Modeling agent decision and behavior in the light of data science and artificial intelligence
Agent-based modeling (ABM) has been widely used in numerous disciplines and practice
domains, subject to many eulogies and criticisms. This article presents key advances and …
domains, subject to many eulogies and criticisms. This article presents key advances and …
Recent advances in artificial intelligence and tactical autonomy: Current status, challenges, and perspectives
This paper presents the findings of detailed and comprehensive technical literature aimed at
identifying the current and future research challenges of tactical autonomy. It discusses in …
identifying the current and future research challenges of tactical autonomy. It discusses in …
How to design AI for social good: Seven essential factors
Abstract The idea of Artificial Intelligence for Social Good (henceforth AI4SG) is gaining
traction within information societies in general and the AI community in particular. It has the …
traction within information societies in general and the AI community in particular. It has the …
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim - arxiv preprint arxiv:1702.08608, 2017 - arxiv.org
As machine learning systems become ubiquitous, there has been a surge of interest in
interpretable machine learning: systems that provide explanation for their outputs. These …
interpretable machine learning: systems that provide explanation for their outputs. These …
Algorithmic bias: Senses, sources, solutions
S Fazelpour, D Danks - Philosophy Compass, 2021 - Wiley Online Library
Data‐driven algorithms are widely used to make or assist decisions in sensitive domains,
including healthcare, social services, education, hiring, and criminal justice. In various …
including healthcare, social services, education, hiring, and criminal justice. In various …
It's just not that simple: an empirical study of the accuracy-explainability trade-off in machine learning for public policy
To achieve high accuracy in machine learning (ML) systems, practitioners often use complex
“black-box” models that are not easily understood by humans. The opacity of such models …
“black-box” models that are not easily understood by humans. The opacity of such models …
Considerations for evaluation and generalization in interpretable machine learning
F Doshi-Velez, B Kim - Explainable and interpretable models in computer …, 2018 - Springer
As machine learning systems become ubiquitous, there has been a surge of interest in
interpretable machine learning: systems that provide explanation for their outputs. These …
interpretable machine learning: systems that provide explanation for their outputs. These …
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
Child welfare agencies across the United States are turning to data-driven predictive
technologies (commonly called predictive analytics) which use government administrative …
technologies (commonly called predictive analytics) which use government administrative …
Data science as political action: Grounding data science in a politics of justice
B Green - Journal of Social Computing, 2021 - ieeexplore.ieee.org
In response to public scrutiny of data-driven algorithms, the field of data science has
adopted ethics training and principles. Although ethics can help data scientists reflect on …
adopted ethics training and principles. Although ethics can help data scientists reflect on …
The false promise of risk assessments: epistemic reform and the limits of fairness
B Green - Proceedings of the 2020 conference on fairness …, 2020 - dl.acm.org
Risk assessments have proliferated in the United States criminal justice system. The theory
of change motivating their adoption involves two key assumptions: first, that risk …
of change motivating their adoption involves two key assumptions: first, that risk …