Search Results for author: Rayid Ghani

Found 17 papers, 10 papers with code

Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

no code implementations19 Mar 2024 Catalina Vajiac, Arun Frey, Joachim Baumann, Abigail Smith, Kasun Amarasinghe, Alice Lai, Kit Rodolfa, Rayid Ghani

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness.

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

no code implementations29 Sep 2023 Emily Black, Rakshit Naidu, Rayid Ghani, Kit T. Rodolfa, Daniel E. Ho, Hoda Heidari

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data.

Fairness

A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions

no code implementations12 Jul 2022 Ka Ho Brian Chor, Kit T. Rodolfa, Rayid Ghani

The ML framework guides how child welfare agencies might conceptualize a target problem that ML can solve; vet available administrative data for building ML; formulate and develop ML specifications that mirror relevant populations and interventions the agencies are undertaking; deploy, evaluate, and monitor ML as child welfare context, policy, and practice change over time.

BIG-bench Machine Learning

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

no code implementations24 Jun 2022 Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani

Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their design, resulting in limited conclusions of methods' real-world utility.

Experimental Design Fraud Detection

An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

1 code implementation13 May 2021 Hemank Lamba, Kit T. Rodolfa, Rayid Ghani

Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems.

BIG-bench Machine Learning Fairness

Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy

1 code implementation5 Dec 2020 Kit T. Rodolfa, Hemank Lamba, Rayid Ghani

Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities.

BIG-bench Machine Learning Fairness

Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions

2 code implementations27 Oct 2020 Kasun Amarasinghe, Kit Rodolfa, Hemank Lamba, Rayid Ghani

The contribution is 1) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and 2) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

BIG-bench Machine Learning

Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis

1 code implementation27 Aug 2020 Isabelle Tingzon, Niccolo Dejito, Ren Avell Flores, Rodolfo De Guzman, Liliana Carvajal, Katerine Zapata Erazo, Ivan Enrique Contreras Cala, Jeffrey Villaveces, Daniela Rubio, Rayid Ghani

Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history.

Computers and Society

Bandit Data-Driven Optimization

1 code implementation26 Aug 2020 Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

In this paper, we introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points.

BIG-bench Machine Learning

Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions

1 code implementation24 Jan 2020 Kit T. Rodolfa, Erika Salomon, Lauren Haynes, Ivan Higuera Mendieta, Jamie Larson, Rayid Ghani

The criminal justice system is currently ill-equipped to improve outcomes of individuals who cycle in and out of the system with a series of misdemeanor offenses.

Decision Making Fairness

A Clinical Approach to Training Effective Data Scientists

no code implementations15 May 2019 Kit T. Rodolfa, Adolfo De Unanue, Matt Gee, Rayid Ghani

Like medicine, psychology, or education, data science is fundamentally an applied discipline, with most students who receive advanced degrees in the field going on to work on practical problems.

Aequitas: A Bias and Fairness Audit Toolkit

2 code implementations14 Nov 2018 Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, Rayid Ghani

Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics.

BIG-bench Machine Learning Decision Making +1

Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks

1 code implementation9 May 2018 Avishek Kumar, Syed Ali Asad Rizvi, Benjamin Brooks, R. Ali Vanderveld, Kevin H. Wilson, Chad Kenney, Sam Edelstein, Adria Finch, Andrew Maxwell, Joe Zuckerbraun, Rayid Ghani

A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure.

Applications

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