If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This guidance explains how algorithms and artificial intelligence can lead to disability discrimination in hiring. The Department of Justice enforces disability discrimination laws with respect to state and local government employers. The Equal Employment Opportunity Commission (EEOC) enforces disability discrimination laws with respect to employers in the private sector and the federal government. The obligation to avoid disability discrimination in employment applies to both public and private employers. Employers, including state and local government employers, increasingly use hiring technologies to help them select new employees.
This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.
To help organizations overcome these challenges, the World Economic Forum collaborated with over 50 experts in HR, data science, employment law, and ethics to create a practical toolkit for the responsible use of AI in this field. The toolkit begins with a guide that provides: an overview of AI in HR including a short primer on how AI works; describes key areas of concern including data privacy, bias, and transparency and explainability; and covers the steps to adopting AI-based HR tools including forming an assessment team, evaluating the risk of a tool, implementation, and monitoring. The toolkit then provides two checklists that are linked to each section of the guide. The first checklist is for the evaluation of a specific tool. The second checklist focuses on broader questions of strategic planning and the development of policies and procedures.
If you've spent any time in the tech industry, you've no doubt heard quite a bit about artificial intelligence (A.I.) and machine learning. Based on that chatter, you might think companies everywhere are trying to fill many thousands of roles that utilize artificial intelligence. As a new CompTIA analysis of U.S. Bureau of Labor Statistics (BLS) data makes clear, a rising number of jobs in "emerging tech" deal with A.I. In December 2021, some 13 percent of all "emerging tech" job postings mentioned A.I. as a necessary component of the job (and "emerging tech" job postings constitute roughly 30 percent of all tech job postings). In states like California and Texas, where enormous tech companies are looking for highly specialized talent (including A.I. experts), there are thousands of A.I.-related job openings every month; in other states, the average number generally drops to several hundred.
The U.S. Bureau of Labor Statistics reported that 4 million Americans quit their jobs in July 2021. Termed "The Great Resignation," the mass exodus of employees from the workplace is perhaps the reflection of deep dissatisfaction in the past work culture. You may have heard of a concept called time poverty – when you have too much to do and too little time to do it. In 2020, about 80 percent of American workers persistently felt time-poor, and the concept wasn't just in their heads. It is a well-known fact that most employees have been overburdened over the years.
Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.
Become a Computer Vision Engineer by completing our 12 weeks Online Computer Vision course. The course covers state-of-the-art algorithms in object detection, image classification, object tracking. Applications include Medical diagnosis, E-commerce, Recommendation Systems, Robotics etc. According to the United States Bureau of Labor Statistics, jobs for computer and information research scientists are expected to grow by 15% between 2019 and 2029. The average annual compensation for a Computer Vision Engineer is $124,000 where the top earners earn more than $175,000 per year.
With New York City's passage of one of the toughest U.S. laws regulating the use of artificial intelligence tools in the workplace, federal officials are signaling that they too want to scrutinize how that new technology is being used to sift through a growing job applicant pool without running afoul of civil rights laws and baking in discrimination. The use of that new technology in hiring and other employment decisions is growing, but its volume remains hard to quantify, and the regulations aimed at combating bias in its application may be difficult to implement, academics and employment attorneys say. "Basically, these are largely untested technologies with virtually no oversight," said Lisa Kresge, research and policy associate at the University of California, Berkeley Labor Center, who studies the intersection of technological change and inequality. We have rules about pesticides or safety on the shop floor. We have these digital technologies, and in virtual space, and that ...
The New York City Council voted 38-4 on November 10, 2021 to pass a bill that would require hiring vendors to conduct annual bias audits of artificial intelligence (AI) use in the city's processes and tools. Companies using AI-generated resources will be responsible for disclosing to job applicants how the technology was used in the hiring process, and must allow candidates options for alternative approaches such as having a person process their application instead. For the first time, a city the size of New York will impose fines for undisclosed or biased AI use, charging up to $1,500 per violation on employers and vendors. Lapsing into law without outgoing Mayor DeBlasio's signature, the legislation is now set to take effect in 2023. It is a telling move in how government has started to crack down on AI use in hiring processes and foreshadows what other cities may do to combat AI-generated bias and discrimination.
Sign up for the daily Marketplace newsletter to make sense of the most important business and economic news. When a new law in New York City takes effect at the start of 2023, employers won't be allowed to use artificial intelligence to screen job candidates unless the tech has gone through an audit to check for bias. The potential for algorithmic discrimination in hiring has been the target of state laws in Illinois and Maryland. The federal Equal Employment Opportunity Commission also recently formed a working group to study the issue. The internet has made applying for jobs easier than ever, but it's also made the process less human, said Joseph Fuller at Harvard Business School. "When you open the faucet, all of a sudden a lot of applications started coming in, and no one's gonna hit print 250 times," he said.