In response to the dearth of scholarship surrounding responsible data science (RDS), NYU CDS faculty are paving the way with a course dedicated to RDS and the publication of their pedagogical methodology.
While a significant amount of attention and research has addressed individual privacy concerns in private companies’ datasets, data owners and publishers also want to avoid revealing certain patterns — even in anonymized datasets — that might compromise a competitive advantage or perpetuate discrimination against any group of people. Data published by urban transportation companies is highly valuable for research, policy, and public accountability.
Philadelphia’s mayor and staff have pushed for evidence-based decision making through their collaboration GovLaBPHL, which studies human interaction, but are they making steps to ensure unbiased data?
The Automated Decision Systems Task Force, created by New York Mayor Bill de Blasio, will create procedures for assessing the city’s algorithmic tools. The task force is primarily made up of people with expertise in tech law and public policy. There are only a handful of people that have technical expertise in computer science and I’m one of those people. I’m looking forward to using my research insights specifically on operationalizing ethics in algorithms and data to come up with actionable recommendations for agencies.
Today, Mayor de Blasio announced the creation of the Automated Decision Systems Task Force which will explore how New York City uses algorithms. The task force, the first of its kind in the U.S., will work to develop a process for reviewing “automated decision systems,” commonly known as algorithms, through the lens of equity, fairness and accountability.
Virtually every interaction we have with a public agency creates a data point. Amass enough data points and they can tell a story. However, factors like privacy, data storage and usability present challenges for local governments and researchers interested in helping improve services. In this installment of MetroLab’s Innovation of the Month series, we highlight how researchers at Data Responsibly are addressing those challenges by creating synthetic data sets for social good.
Stoyanovich’s diversity of experience makes her a perfect fit to be a member of the Program Committee for the 4th annual Data for Good Exchange (D4GX), to be held Sunday, September 24, 2017 at Bloomberg’s New York headquarters. The conference explores the use of data science for social good, and brings together data scientists from industry and academia with not-for-profits and government entities seeking to use modern machine learning and data science methods to address challenges in the public and non-profit sectors.
La plupart des données et de la puissance d’analyse sont concentrées dans les mains de quelques entreprises, ce qui leur donne les moyens d’éliminer toute concurrence dans des pans entiers de l’économie. Une poignée de sociétés contrôlent toutes nos données personnelles, déterminent quelles informations nous sont proposées, et orientent la plupart de nos décisions, portant potentiellement atteinte à notre vie privée et à nos libertés. Avec l’analyse de données massives, le big data, ces entreprises disposent d’un pouvoir énorme.