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Gender-inclusive data practices

In this video, course contributors Hera Hussain and Cami Rincon, discuss gender-inclusive practices for managing data.

In this video, Cami and Hera discuss that when we are designing gender-inclusive technologies we need to make specific decisions about how to handle stakeholders’ data and privacy.

We need to adopt ethical data practices, be transparent about our policies and give stakeholders autonomy over their data.

Here are the gender-inclusive considerations to managing data and privacy they discuss:

Inclusive onboarding

When we first interact with new technologies, whether they are web platforms or physical devices, we are often asked to disclose our gender, our name and our title. As Hera noted, it’s important to question whether having this information is necessary.

Does having data on the gender of your stakeholders positively improve your technology’s functionality? If the answer is no, considering not asking for this information.

If you decide it is necessary, then the process of obtaining it should be gender inclusive. Cami suggests processes that are flexible, giving people the option to not disclose or self-describe.

Minimum viable data

One of the ways we can build trust and minimise concerns around privacy is by gathering the minimum amount of data necessary and only storing that data for as long we need it. Ask yourself how much detail you actually need to achieve what you are trying to achieve.

If you are choosing to collect data from your stakeholders, can you justify why you are doing so? We should be using data solely for the betterment of our technologies, and working with minimum viable data forces us to be more efficient about the problems we are trying to solve with our data.

Remember, you always have the option to not collect any data at all.

So often when we consent to share our data with companies we are presented with a lengthy set of terms and conditions and the intricacies of what we are consenting to is hidden from us. If you are choosing to collect data from your stakeholders be clear with them about what you are asking to collect.

Explain to them how it is being collected, what it is being used for, and how long it will be stored. Help them to understand what the benefits of giving you their data are, justify why it is for the betterment of your technology.

Create active feedback loops with the people using your technologies so that you can show how the data they have provided you have improved their products and services. Doing so builds trust with your stakeholders.

Autonomy

Stakeholders should be given ultimate autonomy over their data. Present them with options, allow them to choose which aspects of their data they would like to provide if any at all. If you choose to collect data you should always try to consider how your technology will operate should someone consent to share nothing.

Stakeholders should have the option to delete their stored data at any time and have the flexibility to modify any relevant information should their situation change.

As Cami notes, this is particularly important when considering the privacy of trans and non-binary people, as they might not be comfortable in disclosing certain aspects of their lives.

Ethics

Within your team, think about creating a code of ethics as to how you will manage data and privacy. This allows you to have a set of guidelines that you can refer to if needs be. These guidelines can cover all the things mentioned above and be shared easily with your team.

You might also consider sharing them with your stakeholders. These guidelines should be subject to change through feedback. Make specific considerations around how and if data will be shared with any external company.

There are important ethical considerations that need to be made when sharing anyone’s information, always clearly disclose this in your privacy policy.

As Hera notes, personalisation should not be based on antiquated ideas of what a particular gender group wants to see or do and the selling of gendered data has been proven to contribute to gender stereotypes in target advertising

Further Reading and Resources:

  1. Catherine D’Ignazio and Lauren F. Klein, 2020. Data Feminism.
  2. The Engine Room – Responsible Data Policy
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Gender-Inclusive Approaches in Technology

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