#RedLight: Using Tech to Support Sex Workers
#RedLight: Using Tech to Support Sex Workers
Role: UX and Strategy Lead
Key contributions: User research, UX strategy, wireframes, interactive prototypes
At a Glance
As part of a Hackathon-for-Good, I worked with a team of developers, strategists, and designers to design solutions addressing prostitution in Singapore.
With the advent of online media, vice syndicates have taken their businesses online to widen client-reach, while hiding behind the anonymity of the Internet. In the past, support workers could reach out to the sex workers along the streets of Singapore’s infamous red-light districts; however, the sex industry's shift to the online marketplace has made prostitution a more hidden and discreet business, often disenfranchising sex workers and closing off their access to help and support.
Situation: Sex workers are leaving popular red-light districts to sell their services online.
Project Task: Tamar Village, the NGO we worked with, offers offline physical, emotional, and spiritual support to these sex workers. We needed to find a way to bridge the gap between the offline support offered by Tamar Village and the online struggles faced by sex workers.
As we dug deeper into the problem, we realized that there were 2 gaps we needed to address before we could meaningfully designing solutions.
Qualitative and quantitative data on sex workers and the state of the sex industry in Singapore is still not publicly available. Without sufficient information on the motivations and needs of the workers, we could not design solutions that would meaningfully address their needs e.g. a sex worker desperate for financial stability would require a very different kind of support compared to a sex worker longing for companionship.
To this end, we developed #FINDMYSTORY, a neural network (A.I.) that renders advertisements of the sex services from several websites to gain a deeper insight into the ladies in the trade.
The social impact industry has not been spared from technology's disruptions. Since the industry’s shift online, the sex workers are now beyond Tamar Village’s reach; NGOs are unable to provide support in the same ways as they had done in the past.
To this end, we designed #HEARMYSTORY, an online community space for sex workers to share stories, build community, and seek support. They can also connect with a counsellor via a chatbox on the platform.
My team and I have taken this project beyond the hackathon and have turned it into a long-term pro-bono endeavour. We have begun discussions for Phase 2 of #FINDMYSTORY and #HEARMYSTORY.
The solutions that we designed for the hackathon are part of Phase 1 and address the lack of support and protection for sex workers in Singapore. The neural network serves as foundational data for us to gain a deeper understanding of our personas. Through the insights gathered on our personas, we will continually refine our solutions to ensure that they address our users' pain points.
The online platform acts as a short-term solution targeting tech-savvy sex workers between ages 16-25. If the platform proves to be successful in our usability tests, we will iterate it into a long-term solution. If, however, it fails and/or the neural network uncovers further insight, we will design for a different solution.
There is whole lot of secrecy that surrounds prostitution in Singapore. While prostitution is legal, pimping, public solicitation, and owning a brothel are not. However, the government unofficially tolerates and monitors a limited number of brothels. Given the tensions between the de facto and de jure laws, prostitution remains very polarized in the country. By and large, society still frowns upon prostitution, but there have been a growing number of groups advocating for prostitution to be recognized as a socially-acceptable form of work.
The culture of secrecy and taboo acts as a veil that hides the forms of abuse that the sex workers face. Women are often subjected to unsafe sex, with clients resorting to violence to get their way. Others exploit the financial vulnerability of the workers by offering a higher price to do away with the use of a condom. Further, clients falsely assume that their payments grant them the authority to disrespect the established boundaries set by the sex workers. More often than admitted, sex workers also experience various forms of non-consensual sexual activities i.e. rape, but are unable to speak out about it due to fear of negative cultural stigmatization from other sex workers.
Moreover, sex workers do not have access to the law due to the ambiguity of the legality of their work in Singapore. Non-Singaporean sex workers run the risk of being charged for sexual solicitation or face the risk of deportation if they do indeed report the crimes committed against them.
While there is scant data publicly available on sex workers in Singapore, there may be up to 7,000 active sex workers in Singapore on any given day, a majority of whom coming from Thailand, Vietnam, Indonesia, and China.
We hit several road bumps early in our research. With scant data on the sex industry, we struggled with defining our personas; they were a myriad of groups e.g. non-Singaporeans (i.e. Thais, Vietnamese, Indonesians, Chinese) sex workers, and the Singaporean sex-workers (which can be broken down into the older crowd and the younger university students). We struggled with defining their motivations and needs, and in turn, their pain points and potential solutions addressing those points.
Thus, the neural network was birthed as an idea early in our research; we urgently needed to begin building a repository of data that did not yet currently exist. While we were aware that the A.I. capabilities of the neural network would help us develop a deeper understanding of the sex workers in Singapore, we also needed qualitative data to uncover the nuances of the trade and the differing motivations of each sex worker.
We were fortunate enough to have several ex-sex workers present with Tamar Village at the hackathon. We conducted user interviews with those comfortable and willing to share their experiences. The nature of this project and the sensitivities unique to sex workers meant that we had to be extra gentle in our approach.
We also heavily relied on our user interviews with the NGO workers at Tamar Village and online research.
Given the time constraints of the hackathon, we chose to focus on designing solutions for the persona group that had been most commonly overlooked: Singaporean students in the sex trade.
The Singaporean society has long pegged sex workers as foreigners from nearby nations, seeking other forms of higher-paid employment in Singapore, overlooking the local and younger population involved in the trade. This persona group is also Tamar Village's biggest challenge since it is made up of a tech-savvy individuals exclusively selling their services online.
Our problem was thus to bridge the gap between the online sex workers and Tamar Village's offline support.
We designed our solutions in two parts: #FINDMYSTORY, a longer-term solution, and #HEARMYSTORY, a shorter-term solution.
An A.I. neural network renders the advertisements that women post online, in order to gain a deeper insight into their triggers, motivations, and needs. Presently, it has rendered around 6,000 online posts.
The neural network runs on artificial intelligence technology.
The neural network provides 3 kinds of information:
1. z-axis: Maps the number of occurrence of a particular word i.e. university
2. x-axis: Maps the number of times the word correlates with another word i.e. university (student) with businessman
3. y-axis: Maps the context of the correlation of words used i.e. university student selling their services exclusively for businessmen
The neural network is key to our work as it enables us to decode the language used by sex workers. The ladies in the sex industry use slangs and coded language in their work. Thus, the network enables us to map the number of times certain words are associated with others and in the process, sense-make the meanings of the code words. This allows us to track the patterns of interactions between the sex workers and their clients and also map out anomalies and abuse cases.
A community space for younger sex workers to anonymously share and exchange stories, build community, and offer support to one another. It also connects sex workers to a counsellor via a chatbot.
This platform also serves to educate younger women on the realities of working in the sex industry in Singapore.
Conduct usability tests for #HEARMYSTORY. Based on its successes/failures, we will conduct several rounds of iteration and/or design a more impactful solution
Design customized solutions for the other persona groups i.e. foreign sex workers living in Singapore, older Singaporean sex workers, sex workers working under pimps
Conduct deep ethnographic work to gather more meaningful qualitative data and better understand our persona groups
Run A.I. neural network on more websites and posts to generate more quantitative data
UX is heart work. More than just hard work, UX preaches the value of empathy. If we get deep about empathy, it is about intellectually and emotionally entering into the shoes’ of another. This project required hard heart work as we scoured the web reading posts and adverts of women as young as 15 (which is below the legal age for sexual intercourse i.e. 16). We also learnt that sex workers mainly used commercial property sites to sell their services – which brought up larger epistemological and ethical dilemmas about the human body as a site for capital exploitation. However, this was one of my favorite UX projects that I worked on as it showcased UX’s intersection with and importance in bringing about social change.
UX requires deep listening skills. Conducting user interviews on a sensitive issue comes with it a whole layer of complexities. While I needed deep listening skills to glean insights from my users for the project, I also needed to practice deep listening as lending a listening ear to a friend as they vulnerably bare their hearts to me. Deep listening was also important in working meaningfully together as a team with a myriad of skills, backgrounds and expectations.