations to govern AI, separate from existing government regulations. This approach would allow the business sector to assume responsibility and establish strategies for its own supervision.

In addition to the aforementioned discussions, the Netherlands also presented on an AI development competition (Hackathon), which was initiated after the public and civil sectors jointly set goals to increase opportunities for women in the labor market. The government then recognized the need to regulate AI to ensure behavior that aligns with ethical standards. Consequently, a collaboration with the private sector was established to promote the development of ethical AI. The inaugural Hackathon was organized in 2020 to build upon long-discussed approaches to ethical AI development. Participants from various sectors, including data scientists, human resources professionals, business analysts, lawyers, social scientists, and general entrepreneurs, joined the competition to explore ways to improve AI focused on addressing the root causes of gender bias. Interesting ideas were awarded, and some received government support to further develop the prototypes (European Commission, 2020).

Case Study: Project Operation Model in Thailand under the Feminist AI Project and AI Ethics Workshop by the Center for Science, Technology and Society

Since mid-2021, the Center for Science, Technology, and Society (CSTS) has been funded by the International Development Research Centre (IDRC) to conduct the Incubating Feminist AI project. This project aims to raise awareness and develop knowledge and skills in artificial intelligence that promotes gender fairness across all societal groups. The initiative encompasses a variety of activities, including organizing joint meetings between agencies and networks from all around the world to exchange knowledge, providing training on the design and use of AI that emphasizes gender equality, and enhancing potential through various projects. Most recently, on May 20, 2022, the Center, in collaboration with IDRC, launched a workshop project called the Feminist AI Workshop, which adopts the multi-stakeholder approach.

Under the Feminist AI and AI Ethics Workshop project, participants from various career fields with diverse knowledge bases were invited. The activities included lectures on the development of various forms of AI, tracing their origins from input data to model creation and decision outcomes. The lecturers also explained the causes of hidden biases in AI decision-making processes that lead to undesired gender inequality results and discussed potential improvements. Furthermore, case studies were presented, illustrating instances where the use of artificial intelligence resulted in gender-biased outcomes. These included analyses of the causes and methods for resolving such issues. Following the lectures, participants engaged in discussions on additional case studies, collaboratively analyzing the causes and devising solutions.

The meeting began with a presentation on AI and Feminist AI by a social studies, science, and humanities academic, Prof. Dr. Soraj Hongladarom. He discussed the origins of AI and the social problems caused by unethical AI development. Following this, a computer engineering expert, Asst. Prof. Dr. Jitthat Phakcharoenphon, delivered an in-depth lecture on AI. He covered the general principles of AI, starting with imported data and leading up to decision-making, including the principles of fairness used in AI analysis. He presented case studies concerning fairness across various dimensions, such as in health matters—deciding on vaccinations—and predicting crime, as well as issues like cropping images of people’s faces on Twitter, sorting different types of objects (which results in racism towards certain groups), language translation, and loan approvals. He explained that the causes of such biases include an asymmetry in information and the nature of data acquisition. He also described methods for managing bias, including direct methods (model review and correction) and indirect methods (e.g., adjusting staff, designing a new model checking system).

After that, additional case studies were introduced, prompting participants to divide into groups for collaborative analysis. The discussions were split into three case studies focusing on employment issues, facial recognition systems, and health issues. During the session, participants shared their perspectives based on their varied expertise. Most agreed that a major cause of bias is the improper importation of data, such as datasets that lack comprehensive coverage. This issue often stems from reluctance to disclose information or the underrepresentation of certain groups. A notable example discussed was in employment, where AI systems are used to enhance efficiency in personnel selection. From this case study, participants explored solutions to gender bias, such as adjusting preliminary selection processes to prioritize candidates with specific potential. Following these discussions, Assoc. Prof. Dr. Supawadee Aramwit, an expert in computer engineering, provided insights into other encountered case studies, including the ongoing issues with facial recognition systems where database limitations still exist. She emphasized that organizing this event was a valuable initiative, offering a platform for individuals from diverse academic backgrounds to converge, identify issues, and develop guidelines for creating more ethical AI

In this regard, organizing a meeting to exchange knowledge has substantially increased participants' understanding. Those previously without a basic knowledge of AI are now able to comprehend the problems and envision solutions. The meeting also heightened awareness of the negative effects of bias embedded in AI and underscored the necessity of collaborative efforts to prevent and correct these biases before they impact society adversely. Moreover, the event inspired some participants to pursue deeper learning to contribute to designing AI systems that prioritize equality in decision-making processes. Although the project, as described above, has not yet organized activities such as hackathons, similar to those in Europe, it has laid a foundation for positive thinking within society. This is considered a good starting point for future developments. The most promising ideas will be further conveyed to AI developers. The author anticipates that if this project continues, it may elevate to developing activities that can foster new technologies in the future (activity photos are included in the appendix).

From the case studies of both Thailand and Europe mentioned above, it is evident that despite the wide scope of the projects organized in Europe, which included a hackathon-style competition as part of the seminar leading to more concrete changes in the form of new technology prototypes, the projects implemented in Thailand by the Center for Science, Technology, and Society have not yet reached the stage of researching and developing new technology. However, they have designed methods to provide knowledge to attendees, fostering systematic analytical thinking and a comprehensive consideration of the entire value chain of artificial intelligence. This approach has given meeting participants a broader perspective, not limited to solving specific problems. The foundational knowledge gained, if continuously built upon, will surely contribute to changes in the development of AI technology in ways that effectively reduce gender inequality. Nevertheless, the information derived from the lessons above shows that the implementation of projects in both Thai and European contexts, despite some differences, involves similar work processes primarily focused on collaborative problem analysis and solution finding, as summarized in Figure 1.

Diagram Description automatically generated

Figure 1: Guidelines for Implementing Projects to Enhance AI Development with a Focus on Gender Equality


The application of Artificial Intelligence (AI) technology is widespread in today's world, encompassing almost every aspect of human life and covering many economic fields. Although the adoption of such technology can bring positive effects, particularly by enhancing efficiency in various activities, it is undeniable that there are also significant negative impacts. These often disproportionately affect certain groups of people, especially in terms of fairness in employment opportunities between men and women. In recent years, the international community has begun to recognize the dangers of gender-biased AI, which exacerbates societal inequalities. Consequently, there has been a social movement to accelerate the creation of awareness and initiate changes in this area. This has led to research studies on the format and effectiveness of project implementations using a multi-stakeholder approach, with the goal of raising awareness and promoting changes in AI development to ensure fairness among different groups of people.

From a study on the cooperation to develop artificial intelligence technology with a focus on gender fairness through the multi-stakeholder approach in Europe, it was found that the case of the Netherlands is particularly notable. A seminar was organized to present a case study on the problem of gender bias arising from AI algorithms. The event aimed to raise awareness and develop solutions to address gender bias created by AI algorithms. Speakers presented real-life case studies from the labor market, highlighting the causes of these issues and the urgent need for solutions. This allowed seminar participants to engage in discussions to develop guidelines not only for improving AI algorithms but also for amending laws to prevent gender bias resulting from the application of AI in the labor market.

The Netherlands has also proposed an ethical AI development competition (Hackathon) that includes participants from various sectors. These participants come together to present ideas for improving AI algorithms, fostering societal attitudes towards justice through AI use. Additionally, the researcher examined the Thai case under the Feminist AI and AI Ethics Workshop project, operated by the Center for Science, Technology, and Society. This project drew participants from diverse professional fields, offering educational activities led by speakers about the origins and development of AI. They discussed the causes of hidden biases in AI decision-making processes that lead to undesirable gender inequality outcomes throughout the AI value chain and explored potential improvements. Various case studies were presented, demonstrating how AI has produced gender-biased results, accompanied by analyses of causes and methods for resolving these issues. After the speaker provided knowledge, further case studies were analyzed collaboratively by the participants. This meeting not only enhanced participants' understanding of AI—especially for those previously without basic knowledge—but also raised awareness about the negative effects of biases hidden within AI. It highlighted the necessity for collective action to prevent and correct these biases before they adversely impact society. Moreover, the session inspired some attendees to pursue deeper learning to engage in designing AI systems that prioritize equality in their decision-making processes.

From both the Thai and European case studies mentioned above, despite the broad scope of the projects organized in Europe—which included joint problem analysis and solution finding by meeting participants, as well as integrating a hackathon-style competition into the seminar, leading to more concrete changes through the development of new technology prototypes—the projects implemented in Thailand by the Center for Science, Technology, and Society have not yet reached the stage of research and development of new technologies. However, they have successfully designed methods to educate attendees to think analytically and systematically about the entire value chain of artificial intelligence. This approach has enabled meeting participants to gain a broader perspective, not merely limited to solving specific problems. If this foundational knowledge continues to be built upon, it will undoubtedly play a crucial role in future developments.


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