Ideas at the intersection of data, research & learning.
Two perspectives, connected by a love of learning.
Explore the people, ideas, and questions behind our journal of data science, research, and teaching.
When Algorithms Are Not Fair
The content discusses algorithmic bias in artificial intelligence, highlighting how biases can emerge through historical data, representation issues, and model design. It emphasizes the importance of fairness at all stages of AI development, explores various real-world implications, and advocates for ongoing evaluation and human oversight to ensure equitable outcomes across diverse populations.
Before the Algorithms, There Was Data
The discussion emphasizes the significance of comprehending data in the age of artificial intelligence. While new algorithms dominate conversations, understanding what the data represents is crucial. Using examples like student datasets, the article illustrates different data types and encourages readers to classify their own datasets, enhancing their data interpretation skills.
What Does It Mean for AI to Be Ethical?
The discussion on AI ethics highlights the importance of fairness, privacy, transparency, accountability, and human autonomy in AI systems. While AI can enhance efficiency, it may also perpetuate biases and inequalities. Responsible AI requires careful data selection, evaluation, and governance to ensure outcomes serve human needs without causing harm.