50 citations · 131 across the 21 of their papers we have counts for
23 papers
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Annie Ulichney, Amanda Coston
Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making. Two common sources…
Falsifying Discriminant Validity of Predictive Algorithms
Amanda Coston
Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended. These exposés highlight the need to iden…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use
Anna Kawakami, Amanda Coston, Hoda Heidari +2
As public sector agencies rapidly introduce new AI tools in high-stakes domains like social services, it becomes critical to understand how decisions to adopt these tools are made…
Predictive Performance Comparison of Decision Policies Under Confounding
Luke Guerdan, Amanda Coston, Kenneth Holstein +1
Predictive models are often introduced to decision-making tasks under the rationale that they improve performance over an existing decision-making policy. However, it is challengin…
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A…
Examining risks of racial biases in NLP tools for child protective services
Anjalie Field, Amanda Coston, Nupoor Gandhi +4
Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be refl…
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research…
Counterfactual Prediction Under Outcome Measurement Error
Luke Guerdan, Amanda Coston, Kenneth Holstein +1
Across domains such as medicine, employment, and criminal justice, predictive models often target labels that imperfectly reflect the outcomes of interest to experts and policymake…
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu +1
A growing literature on human-AI decision-making investigates strategies for combining human judgment with statistical models to improve decision-making. Research in this area ofte…
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
Ashesh Rambachan, Amanda Coston, Edward Kennedy
Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an ident…
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…
The role of the geometric mean in case-control studies
Amanda Coston, Edward H. Kennedy
Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily availa…
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
Amanda Coston, Anna Kawakami, Haiyi Zhu +2
Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improv…
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston, Ashesh Rambachan, Alexandra Chouldechova
Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings. Often multiple predictive models deliver similar overall performance but differ…
Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
Amanda Coston, Neel Guha, Derek Ouyang +3
Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet litt…
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
George H. Chen, Linhong Li, Ren Zuo +2
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationship…
Counterfactual Predictions under Runtime Confounding
Amanda Coston, Edward H. Kennedy, Alexandra Chouldechova
Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal…
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D
Maria De-Arteaga, Tejumade Afonja, Amanda Coston
This is the proceedings of the 3rd ML4D workshop which was help in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference.
Conditional Learning of Fair Representations
Han Zhao, Amanda Coston, Tameem Adel +1
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification…
Counterfactual Risk Assessments, Evaluation, and Fairness
Amanda Coston, Alan Mishler, Edward H. Kennedy +1
Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases,…
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact
Maria De-Arteaga, Amanda Coston, William Herlands
This is the Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact, held in Montreal, Canada on December 8, 2018