This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. The growing use of artificial intelligence will likely lead to biased and discriminatory outcomes for minorities and disabled people, several groups warned the federal government this week. The National Artificial intelligence Advisory Committee, an interagency group led by the Commerce Department, held a public hearing online Tuesday aimed at informing policymakers about how the government can best manage the use of AI. Panelists were told by most of the witnesses that bias and discrimination are the biggest fears for the people they represent. Patrice Willoughby, vice president of policy and legislative affairs at the NAACP, told panelists that technology has already been used as a means to disenfranchise and mislead voters, and said her group worries about AI for the same reason.

As artificial intelligence (AI) continues to play a significant role in various industries and aspects of daily life, the issue of bias in AI algorithms has become increasingly prevalent. Biased AI systems can perpetuate existing social inequalities and lead to unfair treatment, creating a critical need for addressing and mitigating discrimination in machine learning applications. Bias in AI can originate from several sources, such as biased training data, lack of diversity in AI development teams, and biased algorithms themselves. Training data is the foundation of any AI system, and if the data used to train an algorithm contains biases, those biases will be passed on to the AI system. For example, biased facial recognition systems have been found to misidentify people of color at a higher rate than white individuals, leading to wrongful arrests and other consequences.

Mitigating bias: AI systems can perpetuate and amplify bias in their training data, which can lead to unfair or discriminatory outcomes. To mitigate bias, it's important to actively identify and address sources of bias in the data and algorithms used to train AI systems. Transparency: AI systems should be transparent in their decision-making processes, so that users can understand how they arrived at a particular decision or output. This can help users to identify and correct any errors or biases in the system. Accountability: AI systems should be designed and implemented in a way that makes it possible to hold individuals and organizations responsible for their actions.

Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions. Accordingly,enterprises are increasingly embracing this dynamic technology. A 2022 global study by IBM found that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a 2021 PwC study the COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises' impact on workforce planning, supply chain resilience, and demand projection.

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).

The company and law firm names shown above are generated automatically based on the text of the article. We are improving this feature as we continue to test and develop in beta. We welcome feedback, which you can provide using the feedback tab on the right of the page. July 8, 2021 - The FTC has long exercised its authority to regulate private sector uses of personal information and algorithms that impact consumers. That authority stems from Section 5 of the FTC Act (Section 5), the Fair Credit Reporting Act (FCRA) and Equal Credit Opportunity Act (ECOA).

Artificial intelligence is becoming a major feature of educational practice and policymaking, but researchers are beginning to raise critical questions about its ethics and effects. Artificial Intelligence (AI) has become the subject of both hype and horror in education. During the 2020 Covid-19 pandemic, AI in education (AIed) attracted serious investor interest, market speculation, and enthusiastic technofuturist predictions. At the same time, algorithms and statistical models were implicated in several major controversies over predictive grading based on historical performance data, raising serious questions about privileging data-driven assessment over teacher judgment. In the new special issue AI in education: Critical perspectives and alternative futures published in Learning, Media and Technology, Rebecca Eynon and I pulled together a collection of cutting edge social scientific analyses of AIed.

Employed across industries, AI applications unlock smartphones using facial recognition, make driving decisions in autonomous vehicles, recommend entertainment options based on user preferences, assist the process of pharmaceutical development, judge the creditworthiness of potential homebuyers, and screen applicants for job interviews. AI automates, quickens, and improves data processing by finding patterns in the data, adapting to new data, and learning from experience. In theory, AI is objective--but in reality, AI systems are informed by human intelligence, which is of course far from perfect. Humans typically select the data used to train machine learning algorithms and create parameters for the machines to "learn" from new data over time. Even without discriminatory intent, the training data may reflect unconscious or historic bias. For example, if the training data shows that people of a certain gender or race have fulfilled certain criteria in the past, the algorithm may "learn" to select those individuals at the exclusion of others.

As we empower machines to make critical decisions about who can access vital opportunities, we need to prevent discriminatory outcomes. After all, machine learning is only a tool. The responsibility falls on people use it wisely – especially the people leading the way in its advancement, from corporate leaders down to system engineers. In other words, we need to design and use ML applications in a way that not only improves business efficiency but also promotes and protects human rights. But the nature of ML technology – its ubiquitousness, complexity, exclusiveness and opaqueness – can amplify longstanding problems related to unequal access to opportunities.