With just one month until International Women’s Day 2022 on the theme “Break the Bias”, María Santos Alfageme calls for action to solve transport data’s gender bias

Despite decades of investing in better analytics and storage systems, many organisations still struggle to make optimal use of their data. Data enables governments to govern, and good data enables governments to govern efficiently (International Transport Forum, 2021). However, the adoption of data-driven approaches in policymaking comes with several challenges. These include having an under-skilled workforce, digitalising valuable data trapped on printed paper, or accounting for citizens who do not produce digital data. Too often, the public sector has an abundance of data that is underutilised or yet to be discovered (GovTech, 2021). Not harnessing this “dark data” is partly responsible for our gender-blind transport policies.

Infinite examples of gender biases in the transport sector exist. Probably the most prominent ones are the design of default-male transport products like buses, bus straps, or seatbelts. Gender biased transport policies are even more worrying when we realise that, even if women represent the majority of public transport users, public transport planning has historically been shaped around the standard male A-to-B travel patterns. Or when we realise that samples used to determine vehicle safety standards make women 73% more likely than men to be seriously injured or die in a car crash. As we become increasingly reliant on artificial intelligence and different technologies to manage our databases, the need to address this bias is more urgent now than ever.

Achieving gender equality in transport requires quality, policy-relevant data on women and girls’ transport use. Without it, we cannot make informed decisions, and we cannot track if or how those decisions are improving lives. The good news is – we have that data! In recent years, there has been a proliferation of concepts underlining the gender bias in transport policies, like NYU’s “pink tax on transport “or Professor Inés Sánchez de Madariaga’s coined term “mobility of care “. The last two decades have seen public investments in projects to bridge the data collection gender gap (e.g. DIAMOND project, the Transport Innovation Gender Observatory, etc.), as well as free, self-paced training courses. These contribute to a growing body of evidence collating best practices to address this issue institutionally.

The International Transport Forum (ITF) works to better understand both female transport users and transport professionals. For instance, the ITF published a compendium highlighting positive examples of how women can benefit from the transformative innovations in the transport sector, proving that transport connectivity is a decisive factor in women’s empowerment. ITF will soon publish a Gender Analysis Toolkit for Transport Policies to help countries carry out their own gender analysis. Last year, ITF’s official platform for interaction with the corporate sector – the Corporate Partnership Board – held a workshop on gender bias in transport data, and a joint and public event with the Science Technology and Innovation Division at the OECD, entitled Addressing the Gender Bias in AI Data. These exchanges made it clear that without inclusive, purposeful public-private data partnerships between mobility operators and authorities, the biases of our systems risk being perpetuated.

These events were not aimed at data professionals, though. They served as an eye-opener to the social consequences of not addressing gender bias and the benefits that ethical, inclusive use of databases could bring to society. The adoption of data-driven approaches can increase objectivity, equity and fairness. And they will, if we ensure that the data we collect and use to design policies is representative by default.

Contrary to what many still claim, a gender perspective to transport planning is not ideological. It is about effectiveness. Not accounting for half of the population means that we will not deliver policies that serve all citizens. This is not only unfair and inequitable, but unsustainable. “Garbage in, garbage out” (GIGO) is a concept common to computer scientists and the tech industry to express that the quality of output is determined by the quality of the input. Since a computer processes what it is given, we must be mindful of producing policies that do not blindly perpetuate old injustices, as Caroline Criado-Perez upholds in her book Invisible Women.

The OECD March on Gender calls us to raise the bar for better policies for gender equality. Now is the best time to rethink and restructure the way we collect data and use it meaningfully. At the same time, new governance frameworks are being developed to effectively govern digital spaces. It would make sense to push to create systems that ensure a gender lens is embedded in all aspects of public policy. Let’s prioritise equity, and let’s not lose sight of heterogeneity within the “women” cluster. We must be ready to assess multi-variable realities that have an effect on travel patterns, such as income, race, background, occupation or age, to ensure a “global dialogue for better transport”.

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María Santos Alfageme is a Research Officer at the International Transport Forum’s Corporate Partnership Board