I School Lectures

How might AI reshape the sensory norms and moral economies of discernment of American mental healthcare?

The death by suicide of disabled Black teen Sewell Setzer III and his family’s lawsuit against chatbot company Character.AI have opened up renewed debate and uncertainty about AI safety.

Algorithms and AI impact access to work and other essential resources, especially for low-income people. Lily Irani describes  the policies, practices, and algorithms of suspicion that control workers’ access to wages and work on digital platforms.

Venture capital investors push nascent tech firms to scale as quickly as possible to inflate the value of their asset. The gains generated by tech startups are funneled into the pockets of a small cadre of elite investors and entrepreneurs, leaving workers and users to bear many of the costs and risks.

Biobanks and electronic health records systems are increasingly used to train and develop machine learning and artificial intelligence models, raising concerns for social equity and justice.

Professor Deirdre K. Mulligan was principal deputy U.S. chief technology officer at the White House Office of Science and Technology Policy and director of the National Artificial Intelligence Initiative Office (NAIIO) in the Biden-Harris Administration.

Who should make decisions about ethical and responsible technology deployments? And how do impacted communities make political claims over data technologies?

When identifying organ transplant recipients — and in other matching problems — is it better to find a match more quickly, or more slowly and carefully? Afshin Nikzad’s research weighs the tradeoffs in different circumstances.

Can we combine data from satellites, mobile phones, and financial services providers with machine learning to identify the neediest people and better target humanitarian aid?

Timothy Tangherlini uses a computational folkloristic approach to analyze conversations on the social media platform Parler leading up to the January 6th attack on the Capitol.

Jevin West breaks down the threats of scientific disinformation, predatory publishing and pseudoscience, the reproducability crisis, and generative AI.

Narges Mahyar discusses community-centered tools that empower the general public to engage in real-world sociotechnical problems.

Diag Davenport outlines an unexpected pattern of bias underlying officers’ choices to use or not use an algorithmic risk score.

Analyses of police misconduct rely heavily on self-reported law-enforcement data. Dean Knox proposes a research algorithm to deal with unreliable and distorted data.

Alex Chohlas-Wood demonstrates two ways that data science and information technology can improve outcomes in the criminal legal system.

Jennifer Allen estimates how much curbing misinformation could benefit public health.

Cornelia Ilin gives a high-level introduction to the transformer model architecture, using bidirectional representations from transformers (BERT) on electronic medical health records to predict pediatric patients’ diagnosis codes.

Ali Montazeralghaem researches recommender systems, deep learning and reinforcement learning methods, and natural language processing.

The creator of open-source projects FinRL, ElegantRL, and FinGPT outlines the deep learning revolution and his experiences applying it to the challenging domain of the financial market.

Arvind Satyanarayan explores the future of human/machine partnerships.