A survey of algorithmic recourse: contrastive explanations and consequential recommendations
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Actionable recourse in linear classification
Classification models are often used to make decisions that affect humans: whether to
approve a loan application, extend a job offer, or provide insurance. In such applications …
approve a loan application, extend a job offer, or provide insurance. In such applications …
Performative prediction
When predictions support decisions they may influence the outcome they aim to predict. We
call such predictions performative; the prediction influences the target. Performativity is a …
call such predictions performative; the prediction influences the target. Performativity is a …
Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges
The combination of increased availability of large amounts of fine-grained human behavioral
data and advances in machine learning is presiding over a growing reliance on algorithms …
data and advances in machine learning is presiding over a growing reliance on algorithms …
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
Fairness is not static: deeper understanding of long term fairness via simulation studies
As machine learning becomes increasingly incorporated within high impact decision
ecosystems, there is a growing need to understand the long-term behaviors of deployed ML …
ecosystems, there is a growing need to understand the long-term behaviors of deployed ML …
Sok: Security and privacy in machine learning
Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
Towards the science of security and privacy in machine learning
Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
How to talk when a machine is listening: Corporate disclosure in the age of AI
Growing AI readership (proxied for by machine downloads and ownership by AI-equipped
investors) motivates firms to prepare filings friendlier to machine processing and to mitigate …
investors) motivates firms to prepare filings friendlier to machine processing and to mitigate …