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Strong Girl AI: Frameworks for the Empowered Mobility of Women in Southeast Asia

Transport systems in Southeast Asian cities have been hailed as particularly dangerous and unsafe for women and girls. To address this issue, some machine learning applications powered by Artificial Intelligence (AI) have been created and developed.

Published onApr 16, 2024
Strong Girl AI: Frameworks for the Empowered Mobility of Women in Southeast Asia

Strong Girl AI:
Frameworks for the Empowered Mobility of Women in Southeast Asia

Hazel T. Biana* and Rosallia Domingo**

ABSTRACT

Transport systems in Southeast Asian cities have been hailed as particularly dangerous and unsafe for women and girls. To address this issue, some machine learning applications powered by Artificial Intelligence (AI) have been created and developed. These apps are now used to report to authorities, get in touch with emergency contacts, share real time location, ring danger signals, and monitor unsafe areas and modes of transportation. Despite helping women and saving their lives, these apps do not, however, tackle the underlying issue of perpetrators’ violence against women. Rather than empowering women to fully take control of their mobility, these apps normalize violence and reinforce victim blaming mentalities. But what if AI can actually “strengthen” or truly empower women, so that they can “take back” their mobility? We propose revised frameworks of thinking for new AI models that are not based on normalizing violence but rather empowering women to be “strong girls”. Perhaps, AI can eventually empower and protect the vulnerable, particularly women and girls in Southeast Asia, and help them reclaim their mobility.

Introduction

Public safety is a key issue when it comes to the mobility of Southeast Asian women (OECD, 2021). It has been reported that the Southeast Asian cities of Jakarta, Kuala Lumpur, Bangkok and Manila are some of the most dangerous transport systems in the world for women (Thomson Reuters Foundation, 2014). In order to address this issue, the New Urban Agenda and the United Nations’ Sustainable Development Goals (5, 11, 16) have included the safety and inclusiveness of these systems in tracking sustainable progress (United Nations Women, 2017).

One of the developments to keep women safe, is the creation of machine learning applications powered by AI. Throughout the world, women have been making use of personal safety apps either to report trouble to authorities, get in touch with their emergency contacts, share real time location information, ring danger signals, or be alerted about unsafe areas and modes of transportation. These apps have saved many lives and have kept women safe. In Southeast Asia, some examples of these apps include Jakarta Aman which uses an in-app tracking device that tracks your location and trigger an alert to your designated contacts when in an emergency; Dlock which uses a hyperlocal bulletin board that connects to other app users who are within a set of radius to give distress signals if needed; and Riding Pink which is a woman-only ride-hailing app that provides safe and alternative modes of transport for both women drivers and passengers.

From SOS alerts to emergency contacts, to location sharing, and only-women ride-sharing services, these apps are designed based on the idea that a smart digital solution can help women feel safer in public spaces. These apps, however, do not tackle the underlying issue of perpetrators’ violence against women. Rather than empowering women to fully take control of their mobility, these apps normalize violence and reinforce victim blaming mentalities. For example, data sets derived for machine learning presupposes that someone be exposed to such unsafetyness first so that safer routes may be predicted. Furthermore, women would already have been harassed or exposed to danger before the apps could actually protect them.

As an implication, women are continuously being conditioned to avoid going out at night, walking in dark alleys, or riding in male-driven taxis. Rather than being empowered to make their own commuting or transportation choices, these apps make women adjust to the possible would-be offenders. Women are responsible for their own safety, expected to be careful, and are thereby blamed for not being careful. In these cases, victims may even be blamed for not having or using these safety apps! But what if AI can actually “strengthen” or truly empower women, so that they can “take back” their mobility? We propose revised frameworks of thinking for new AI models that are not based on normalizing violence but rather empowering women to be “strong girls” in order to reclaim their mobility.

In order to arrive at these revised frameworks of thinking, the paper is divided into five parts: 1) the concerns of Southeast Asian women when it comes to mobility, such as women and girls’ experiences, behaviors, attitudes, and feelings about mobility; 2) existing AI-driven applications utilized by the women in Southeast Asia, and the motivations and ways of thinking behind their development; 3) the gaps in these apps, a critique of motivations behind their creation, and why these AI-driven applications normalize violence, and promote victim-blaming mentalities, 4) a discussion of the “strong girl”, “take back” and other ways of thinking, and proposed revised frameworks of thinking for new AI models that are not based on normalizing violence but rather empowering women; and 5) recommendations on reclaiming the mobility of women in Southeast Asia.

The Mobility of Women in Southeast Asia

"The world is too dangerous. It's tough for the weak. . ."

-Do Bong Soon, Strong Girl Bong Soon

The Southeast Asian cities of Jakarta, Kuala Lumpur, Bangkok and Manila are said to be some of the most dangerous transport systems in the world for women (Thomson Reuters Foundation, 2014). The same has been said about other Southeast Asian cities of Phnom Penh and Hanoi (Son, 2014; You, 2019). Suffice to say, questions have been raised about whether women should actually travel alone in the Southeast Asian region given such findings.

In Indonesia alone, women were thirteen times more likely to be harassed in public places than men (The ASEAN Post, 2019). With more than 400,000 reported cases of violence against women in 2019, 28% of the cases were actually committed in public spaces (Danoekoesoemo, 2020). Three out of five Indonesian women experience verbal harassment, physical and non-physical harassment such as staring or indecent exposure in public premises. Unfortunately, victims who report sexual harassment are blamed by police officers who insist that the victims brought the violence upon themselves for travelling late at night or dressing provocatively (Danoekoesoemo, 2020). Because of this, public transportation has become a venue of naturalized violence (Sadiq, 2017). To cope with such circumstances, and to address their fear of sexual assault, some Indonesian women have developed their own individual strategies to travel safely through private mobility (Sadiq, 2017).

In Malaysia, 57% of women experience being sexually harassed verbally while walking on streets (Centre for Governance and Political Studies and All Women’s Action Society Malaysia, 2021). While their Indonesian counterparts have used private mobility as a strategy to travel safely, 68% of 1,056 Malaysian women aged 18 to 30 claim to feel unsafe driving alone at night. 71% even changed their travel routes and routines for fear of being sexually harassed. When it comes to traveling alone, Malaysian women’s main worry is fear. This fear includes safety and anxiety concerns (Ying et al., 2017, p. 52). This is on top of the concern that some Malaysian women feel that Southeast Asian societies deem the solo travel of women as inappropriate (Ying et al., 2017, p. 52). In one study, women asserted that “public transportation facilities, and infrastructure related to it do not consider the needs of women travelers but fit men's standards” (Harumain et al., 2021a, p. 109). This observation has resulted in difficulties for the women in Kuala Lumpur as they become dependent on men for traveling or deterred from using public transportation independently (Harumain et al., 2021a, p. 109).

One in five Thai women have likewise experienced sexual harassment in public places through public transportation (Khidhir, 2019). Such harassment includes assault, verbal comments, flashing, taking pictures without consent, or even showing pornographic materials to victims. Unfortunately, women do not report these cases to authorities because of embarrassment, feelings that no one will address these problems anyway, fear of repercussion, and societal or cultural pressures. Some women state that Thailand is a safe place for women to travel, but others still keep alert due to recent cases of rape and assault in Koh Tao (Brugulat & Coromina, 2021, p. 649). For transgender women commuters in Bangkok, verbal and sexual harassment is an everyday occurrence. As such, these instances impact on their choices of transportation, making transgender women strive to buy their own cars so that they can travel in peace (Chaiprakobwiriya, n.d., para. 8).

In Manila, some college students have been sexually assaulted and harassed while in transit, be it on a bus, train, at the bus stop or station platform, or on their way to/from transit stops (Mateo-Babiano et al., 2020). Based on their experience, students would more likely be verbally harassed if they were female, if they take shorter and more frequent commutes, or if environments were poorly guarded or dimly lit (Ceccato & Loukaitou-Sideris, 2020). To avoid such instances, Filipino women would take precaution by dressing in a certain manner, making sure that they only wait for buses at well-lit places, or avoiding bus stops and train stations where crime is prevalent. The Metro Rail Transit, on the other hand, is a different matter altogether wherein victims experience groping and other lewd acts (Dakis, 2012). To avoid such occurrences, some women resort to using their hands or bags to shield themselves from other passengers' body parts, trying to move away from perpetrators, or reporting incidents to the guards (Dakis, 2012, paras. 31–35).

In other Southeast Asian cities such as Hanoi, more than half of women and girls state that sexual harassment likely occurs in the streets, parks, bus stations and public bus terminals (Son, 2014, paras. 4–5). In Phnom Penh, many feel unsafe when leaving their workplace, especially when they must walk in deserted and dark streets (You, 2019, p. 222). Lao women also report not feeling safe walking alone at night (Social Institutions and Gender Index 2019). Accordingly, Cambodian women likewise feel unsafe in their mobility especially when they are in transit (You, 2019, p. 222). In Timor-Leste, insecurity in public spaces due to communal conflicts and gang violence remains to be a barrier to women’s mobility (Grameen Foundation, 2021, p. 9). Sexual harassment is also a pervasive problem in Brunei but remains to be a social taboo that no action had been taken to address reported incidents (DkNur Qasrina Nadiah Pg Abd Rahim et al, 2021). While the streets of Singapore may be the safest for women in Southeast Asia, a spate of cases of sexual assault and harassment in other spaces such as private-hire vehicles, classrooms, hotel rooms and their own homes are more common (Thomas, 2021).

The women in Southeast Asia are fearful, anxious, and distressed whenever they are in transit. Unfortunately, they, most often than not, do not have a choice, as they are ‘transit captives’, or “overly reliant on public transport” (Ceccato, 2017, para. 4). As seen in the examples above, women have to adjust or create mechanisms to lessen the risk of being victimized by changing their routes and schedules or negating their feelings of insecurity and fearfulness (Ceccato, 2017, para. 4). Some even negotiate the risks (Yang et al., 2018, p. 32). For them, travel risks are inevitable anyway. While this should not be the case, some women even accept harassment in transit or in the streets as “normal”, since they are already used to society’s objectification of women (Yang et al., 2018, p. 40).

It can be concluded that women in Southeast Asia are indeed more prone to be victims of violence in public transport. These occurrences limit the rights and freedoms of women, thereby impeding their movement “as they avoid certain places, times, routes, and modes of public transportation” (Kacharo et al., 2022, para. 2). Furthermore, violence against women seems to be normalized in public transport, thereby threatening the safety, security, and Southeast Asian women’s access to the mobility. These traveling restrictions, regrettably, hinder women’s social and economic activities, and access to resources and opportunities, which in turn amplify gender inequities further (Sur, 2015).

Development Motivations of Machine Learning Apps

As part of the SDGs (5, 11, 16), and in order to ensure the safety of women in public spaces, government agencies, nongovernmental organizations and women’s rights advocates have come up with initiatives involving the use of technology, particularly women safety apps. These initiatives seek to give women a sense of security amid threats of sexual harassment and violence in streets and public transport, while acknowledging the vulnerability of women in public spaces. Some Southeast Asian women already rely on other technological advancements such as the Internet, social media, and blogs to be empowered in transit (Ying et al., 2017, p. 51), but recent machine learning safety apps powered by AI take women’s safety to a whole new level.

In the Philippines, for example, the first prize of the Safe Cities Hackathon Professional Category (as part of the United Nations Women’s Safe Cities Global Flagship Programme) was a safety app called Dlock (United Nations Asia and the Pacific, 2016). Dlock features an easy-to-use lock screen with one-click buttons that allows users to send a message or call an emergency contact and ring a siren even when their phone screen is locked. The app can be used to report via message posting, connecting with other app users who are nearby. Dlock also activates distress calls for help and features a directory as well (which lists emergency hotlines, phone numbers of police stations, hospitals, and fire stations). The student category prize went to the ScAFE app, which shows its users the safe points in an area based on the user’s location. Users may also send messages to the nearest patrol area and inform their emergency contacts about their location through ScAFE.

The Office of Empowerment, Child Protection, and Population Control (Dinas PPAPP) launched a similar initiative in Jakarta, Indonesia as well. The mobile safety app Jakarta Aman has an emergency button feature integrated with emergency number 112 and other agencies in the DKI Jakarta provincial government. It also has a “Report” feature so that users may report incidents to government agencies, an “Important Numbers” feature which may be used in all regions in Indonesia, a “Secure Community” or e-Siskamling (night patrol) feature that informs the neighborhood if there are guests visiting within 24 hours, and a “Family Safe” feature that enables tracking of family members’ locations when traveling (CoHive 2019).

An Indonesian research project referred to as After Dark: Encouraging Safe Transit for Women Travelling at Night revealed that women’s risk of harassment in public spaces tends to increase during the evening hours thereby negatively affecting their mobility and travel choices. (Pulse Lab Jakarta, United Nations Women 2019). In response to After Dark’s recommendation, the ride-hailing company Gojek launched an initiative referred to as #AmanBersamaGojek (#SafeWithGojek). #AmanBersamaGojek improves security measures for its female customers (Mulia 2020). With the addition of the AI-powered Gojek SHIELD feature, customers’ safety is ensured with features such as phone number masking, chat intervention, and an emergency button connected to Gojek’s 24-hour customer care and emergency unit.

In Malaysia, the first women only ride-sharing online platform called Riding Pink was established in 2017. This platform was launched in response to the harassment that Malaysian women suffer from, such as unwanted texts or calls after a ride with a male driver, glares from male drivers, or even serious cases like bodily harm (Tan 2019). Riding Pink links women drivers and riders via Whatsapp and Facebook Messenger. Riding Pink aims to provide women a flexible source of income and a safer transportation alternative. As the service became more popular, it transitioned to a web-based platform, and a complete app-based ecosystem later on. The ride-sharing app includes essential safety features such as GPS tracking, and an SOS option that sends out distress messages and voice clips. A similar service was introduced in Thailand in 2021 by the ride-hailing app Grab, with its GrabCar (Lady) option. To help women feel safer, Grab users in Bangkok can now find female drivers as an additional option for their trips.

These are just a few of the apps developed that not only help keep women in Southeast Asia feel safe while in transit, but also save their lives every day. A pattern that may be seen in these apps, though, is that they are developed with the assumption that existing transport systems are unsafe for women, and that they should be ready in the event of violence or harassment. Furthermore, these apps give women the option to change their routes and routines, be it through avoiding unsafe places or riding female-only transportation. This option is, again presented, under the assumption that women are the ones who should avoid unsafe places or transportation wherein males are present. There is a normalization of the unsafetyness of women; therefore, women should adjust to these situations and take it upon themselves to install these apps to protect themselves against would-be perpetrators.

A Critique of these Apps

The aforementioned apps are designed based on the idea that a smart digital solution can deter would-be sex offenders and help women feel safer in public spaces. These apps, however, do not tackle the crucial matter of violence against women. The apps normalize violence and reinforce victim blaming mentalities rather than empowering women to take control of their mobility. Aside from assumptions of violence and rampant unsafetyness, the data sets derived for machine learning presupposes that someone be exposed to such unsafetyness first so that safer routes may be predicted. In one study, the majority of personal safety apps were focused on intervening at the time of a criminal event, and post-event. (Maxwell et al., 2020, p. 7). This means that women would already have been harassed or exposed to danger before the apps could actually protect them.

The apps, while helpful in easing the fear of women, reinforce a victim blaming mentality. The normalization of sexual violence along with the burden put on women to protect themselves contributes to a victim blaming culture wherein “the victim(s) of a crime or an accident is held responsible — in whole or in part — for the crimes that have been committed against them (Canadian Resource Centre for Victims of Crime, 2021).” In the case of the apps, the crimes that will be committed against them. Common examples of victim blaming in Southeast Asia include cases wherein women are blamed for the harassment because they dress in a certain way, have a certain body shape or personality. This type of victim blaming culture also prevents women from sticking to their usual travel routes and routines. Machine-learning platforms that segregate women commuters and drivers, on the other hand, provide short-term assurance of safety and protection from harassment. Such segregation deepens gender divides and reinforces gender inequality, making long-term equality between men and women more difficult to achieve. These segregation technological platforms are “red herring solutions to the deeper, more complicated question of the role of women in a rapidly changing society” (Sadiq 2017).

Another related matter to the above, is the “normalization of constant surveillance, sharing of personal information to third parties, and promotion of the idea that privacy should be sacrificed for increased safety” of potential victims (Maxwell et al., 2020, pp. 4–5). Furthermore, women safety apps with a tracking feature normalizes the concept that others have a right to know where a woman is at all times. This may also lead to another type of violence against women wherein abusers may stalk women and limit their movement and mobility. A question that can be raised, therefore, is whether the existing apps address the fundamental issue of gender inequality which causes violence and harassment, i.e. deeply ingrained attitudes toward the treatment of women, and the notion that women need protection from men, and harmful gender norms of masculinity.

It is not surprising then, that women safety apps do not have the power to decrease incidents of sexual harassment (Sheikh and Fayyaz 2019). The apps do not reduce the vulnerability of women to victimization (Maxwell et al., 2020, p. 1). Even with their use, women still do not feel substantially safer because they still fear traversing public spaces (Sheikh and Fayyaz, 2019, p. 6). Rather than being empowered to make their own commuting or transportation choices, existing apps make women adjust to their possible would-be offenders. One study even claimed that these apps “provide an illusion and false sense of security” that would put more women in potential danger (Maxwell et al., 2020, p. 9). For these reasons, women are continuously being conditioned to not go out at night, walk in dark areas, or ride in male-driven taxis. For women who have more financial freedom, they may drive their own private vehicles, but they have to likewise avoid areas that are deemed unsafe. The burden is put on the women, and they are considered responsible for their own safety. The irony here is that they already have the lack of control with their personal safety (Maxwell et al., 2020, p. 3), and yet, they are expected to be careful, and are thereby blamed for not being careful. In some cases, victims may even be blamed for not having or using these safety apps!

Existing women safety apps do not truly empower women to reclaim their mobility. Women are still restricted in their mobility and active participation in city life whenever they must negotiate the risks of sexual violence in ordinary situations everyday (García-Carpintero and de Diego-Cordero, 2020, pp. 6-7). If AI interventions were to work, they must focus on the empowerment of Southeast Asian women to gain control over their safety.

Rethinking Models for Empowerment

“We should try something new rather than being stuck with the wrong answer.”

-Seo Dal Mi, Start-Up

More than just the challenge of developing technological advancements through AI to keep women safe, the real challenge is rethinking the motivational models behind these interventions. Since existing apps do not substantially reduce the vulnerability of women to victimization while in transit, there is a need to redirect frameworks to ensure that the women in Southeast Asia gain empowered mobility. These revised AI frameworks should not be based on normalizing violence but rather highlighting women’s strengths and reducing their vulnerabilities. Proposed below are frameworks that challenge conceptions of women’s mobility which may be considered as motivations for AI development.

One of the main critiques mentioned in the previous section is the presumed burden of women to protect themselves against would-be perpetrators. The function of these apps is almost similar to the idea of bringing peppermint spray (or a gun even) to use in case one is violently attacked. Aside from the unnecessary burden put on vulnerable groups, statistics and experiences reaffirm that violence is the norm. If this type of victim-blaming thinking and the normalization of violence were to be revised, the safety of transit systems and the burden of protection should not be on the potential victims but on the institutional systems that should protect them. Revising this line of victim-blaming thinking further, the perpetrators and potential abusers are the ones who should be in fear (rather than the victims) since they are the ones who are in violation of women’s rights anyway!

Such a framework of thinking can be traced back to the Take Back the Night movement wherein 2000 women stepped in the streets of North America in the 70s and demanded for their right to move freely (especially at night) without fear of sexual violence. A huge influence on the movement, Andrea Dworkin, in The Night and Danger (1979), asserted that women’s struggle for freedom should begin with the fight for freedom of mobility or movement. Dworkin (1979) discussed the risks that women continuously face every time they step out (or even when they are in their homes). Women are conditioned to believe that the “rules of civilized society” include restricting their mobilities (at night) because it is the norm for men to be dangerous (Dworkin, 1979, para. 2). Dworkin (1979, para. 8) proposed that the violence is enough, and it is time for women to “form a barricade” with their bodies. This barricade should be “formidable as the ocean is formidable”, and that “collective strength and passion and endurance” be used to “to take back this night and every night” (Dworkin, 1979, para. 8).

This frame of thinking is more proactive than passive, and it celebrates the collective strength of women (and those who wish to protect women). Rather than waiting for the violence to happen before one is saved (as in the apps which address occurring events or post events), the women themselves would already have taken action. Now, what if future AI-driven apps can empower women to shift the burden of fear to potential perpetrators? For example, rather than the apps normalizing the avoidance of dimly lit areas, male-driven taxis, etc., what if the apps discourage or prevent perpetrators from committing violent acts? One of the reasons why perpetrators “attack” women in transit is because they are perceived as vulnerable and weak. If AI were to “strengthen” women and girls, or revise that perception of weakness, would they still victimize these persons? Using Dworkin’s idea of collective strength and endurance, AI-driven apps can put the transiting female community into a proactive stance by, say for example, deterring violence before it happens by banding together through reporting suspicious-looking individuals in trains or buses, requests for security visibility, making suggestions for improved safety, etc. This idea, of course, should be in tandem with institutional systems, and their policies and interventions.

These frameworks, however, must be verified empirically to fully incorporate gender perspectives in AI development and transport systems in Southeast Asia. For example, in New York City, study shows that women feel safer with the visibility of uniformed/non-uniformed police or transit personnel rather than electronic surveillance (Buckley, 2016, p. 45). Another finding suggests that aside from “having more eyes on the street to reduce crime intentions'', there is a need for street improvement in connection to the frequency of women’s use of public transportation (Harumain et al., 2021b, p. 109). This means that using technology in improving the security and women’s freedom should involve building inclusive and safe spaces, such as mapping urban spaces of fear where young women’s empowerment and reflective freedom can be developed (García-Carpintero and de Diego-Cordero, 2020, pp. 12). Of course, empirical studies as bases for AI development should also reconsider the environmental and individual factors that influence victim selection. The local community, therefore, is part of this “collective strength”, wherein it ensures that the environment is perceived to be a safe place for women, and an unsafe place for would-be perpetrators to commit their acts of harassment and sexual violence. Nathaniel Buckley (2016, p. 47) refers to this as “reversing the bystander effect” wherein the normalization of sexual violence is addressed. So, rather than women avoiding the bus, we “push the harassers off the bus and make transit spaces safer” (Buckley, 2016, p. 47).

Recommendations for Reclaiming Mobility

The fundamental question is about AI’s power to actually “strengthen” or truly empower women so that women can “take back” their mobility. Revised frameworks of thinking for new AI models that are not based on normalizing violence but rather empowering women to be “strong girls” to reclaim their mobility were proposed above. These frameworks, however, should be examined empirically on individual and institutional levels, so that they may be accurate bases for AI development (Maxwell et al., 2020, p. 11). After all, women must be consulted on transport planning be it through participatory methods such as interviews and focus groups. Distinct demographics and locations of women should also be considered as well. The unique circumstances of Southeast Asian transit systems and local communities, and other socio-economic and socio-cultural factors are also relevant in conceptualizations of machine learning models. A theory used by Mireia Brugulat and Lluís Coromina (2021, p. 644), for example, looks at the sociocultural, personal, practical and spatial constraints of solo female travelers in Southeast Asia. Surveys and data sets are, therefore, necessary in rethinking strategies and informing future AI-powered apps.

The “strong girl AI” challenge is open to investors, developers, designers, educators, engineers, programmers, coders, and other stakeholders. This goes with the hope that one day AI-powered safety apps will truly empower Southeast Asian women in their mobility, make them “stronger”, and become authentic extensions of women's individual and collective strengths (rather than their vulnerabilities).

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