297 citations · 447 across the 17 of their papers we have counts for
17 papers
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
Galit Shmueli, David Martens, Jaewon Yoo +1
Counterfactuals play a pivotal role in the two distinct data science fields of causal inference (CI) and explainable artificial intelligence (XAI). While the core idea behind count…
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…
Good intentions, unintended consequences: exploring forecasting harms
Bahman Rostami-Tabar, Travis Greene, Galit Shmueli +1
Organizations worldwide that rely on data-driven approaches regularly employ forecasting methods to enhance their planning and decision-making processes. While extensive research h…
Monetizing Explainable AI: A Double-edged Sword
Travis Greene, Sofie Goethals, David Martens +1
Algorithms used by organizations increasingly wield power in society as they decide the allocation of key resources and basic goods. In order to promote fairer, juster, and more tr…
Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue
Travis Greene, Amit Dhurandhar, Galit Shmueli
In response to growing recognition of the social impact of new AI-based technologies, major AI and ML conferences and journals now encourage or require papers to include ethics imp…
Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools
Travis Greene, Galit Shmueli, Jan Fell +2
Big data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision making. Yet different, equally-j…