Navigating the Cyber Minefields
Participation in "Navigating the Cyber Minefields" panel at Bioneers 2023 conference with Cindy Cohn and Randima Fernando, moderated by Kellen Klein.
Blog
Research, Writing, Talks and News on the intersections of technology and mental health.
Ethics in AI
Innovit (Italian Innovation and Culture Hub)
Geriatric Data Science: Life after Senior
Normconf 2022
Responsible AI in Practice: Lessons from Experience at scale
IBIS 2022 Keynote Talk
Algorithmic amplification of politics on Twitter
Based on a massive-scale experiment involving millions of Twitter users, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left.
Algorithmic Amplification of Politics on Twitter
Brown Data Science Initiative 2022
Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics
In recent years, many examples of the potential harms caused by machine learning systems have come to the forefront. Practitioners in the field of algorithmic bias and fairness have developed a suite of metrics to capture one aspect of these harms.
Past and Next Steps for the FAccTRec
FAccTRec panel 2021
From Optimizing Engagement to Measuring Value
Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of "value" that is worth optimizing for.
Challenges in Building the RecSys 2020 Dataset
OpenMined PriCon 2020
Assessing demographic bias in named entity recognition
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition (NER) systems for English across different demographic groups with synthetically generated corpora.
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network platforms, aiming to maximize user satisfaction along with other key business objectives. The implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent.