Publications (12)
Dead Zone of Accountability: Why Social Claims in Machine Learning Research Should Be Articulated and Defended
Tianqi Kou, Dana Calacci, Cindy Lin
Many Machine Learning research studies use language that describes potential social benefits or technical affordances of new methods and technologies. Such language, which we call…
Interaction Context Often Increases Sycophancy in LLMs
Shomik Jain, Charlotte Park, Matt Viana +2
We investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user's values, preferences, and se…
FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations
Varun Nagaraj Rao, Samantha Dalal, Andrew Schwartz +3
What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platfo…
FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
Dana Calacci, Varun Nagaraj Rao, Samantha Dalal +5
Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found th…
Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy
Varun Nagaraj Rao, Samantha Dalal, Eesha Agarwal +2
Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designe…
Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use
Alex Berke, Robert Mahari, Sandy Pentland +2
Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understandin…
The tradeoff between the utility and risk of location data and implications for public good
Dana Calacci, Alex Berke, Kent Larson +2
High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant…
Breakout: An Open Measurement and Intervention Tool for Distributed Peer Learning Groups
Dana Calacci, Oren Lederman, David Shrier +1
We present Breakout, a group interaction platform for online courses that enables the creation and measurement of face-to-face peer learning groups in online settings. Breakout is…
Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers
Alex Berke, Dana Calacci, Alex +2
We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time,…
Surveillance, Spacing, Screaming and Scabbing: How Digital Technology Facilitates Union Busting
Frederick Reiber, Nathan Kim, Allison McDonald +1
Despite high approval ratings for unions and growing worker interest in organizing, employees in the United States still face significant barriers to securing collective bargaining…
Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers
Alex Berke, Kent Larson, Sandy Pentland +1
What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' persona…
Organizing in the Digital Age: Understanding Community, Challenges, and Consequences in Digitally-facilitated Labor Organizing
Frederick Reiber, Alishah Chator, Dana Calacci +1
The contemporary American labor force is highly dispersed, necessitating the use of digital communication tools to bridge spatial and temporal gaps in union organizing. This study…