Harm wrought by AI tends to fall most heavily on marginalized communities. In the United States, algorithmic harm may lead to the false arrest of Black men, disproportionately reject female job candidates, or target people who identify as queer. In India, those impacts can further impact marginalized populations like Muslim minority groups or people oppressed by the caste system. And algorithmic fairness frameworks developed in the West may not transfer directly to people in India or other countries in the Global South, where algorithmic fairness requires understanding of local social structures and power dynamics and a legacy of colonialism. That's the argument behind "De-centering Algorithmic Power: Towards Algorithmic Fairness in India," a paper accepted for publication at the Fairness, Accountability, and Transparency (FAccT) conference, which begins this week. Other works that seek to move beyond a Western-centric focus include Shinto or Buddhism-based frameworks for AI design and an approach to AI governance based on the African philosophy of Ubuntu.

The information presented in Lega-Tech Artificial Intelligence Market Report 2021 includes qualitative and quantitative insights. Under the qualitative analysis part, manufacturing base, raw materials data, Lega-Tech Artificial Intelligence status, trends, SWOT analysis, PESTEL Analysis, distribution channels, driving factors, and a competitive structure is presented. Under the qualitative analysis part, market value/volume, production analysis, consumption data, import-export data, or each region and country are explained. Also, industry size by Lega-Tech Artificial Intelligence type, application, demand and supply scenario, and economic status are explained. Also, comprehensive information on the latest product development, growth opportunities, industry strategies, cost structures, and recent policies are enlightened in the Lega-Tech Artificial Intelligence report.

What are the opportunities and threats faced by the vendors in the international market?

We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight matrices and identifying redundancy sources in the parameterization. We further apply the Tensor Train (TT) decomposition to the compact SVD components, and propose a non-redundant differentiable parameterization of fixed TT-rank tensor manifolds, termed the Spectral Tensor Train Parameterization (STTP). We demonstrate the effects of neural network compression in the image classification setting and both compression and improved training stability in the generative adversarial training setting.

The Isomap is a well-known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the computational complexity of the graphing stage has been investigated, yet the eigenvalue decomposition stage remains a bottleneck in the problem. In this paper, we propose the Low-Rank Isomap algorithm by introducing a projection operator on the embedded graph from the ambient space to a low-rank latent space to facilitate applying the partial eigenvalue decomposition. This approach leads to reducing the complexity of Isomap to a linear order while preserving the structural information during the dimensionality reduction process. The superiority of the Low-Rank Isomap algorithm compared to some state-of-art algorithms is experimentally verified on facial image clustering in terms of speed and accuracy.

There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.

Although growing numbers of organisations are working with artificial intelligence (AI) software in some shape or form, very few are generating significant financial benefits when rolling it out in a serious way, according to new research. A study conducted by the MIT Sloan Management Review and management consulting firm the Boston Consulting Group revealed that as many as 57% of the 3,000 managers, executives and academics questioned were currently either piloting or deploying the technology. A further 59% had devised an AI strategy and 70% believed they understood how the software could generate business value. Despite this situation, the report, Expanding AI's impact with organizational learning, indicated that just one in 10 organisations were deriving significant financial value from the technology. When exploring the reasons why, researchers found that simply getting the basics right – that is, having an appropriate strategy with the right supporting data, technology and skills in place – was not enough.

When you download a dating app, fill out a profile with some of your most private information, and select "allow app to access location" to locate nearby potential love interests, you may feel a little exposed, but you proceed anyway, in order to find those dates. But there is reason to believe that by using these sites, you may be unknowingly submitting to government tracking--and we can't know for sure because of all of the secrecy involved with deals that data brokers make with government agencies. It's yet another demonstration of the need to bring transparency to the data-collection industry. Dating apps ask users for a variety of highly personal information and retain it indefinitely, potentially forever. This can include photos and videos, text conversations with other users, and information on gender, sexual orientation, political affiliation, religion, desire to have children, location, HIV status, and beyond.

Public Service Commission (PSC) put out a job advert for 151 job vacancies and received 12,639 applicants in a span of one month. This does not only happen for government jobs. The high rate of unemployment has led to the mammoth applications for limited job vacancies yet there are about two human resource (HR) personnel to carry out recruitment in a company. In addition, the emergence of the coronavirus has shifted many companies from the traditional to virtual method of recruitment. However, a few questions come to mind; "Are all submitted applications read and scrutinised to the satisfaction of both the job applicant and employer? How long will that process last? Mr Benjamin Lubogo, leader of a recruitment team at Strategic Engagement Company Limited, says it depends on the human resource manager as well as the recruitment system used at the company. There are different ways of recruiting talent for companies including computer aided software, the traditional method of emailing and most recently artificial intelligence. In Uganda, he says, the most common method of recruitment is the traditional emailing method because of its affordability compared to computer aided software and artificial intelligence. With the traditional emailing system, he says, a company receives applications through an email while computer aided software is automated to notify a company whenever an applications is received with the capability to give job applicants feedback on the progress of their application process. We look through them and that is what a normal HR will do. However, we have heard of situations where people beat the process in that if they get three good CVs, why waste time with all the others," he reveals adding that there are many cases where non- technical HR personnel practicing human resources do not understand professional standards about a certain profession.

As teachers and administrators strive to improve student performance and graduation rates, they're increasingly leveraging new Educational Technology (EdTech) to deliver a higher quality learning experience. To gain a competitive advantage, EdTech market players are integrating advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), robotics, and Blockchain that are set to be the largest revenue contributors to the education sector in the coming years. In the UAE, 1.2 million school and university students started their e-learning journey a year ago with the onset of the pandemic, which has fueled the surge of EdTech startups. The EdTech sector has been gaining significant momentum, leading to an acceleration of investments in 2020. For instance, the regional EdTech companies raised almost $4m in March last year.