Algorithms & code are governing more and more of our lives. It has never been more important for us to take a step back as developers, students & designers and think about some tough ethical questions

• Could you fight the temptation of huge profits but what you'd create is ultimately detrimental to your users?
• Are our algorithms inclusive or do they perpetuate the disadvantage we'd wished to alleviate?

Join us for an evening when we consider these tough question & more!

Brought to you by GDG Polokwane & Cape Town

Kindly Sponsored by Prodigy Finance

The Slippery Slope of Unethical Programming by Jacques Smuts

Have you ever been asked to write code to fake an emissions test? Probably not, but most of us have encountered bits of unethical programming in our careers, whether we realize it or not.

This talk highlights some of the unethical coding I’ve done and regretted, some of the unethical requests I’ve refused, and what I’ve learned from these events. The talk will give people an idea of some of the ethical complexities involved in coding for a worldwide audience, as well as the lowdown techniques used to profit off your users without you even realizing it.

Finally, the talk will end with some advice for the audience, and thoughts on what it would take for an individual, a company and the industry to reduce unethical programming practices.

#UnbiasedAlgorithmsMatter by Shandu Nthai

Let's take a deep dive into the world of algorithmic bias and the real-world implications it has one all of our lives - particularly the lives of women and people of colour.

Algorithms, machine learning and big data are shaping more and more our lives and have tremendous potential to bring about tangible change and to empower communities all around the world.

Various AI tools are now being used to screen job candidates, assist in diagnosing diseases, help identify criminal suspects, and finally to help determine a person's credit score and their access to credit. They make these decisions more efficient, but do they make them fair? Or do they perpetuate the same human biases due to the data used to train them?

To answer these questions I’ll discuss a few case studies of where we got this right and where we fell horribly short. 

Sponsored by our friends at Prodigy Finance