162 citations · 585 across the 42 of their papers we have counts for
7 papers · 1 filter
Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich
In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness.…
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
Andrew Bell, Oded Nov, Julia Stoyanovich
Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors. M…
An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring
Alene K. Rhea, Kelsey Markey, Lauren D'Arinzo +5
Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing,…
Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Falaah Arif Khan, Eleni Manis, Julia Stoyanovich
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most o…
Teaching Responsible Data Science: Charting New Pedagogical Territory
Julia Stoyanovich, Armanda Lewis
Although numerous ethics courses are available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively…
A Nutritional Label for Rankings
Ke Yang, Julia Stoyanovich, Abolfazl Asudeh +3
Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dat…
Synthetic Data for Social Good
Bill Howe, Julia Stoyanovich, Haoyue Ping +2
Data for good implies unfettered access to data. But data owners must be conservative about how, when, and why they share data or risk violating the trust of the people they aim to…