The paper "Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR" by Sandra Wachter, Brent Mittelstadt, and Chris Russell explores the concept of counterfactual explanations in the context of automated decision-making and the EU General Data Protection Regulation (GDPR). The authors argue that while the GDPR does not explicitly grant a right to explanation, counterfactual explanations can serve as a means to provide meaningful insights into automated decisions without fully opening the "black box" of algorithmic systems. Counterfactual explanations are statements that describe how a different set of input values would have led to a different outcome, providing data subjects with a basis to understand, contest, or alter future decisions. The paper discusses the historical context of counterfactuals, their use in AI and machine learning, and their potential advantages in explaining automated decisions. It also examines the challenges and limitations of current approaches to explainability, such as local models and adversarial perturbations. The authors propose that unconditional counterfactual explanations, which are applicable to both positive and negative decisions, can bridge the gap between the interests of data subjects and data controllers, enhancing trust and transparency in algorithmic decision-making.The paper "Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR" by Sandra Wachter, Brent Mittelstadt, and Chris Russell explores the concept of counterfactual explanations in the context of automated decision-making and the EU General Data Protection Regulation (GDPR). The authors argue that while the GDPR does not explicitly grant a right to explanation, counterfactual explanations can serve as a means to provide meaningful insights into automated decisions without fully opening the "black box" of algorithmic systems. Counterfactual explanations are statements that describe how a different set of input values would have led to a different outcome, providing data subjects with a basis to understand, contest, or alter future decisions. The paper discusses the historical context of counterfactuals, their use in AI and machine learning, and their potential advantages in explaining automated decisions. It also examines the challenges and limitations of current approaches to explainability, such as local models and adversarial perturbations. The authors propose that unconditional counterfactual explanations, which are applicable to both positive and negative decisions, can bridge the gap between the interests of data subjects and data controllers, enhancing trust and transparency in algorithmic decision-making.