St Cross Special Ethics Seminar: Not for me: On the external function of guilt

The standard way of thinking about emotions in cognitive science starts with their function. The function of the fear program, for instance, is to help the individual evade imminent dangers. This functionalist proposal illuminates the character of the fear program, e.g., the kinds of things that elicit fear, and the kinds of responses that fear produces. The functionalist approach has been extremely productive, but it faces a puzzle with the emotion of guilt, for it’s unclear what function the guilt program serves for the individual.

The role of business journalism in a financial crisis

Nobel Economist Joseph Stiglitz was former Chief Economist at the World Bank, and founder of the Initiative for Policy Dialogue. He is credited with formulating ideas such as risk aversion, the Henry George theorem and the information asymmetry. This seminar will include expert contributions from Said Business School's accounting professor, Amir Amel-Zadeh, and Sunday Times City Editor Jill Treanor.

日本語の役割語における性差の表現 Expressing Gender Differences in Japanese Role Language

Abstract:

話者の性、年齢、職業・階層、出身地等と深く結び付いた話し方(発話スタイル)を役割語(role language)と呼ぶ。役割語は日本のポピュラーカルチャーで広く用いられているので、日本のマンガ・アニメや小説などの理解のためには、役割語の知識が必須である。役割語の
中でも、特に男女の性差を表す表現が発達しているので、その特徴を分析するとともに、社会言語学的な観点から問題点を指摘する。

Sanjaya Lall Fund Lecture 2023: "Creating Equality of Opportunity: New Insights from Big Data"

PROFESSOR RAJ CHETTY
SANJAYA LALL VISITING PROFESSOR 2023
William A. Ackman Professor of Economics at Harvard University

Director of Opportunity Insights



Raj Chetty is the William A. Ackman Professor of Economics at Harvard University and the Director of Opportunity Insights, which uses big data to study the science of economic opportunity: how we can give children from all backgrounds better chances of succeeding? Chetty’s work has been widely cited in academia, media outlets, and policy discussions in the United States and beyond.

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling.
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