Telegram from Bud Staley, Chairman of the NYNEX Corporation, to Geraldine Ferraro

Telegram from Bud Staley, Chairman of the Board of NYNEX, to Geraldine Ferraro. Includes standard response letter from Ferraro and data entry sheet.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_new_york/1251/thumbnail.jp

Accident investigation

The National Transportation Safety Board (NTSB) has attributed wind shear as a cause or contributing factor in 15 accidents involving transport-categroy airplanes since 1970. Nine of these were nonfatal; but the other six accounted for 440 lives. Five of the fatal accidents and seven of the nonfatal accidents involved encounters with convective downbursts or microbursts. Of other accidents, two which were nonfatal were encounters with a frontal system shear, and one which was fatal was the result of a terrain induced wind shear. These accidents are discussed with reference to helping the aircraft to avoid the wind shear or if impossible to help the pilot to get through the wind shear

Citizen Engineers: Leaders in Building a Sustainable World

As with the “citizen soldiers” of World War II, the engineering industry must produce “citizen engineers” who will accept the leadership challenge necessary to deliver a combination of technical, economic, social, and environmental values to its stakeholders that will truly improve people’s quality of life

A REALITY CHECK

Agricultural and Food Policy,

Exposing the Probabilistic Causal Structure of Discrimination

Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof of discrimination requires evidence of causality, the state-of-the-art methods are essentially correlation-based, albeit, as it is well known, correlation does not imply causation. In this paper we take a principled causal approach to the data mining problem of discrimination detection in databases. Following Suppes' probabilistic causation theory, we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data. The result is a type of constrained Bayesian network, which we dub Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based on random walks on top of the SBCN, addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism. Our experiments on real-world datasets confirm the inferential power of our approach in all these different tasks