Technology Management and Information Systems Seminars

In this presentation, I'll introduce the PerMed (Personalized Medicine) study, aiming to enhance early diagnosis of infectious respiratory diseases by integrating electronic medical records with behavioral data from smartphones and smartwatches. I'll discuss the study's motivation, goals, and design, and share key results from substudies, including predicting COVID-19 infections, evaluating vaccine safety, and assessing the impact of wars on individuals and sub-populations.

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individualization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can disproportionately affect members of identifiable groups, including women and people of color. Since algorithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency—transparency about the nature, uses, and consequences of algorithms—is both crucial and challenging; novel methods designed to open the algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on exploitation of an absence of information or of behavioral biases.

This seminar meeting will be a Q&A session with Tali, a behavioral economist and data scientist with vast industry experience. As the title suggests, Tali will talk about data science research and large scale experimentation taking place at Meta today. Please come prepared with questions for Tali to make this a truly engaging conversation!

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. Here, we identify 2,107 registered U.S. voters that account for 80% of fake news shared on Twitter during the 2020 U.S. presidential election by an entire panel of 664,391 voters. We find that supersharers are important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers have a significant over-representation of women, older adults, and registered Republicans. Supersharers’ massive volume does not seem automated but is rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many.

Time series forecasting is essential for decision making across industries such as
retail, energy, finance, and healthcare. However, developing accurate machine-learning-based forecasting models has traditionally required substantial dataset-specific tuning and model customization.
In a paper we have just posted to arXiv, we present Chronos, a family of pretrained time series models based on language model architectures.
Like large language models or vision-language models, Chronos is a foundation model, which learns from large datasets how to produce general representations useful for a wide range of tasks.
The use of pretrained models for time series forecasting is an exciting frontier. By reformulating the forecasting task as a kind of language modeling, Chronos demonstrates a simpler path to general and accurate prediction. Moreover, Chronos will be able to seamlessly integrate future advances in the design of LLMs. We invite researchers and practitioners to engage with Chronos, now available open-source, and join us in developing the next generation of  time series models