Artificial intelligence technology is now used by a growing number of companies looking to hire the best employees, but new research from Rice University warns how it can incorporate biases and overlook important characteristics among job applicants. The study explores the scientific, legal and ethical concerns raised by personnel selection tools that rely on AI technologies and machine learning algorithms. Authors Fred Oswald, a professor in the Department of Psychological Sciences at Rice University; Nancy Tippins of the Nancy T. Tippins Group, LLC, and independent researcher S. Morton McPhail reviewed the use of this technology. Oswald says that AI technology--which includes games, video-based interviews and data mining tools--can save time in the job application process and the screening of potential employees. But he believes the effectiveness of these tools is questionable.

Collision avoidance is a vital capability of any marine vessel navigating in public waterways; this is particularly true for autonomous surface vehicles (ASVs), which cannot benefit by the real-time guidance of a human operator. Safe maritime navigation remains a challenge due to the fact that it requires the seamless coordination of multiple complex subsystems. First, vessels must be able to perceive their surroundings under a wide range of environmental conditions. This is typically accomplished using one or more line-of-sight sensors, which emit electromagnetic or acoustic signals, and detect the reflections produced by nearby obstacles (Robinette et al., 2019). However, in the marine environment, vessels can also utilize the Automatic Information System (AIS) protocol to track nearby vessels. The merits and drawbacks of these sensing modalities will be discussed in Section 1.1. Once an obstacle is detected, the ASV must react quickly and intelligently to avoid it, in accordance with the "rules of the road" set forth by the 1972 International Regulations for Prevention of Collisions at Sea (COLREGs) (International Maritime Organization, 2003). Many ASVs remain unable to perform one or more of these crucial tasks, limiting their adoption beyond the oceanographic research community. B. Cole is with the Laboratory for Autonomous Marine Sensing Systems, Department of Mechanical Engineering.

The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans (e.g., users, designers and developers, manufacturers, legislators). However, the relevant discussions around meaningful human control have so far not resulted in clear requirements for researchers, designers, and engineers. As a result, there is no consensus on how to assess whether a designed AI system is under meaningful human control, making the practical development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying four actionable properties which AI-based systems must have to be under meaningful human control. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human's ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue these four properties are necessary for AI systems under meaningful human control, and provide possible directions to incorporate them into practice. We illustrate these properties with two use cases, automated vehicle and AI-based hiring. We believe these four properties will support practically-minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control and responsibility.

We focus on some shortcomings in current data protection regulation's ability to adequately address the ramifications of AI-driven data processing practices, in particular those of combining data sets. We propose that privacy regulation relies less on individuals' privacy expectations and recommend regulatory reform in two directions: (1) abolishing the distinction between personal and anonymized data for the purposes of triggering the application of data protection laws and (2) developing methods to prioritize regulatory intervention based on the level of privacy risk posed by individual data processing actions. This is an interdisciplinary paper that intends to build a bridge between the various communities involved in privacy research. We put special emphasis on linking technical notions with their regulatory implications and introducing the relevant technical and legal terminology in use to foster more efficient coordination between the policymaking and technical communities and enable a timely solution of the problems raised.

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is the next big thing in #AI innovation? It is what dubbed as Real Superintelligence (RSI) Platform as the CyberEngine of the Metaverse. Today's AI is the statistic ML & DL & ANNs, involving big data, statistic learning theory, optimization, data science and analytics, automated software, GPUs. After 70-years trials and errors with symbolic, statistic, narrow, general and supreme AI, there emerges a real, true, genuine AI as Machine Intelligence and Learning (MIL), or Man-Machine Superintelligence. Man-Machine MetaIntelligence Human Intelligence Artificial Intelligence Machine Learning Deep Learning Data Analytics ML [DNNs DL ML] AI [NAI AGI ASI] DA MIL Global AI Real AI Real Man-Machine Superintelligence The RSI will allow computers to effectively and sustainably interact with the world taking in all of the world's information to solve any possible problems and come up with any possible solutions.

Cognitive bias commonly termed as human bias is a systematic thinking pattern that affects our judgments and decisions. One of the common examples of this bias that influences most of us is blaming external factors when something goes wrong. These human biases can be projected to AI technology even with good intentions. So our human biases become an element of the technology we fabricate in several ways. Artificial intelligence is being used extensively in many sensitive domains such as healthcare, criminal justice, and human resource.

Trailing the US and second-placed China, it holds a slight lead over Canada and South Korea, according to the Global AI Index published in December 2020 by Tortoise Media. Philp continues: "For businesses, we want to ensure that there are clear rules, applied ethical principles and a pro-innovation regulatory environment that can create tech powerhouses across the country." A survey published by Experian in September indicates that more than two-thirds (68%) of UK students wrongly believe that they would need to earn a STEM qualification to stand a chance of landing a data-related job. Dr Mahlet Zimeta, head of public policy at the Open Data Institute, thinks that the widely held view that "the UK needs to produce more people who can code" is unhelpful at best. For employers, this will include ensuring that their staff "have access to suitable training and development opportunities", he adds, pointing out that the government's online list of so-called skills bootcamps is an excellent place to start.Tortoise Media's Global AI Index ranks the UK fourth in the world on its supply of talent and third for the quality of its research.

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling.

We study fairness in the context of classification where the performance is measured by the area under the curve (AUC) of the receiver operating characteristic. AUC is commonly used when both Type I (false positive) and Type II (false negative) errors are important. However, the same classifier can have significantly varying AUCs for different protected groups and, in real-world applications, it is often desirable to reduce such cross-group differences. We address the problem of how to select additional features to most greatly improve AUC for the disadvantaged group. Our results establish that the unconditional variance of features does not inform us about AUC fairness but class-conditional variance does. Using this connection, we develop a novel approach, fairAUC, based on feature augmentation (adding features) to mitigate bias between identifiable groups. We evaluate fairAUC on synthetic and real-world (COMPAS) datasets and find that it significantly improves AUC for the disadvantaged group relative to benchmarks maximizing overall AUC and minimizing bias between groups.