A gender perspective on on-demand transport
Harassment on crowded public transport limits women's access to work and education. At the 2022 International Mobility Congress in Sitges, data scientist Stephi Kobsar explained what on-demand transport can change, and why its algorithms must not inherit the biases in past data.
The 2022 edition of the International Mobility Congress (IMC) took place last October in the city of Sitges, south of Barcelona. The IMC is a professional meeting point to understand new mobility and make it safe, inclusive, and sustainable in the context of public and private collaboration that facilitates achieving these global objectives.
In this year's edition, we participated in multiple ways, including the presence of Data Scientist & Analyst Ms. Stephi Kobsar in the session on Safe and Inclusive Mobility, during which renowned specialists presented their vision and different experiences of the subject.
Ms. Kobsar provided her gender perspective on on-demand transport. She commented that “In our current cities, congestion in public transport vehicles places female passengers more at risk of suffering episodes of violence, since the spaces are not supervised at all times by transport network workers. In addition, the absence in some cases of emergency buttons for riders, or the lack of clear codes of conduct, contribute to violence and harassment of women.”
Gender-based violence and harassment is a top concern in many emerging markets in the public transport sector, where three in five women have reported harassment. For example, up to 81% of female public transport passengers in Baku, 78% in Karachi, 64% in Mexico, and 55% in Mumbai have experienced harassment on their daily commutes. In this context, it is our duty to humanize daily commutes and care about the wellbeing of each passenger.
In the Global North, fortunately, cases of extreme harassment are not frequent. However, we cannot overlook or allow invasions of space or inappropriate comments which, although they may not affect women physically, are rated as very serious by female users.
“When we get on a bus, we take it for granted that everything will work properly and everyone will behave, but that's not always the case,” Ms. Kobsar added, “On-demand transport is based on algorithms that match and pool travelers for each origin and destination. But algorithms learn from the data of the past, data that has not always taken into account the perspective of gender. Therefore, there is a whole ethical part in which we must clean the data of past biases and give them a more modern, more feminine perspective. Because if we continue to feed the algorithms with data from the past, we are going to eternally perpetuate these sexist biases, not only from a gender perspective but also from a racial or ethnic perspective or any other type,” she concluded.
We want on-demand transport to give women better access to economic and educational opportunities. That means designing services, and the data behind them, with women's experience in mind, and working with the operators who run them on driver training and clear codes of conduct.
