Talk Data: Transparency and Accountability | Online
About this event
Transparency and Accountability: Case Studies Important to Implementing Ethical Data Science
Overview: At this monthly event series, we host thought-leaders from the Austin data community to discuss the possibilities that data brings to life. They’ll cover how today’s wealth of data drives business and product decisions across a range of industries, and share their visions for the future.
This month, we're excited to host New Knowledge's Computational Disinformation Analyst, Scott Tarlow.
We live in an unprecedented era of computationally curated experiences: from our news, social media feeds and even entertainment. These recommendations are often made by complex machine learning models that are not transparent in their decision making. This talk will present a synthesis of case studies in modern statistics and data science and frame them in questions surrounding the potential ethical crossroads they come across. Furthermore, the talk will argue that building transparent and accountable data science workflows can lead to better computational decisions.
Takeaways
- The current landscape of data collection for use in machine learning on human data
- Specific case studies where statistics or machine learning influenced human behavior
- The importance of transparency and accountability in data science
- What current professional and academic groups are doing to institutionalize ethical data science
