Topic Proposals: Due by midnight on class day
Key Principle: "Less can be more" - avoid comprehensive coverage in favor of deep analysis
Evaluation Criteria: Evidence of critical thinking over summarization
Discussion Work: Pre-work and post-work discussions on Slack, including question preparation for guest speakers
The more data, the surer we fool ourselves - quantity cannot compensate for poor quality
Large samples can show statistical significance for practically meaningless differences
Balance AI assistance with critical thinking development and academic integrity
Market monitoring and political interpretation with source verification
RAG-powered databases for research references and quotes
AI solutions for underserved communities with cultural sensitivity
Color identification for color blindness and real-time support
Social media assistance with brand consistency and authenticity
AI tutoring with specialized tools like Khan Academy's Khanmigo
Digital safe houses for vulnerable populations with privacy features for dangerous situations and culturally sensitive AI responses.
False positives in AI detection affecting innocent students
Difficulty citing AI-generated content without clear sources
AI detection tools disproportionately flagging non-native speakers
Training data includes copyrighted material without attribution
"The more data, the surer we fool ourselves" - quantity cannot compensate for poor quality
Historical Example: 1936 Literary Digest poll incorrectly predicted Alf Landon's victory due to biased sampling (telephone/car owners skewed wealthy)
Every individual has equal probability of selection
Regular interval selection maintaining randomness
Randomly select groups, sample from selected clusters only
Sample from ALL subgroups to ensure representation
Only 35.8% chance of actually having COVID with positive test
Probability of seeing results as extreme or more extreme, assuming no true effect
P-value is NOT the probability the hypothesis is true
Arbitrary benchmark, not a magical cutoff point
Submit analysis plans before data collection
Make materials publicly available
Repeat important findings
Treat p-values as continuum
Always collected by specific people for specific purposes
Shaped by social context and available resources
Complex phenomena reduced to categorical variables for computer processing
Concerns about data ownership transfer highlight the importance of ethical data collection and responsible long-term data management practices.
Most AI models trained primarily on Western data, affecting performance across cultures
Humans often can't explain their own decisions - AI systems similarly lack transparency
AI systems often amplify existing human biases in hiring decisions
AI insights combined with human oversight for optimal decision-making
AI-generated introductions tailored to individual students
Interactive chat-based learning support
Personalization based on student background and interests
Ability to switch screens if being monitored
Accommodating diverse linguistic backgrounds
Pre-work preparation, question submission, and Slack discussions
This comprehensive session successfully bridged traditional statistical concepts with contemporary AI challenges, providing students with both foundational knowledge and practical frameworks for navigating an AI-integrated future. The class explored real-world applications of AI across diverse fields while emphasizing the critical importance of data quality, ethical considerations, and responsible AI development practices.