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Found 30 papers, 11 papers with code
Sparse Autoencoders for Hypothesis Generation
We describe HypotheSAEs, a general method to hypothesize interpretable relationships between text data (e. g., headlines) and a target variable (e. g., clicks).
Learning Disease Progression Models That Capture Health Disparities
Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases.
User-item fairness tradeoffs in recommendations
Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization.
A No Free Lunch Theorem for Human-AI Collaboration
The result does suggest one model of collaboration with guarantees, where one agent identifies "obvious" errors of the other agent.
Addressing Discretization-Induced Bias in Demographic Prediction
In particular, we show that argmax labeling, as used by a prominent commercial voter file vendor to impute race/ethnicity, results in a substantial under-count of African-American voters, e. g., by 28. 2% points in North Carolina.
Wisdom and Foolishness of Noisy Matching Markets
Each student has a true value $v$, but each college $c$ ranks the student according to an independently drawn estimated value $v + X_c$ for $X_c\sim \mathcal{D}.$ We ask a basic question about the resulting stable matching: How noisy is the set of matched students?
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing
Decision-makers often observe the occurrence of events through a reporting process.
Domain constraints improve risk prediction when outcome data is missing
Though our case study is in healthcare, our analysis reveals a general class of domain constraints which can improve model estimation in many settings.
Reconciling the accuracy-diversity trade-off in recommendations
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories).
Interface Design to Mitigate Inflation in Recommender Systems
The test resulted in a substantial improvement in user rating behavior and a reduction in item quality inflation.
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
Many recommender systems optimize a linear weighting of different user behaviors, such as clicks, likes, and shares.
Coarse race data conceals disparities in clinical risk score performance
Across outcomes and metrics, we show that the risk scores exhibit significant granular performance disparities within coarse race groups.
Supply-Side Equilibria in Recommender Systems
Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content.
Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis
Algorithms provide powerful tools for detecting and dissecting human bias and error.
Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing
A major concern is that residents do not report problems at the same rates, with heterogeneous reporting delays directly translating to downstream disparities in how quickly incidents can be addressed.
Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential
To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits.
Fair ranking: a critical review, challenges, and future directions
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring.
Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT
Alzheimers disease is a fatal progressive brain disorder that worsens with time.
Strategic Ranking
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals.
The Stereotyping Problem in Collaboratively Filtered Recommender Systems
First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users.
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks
Herein a working prototype of a BCI-based wheelchair is detailed that can navigate inside a typical home environment with minimum structural modification and without any visual obstruction and discomfort to the user.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates
Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election correctly recovers the asymptotic outcome.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force.
Designing Optimal Binary Rating Systems
Modern online platforms rely on effective rating systems to learn about items.
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words.
A Bayesian Model of Multilingual Unsupervised Semantic Role Induction
We propose a Bayesian model of unsupervised semantic role induction in multiple languages, and use it to explore the usefulness of parallel corpora for this task.
