Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Loftus, Joshua"'
Autor:
Loftus, Joshua
Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism--an insistence on only using "correct" models--slows the adoption of c
Externí odkaz:
http://arxiv.org/abs/2406.02275
Counterfactuals and counterfactual reasoning underpin numerous techniques for auditing and understanding artificial intelligence (AI) systems. The traditional paradigm for counterfactual reasoning in this literature is the interventional counterfactu
Externí odkaz:
http://arxiv.org/abs/2401.13935
Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we de
Externí odkaz:
http://arxiv.org/abs/2303.04209
Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being mo
Externí odkaz:
http://arxiv.org/abs/2212.03974
Autor:
Rosenblatt, Lucas, Herman, Bernease, Holovenko, Anastasia, Lee, Wonkwon, Loftus, Joshua, McKinnie, Elizabeth, Rumezhak, Taras, Stadnik, Andrii, Howe, Bill, Stoyanovich, Julia
Differential privacy (DP) data synthesizers support public release of sensitive information, offering theoretical guarantees for privacy but limited evidence of utility in practical settings. Utility is typically measured as the error on representati
Externí odkaz:
http://arxiv.org/abs/2208.12700
A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continu
Externí odkaz:
http://arxiv.org/abs/2107.00593
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search results to college admissio
Externí odkaz:
http://arxiv.org/abs/2006.08688
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative
Externí odkaz:
http://arxiv.org/abs/1806.02380
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understanding of causal approaches, argue
Externí odkaz:
http://arxiv.org/abs/1805.05859
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are u
Externí odkaz:
http://arxiv.org/abs/1703.06856