How ethical is your data?

Data ethics is pivotal. You may have noticed examples of questionable methodology in data work from invasive approaches to gathering data for facial recognition to flawed crime data used in predictive policing models, and the issues can arise from them.

Although many of us aren't anywhere near data like this, we are still concerned about the projects we work on can be poorly handled or misinterpreted. Both of these can have profound ethical implications.

How can a junior data professional start conversations about ethical approaches to analytics, especially when they have tiny to no decision-making power in their organization? Strategies such as attempting to advocate problems with an existing project or proactively seeking to prevent future mishaps have proven useful in starting the necessary conversations.

Detecting Pain Points

Before beginning, it is essential to know the kinds of data you, your team, and your organization work with or rely on. You should also be aware of the type of reporting and analytics your team create. This practice of reflection personalizes conversations and refrains people from engaging with such topics as abstract concepts. It is more likely to encourage actionable steps to embed ethics in existing workflows.

For example, when you analyse data on student performance or engagement along with demographic variables such as race/ethnicity, income, and gender, it's vital to ask compact questions.

Making Allies

Whether you're attempting to advocate an ongoing ethical quandary or forestall potential problems at your work, you need support. There are two key points when it comes to choosing allies.

• Start within your own team and those who are from data team.

• Invite non-technical professionals at your organization whose projects focus heavily on analytics.

At any organization, data should be critically discussed and engaged with, not treated like an objective solution only available for experts.

Engage with the Audience

As you traverse these conversations, it's essential to bring the right frame to each team for technical professionals. It is the best way to approach conversations focusing on a mixture of ethics and methodology. In recent years, many of the ethical issues we've come across in data science cause harm, and it is the most critical issue to address. Even if a member from your team is careless about your approach is causing damage or risking inaccurate outputs, he/she may care that it represents a potential legal or PR threat.

Be Patient

Needless to say, this kind of work takes time, and with that comes pros and cons. The only disadvantage is that you may have to watch a flawed project and go through multiple iterations before any changes are made. And the advantage is just having the conversation can have lasting positive results.

If an ethical issue is raised, people are more likely to embed an honest approach into new projects during their development which goes a long way in preventing new problems from becoming part of the status quo.

Keeping at it

Each organization is different with varying levels of enthusiasm for tackling such issues. However, using an ethical framework to data work is not an abstract concept at the end of the day. Reflection on how your work can be improved and creating buy-in collaboratively to meet these goals is possible. The work is pivotal, and there will be roadblocks which make it challenging to use data to tell accurate stories to avoid cause harm.