The adoption of artificial intelligence, data science, data analytics, among other techniques is predominant in many contexts and domains: often used to help us decide which items to buy, what music to listen to, and in high-stakes domains such as education, healthcare provision or criminal justice, among others. The performance of such AI systems depends both on the learning algorithms, as well as the data used for their training and evaluation. The role of the algorithms is well studied. In contrast, research that focuses on the data used in AI systems is not commonplace. Data, however, is always at their core, being a crucial component for advancing and assessing the technological field.
In the first edition of these seminar series, we explored a number of examples of how crowd computing can be leveraged to either debug noisy training data in machine learning systems, understand which machine learning models are more congruent to human understanding in particular tasks, or to advance our understanding of how AI systems can influence human behavior.
In this second edition on the topic of "Responsible Use of Data", we take a multi-disciplinary view and explore further lessons learned from success stories and examples in which the irresponsible use of data can create and foster inequality and inequity, perpetuate bias and prejudice, or produce unlawful or unethical outcomes. Our aim is to discuss and draw certain guidelines to make the use of data a responsible practice.
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