1,029 research outputs found
Minimum Wages and Racial Discrimination in Hiring: Evidence from a Field Experiment
When minimum wages increase, employers may respond to the regulatory burdens by substituting away from disadvantaged workers. We test this hypothesis using a correspondence study with 35,000 applications around ex-ante uncertain minimum wage increases in three U.S. states. Before the increases, applicants with distinctively Black names were 19 percent less likely to receive a callback than equivalent applicants with distinctively white names. Announcements of minimum wage hikes substantially reduce callbacks for all applicants but shrink the racial callback gap by 80 percent. Racial inequality decreases because firms disproportionately reduce callbacks to lower-quality white applicants who benefited from discrimination under lower minimum wages
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Fairness and Bias in Algorithmic Hiring
Employers are adopting algorithmic hiring technology throughout the
recruitment pipeline. Algorithmic fairness is especially applicable in this
domain due to its high stakes and structural inequalities. Unfortunately, most
work in this space provides partial treatment, often constrained by two
competing narratives, optimistically focused on replacing biased recruiter
decisions or pessimistically pointing to the automation of discrimination.
Whether, and more importantly what types of, algorithmic hiring can be less
biased and more beneficial to society than low-tech alternatives currently
remains unanswered, to the detriment of trustworthiness. This multidisciplinary
survey caters to practitioners and researchers with a balanced and integrated
coverage of systems, biases, measures, mitigation strategies, datasets, and
legal aspects of algorithmic hiring and fairness. Our work supports a
contextualized understanding and governance of this technology by highlighting
current opportunities and limitations, providing recommendations for future
work to ensure shared benefits for all stakeholders
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
POVERTY LAWGORITHMS A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities
Automated decision-making systems make decisions about our lives, and those with low-socioeconomic status often bear the brunt of the harms these systems cause. Poverty Lawgorithms: A Poverty Lawyers Guide to Fighting Automated Decision-Making Harms on Low-Income Communities is a guide by Data & Society Faculty Fellow Michele Gilman to familiarize fellow poverty and civil legal services lawyers with the ins and outs of data-centric and automated-decision making systems, so that they can clearly understand the sources of the problems their clients are facing and effectively advocate on their behalf
Study of the South Carolina Department of Corrections
The purpose of this oversight study and investigation is to determine if agency laws and programs within the subject matter jurisdiction of a standing committee: are being implemented and carried out in accordance with the intent of the General Assembly; and should be continued, curtailed, or eliminated
Construction Contractors\u27 Audit Manual, Volume 1
https://egrove.olemiss.edu/aicpa_guides/2174/thumbnail.jp
Unemployment in Indiana
Meeting proceedings of a seminar by the same name, held February 17, 202
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