Key takeaways

• How our datasets and algorithms share societies historical social biases.
• Why this could cause results to be inaccurate and further exaggerate existing discrimination.
• How we can assess the impact and change this from a risk into a powerful solution.

The adoption of Machine Learning in decision making has amplified the risk of socially-biased outcomes.Anyone (not just data scientists) working on ML tools holds immense power over shaping the future of our world. However, we can use this power for good and train models that help to drive positive social change sooner. This talk will provide context for this issue, explore real-world examples and discuss ideas for potential solutions. Together let's discover: - How our datasets and algorithms share societies historical social biases. - Why this could cause results to be inaccurate and further exaggerate existing discrimination. - How we can assess the impact and change this from a risk into a powerful solution.