Officials within the Department of Energy are looking to apply practical uses of artificial intelligence technology to helping underserved communities. Speaking during a FedScoop discussion panel, Pamela Isom, the director of the Artificial Intelligence and Technology Office at Energy, explained the importance of using AI technology to strategically help, as it becomes more and more ubiquitous in daily life. Some current use cases for AI tech within Energy are automated loan and application processing. Ipsom elaborated that her office's mission to ensure equitable access to AI technology came from a community discussion where gaps in adequate technological infrastructure were highlighted. "Today, we're looking at AI for instance, to not only help with procurement cycles, but with processing and evaluating [requests for information] for instance," Isom said.

Dr. Isabel Millar is a philosopher and cultural theorist from London. She received her PhD from Kingston University, School of Art in 2021. She holds an MA in Psychosocial Studies from Birkbeck College, University of London and a BA in Philosophy from The University of Sussex. She writes and talks about AI, sex, the body, space, culture, film and the future. Isabel is also a Research Fellow at the Centre for Critical Thought, University of Kent and Research Fellow and faculty at the Global Centre for Advanced Studies, where she teaches with GCAS' newly formed Institute of Psychoanalysis.

Companies are under mounting pressure from regulators, investors, and consumers to progress toward more sustainable and socially responsible business operations -- and to demonstrate these measures in a robust and verifiable way. In fact, corporate responsibility and environmental sustainability risks tied as the third highest concerns for organizations, as ranked by large corporations in a 2021 Forrester report. However, the various types of data that companies need to understand and report on sustainability initiatives remains highly fragmented and difficult for all relevant parties to access. To help organizations respond to these challenges, IBM has acquired Envizi, a leading data and analytics software provider for environmental performance management. Envizi complements IBM's growing portfolio of AI-powered software -- including IBM Maximo asset management solutions, IBM Sterling supply chain solutions and IBM Environmental Intelligence Suite -- to help companies assess the impacts of the environment on business and of business on the environment.

The Department of Homeland Security (DHS) is collecting feedback and opinions regarding the use of artificial intelligence (AI) and facial recognition between now and December 6. DHS has used and piloted AI-enabled technologies in several functions like customs and border protection, transportation security, and investigations. Earlier this year, DHS launched new shoe-scanning imaging technology, to be deployed at TSA security checkpoints to improve the efficiency of airport screening and potentially eliminate the need to remove shoes and outerwear when passing through checkpoints. However, AI and facial recognition bring public controversies, such as bias, security, and privacy concerns. "Understanding how the public perceives these technologies and then designing and deploying them in a manner responsive to the public's concerns is critical in gaining public support for DHS's use of these technologies," an information collection request posted to the Federal Register stated.

Many powerful Artificial Intelligence (AI) techniques have been engineered with the goals of high performance and accuracy. Recently, AI algorithms have been integrated into diverse and real-world applications. It has become an important topic to explore the impact of AI on society from a people-centered perspective. Previous works in citizen science have identified methods of using AI to engage the public in research, such as sustaining participation, verifying data quality, classifying and labeling objects, predicting user interests, and explaining data patterns. These works investigated the challenges regarding how scientists design AI systems for citizens to participate in research projects at a large geographic scale in a generalizable way, such as building applications for citizens globally to participate in completing tasks. In contrast, we are interested in another area that receives significantly less attention: how scientists co-design AI systems "with" local communities to influence a particular geographical region, such as community-based participatory projects. Specifically, this article discusses the challenges of applying AI in Community Citizen Science, a framework to create social impact through community empowerment at an intensely place-based local scale. We provide insights in this under-explored area of focus to connect scientific research closely to social issues and citizen needs.

Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners) who wanted to incorporate these issues started to assess companies based on how they handle such topics. For this assessment, investors rely on specialized rating agencies that issue ratings along the environmental, social and governance (ESG) dimensions. Such ratings allow them to make investment decisions in favor of sustainability. However, rating agencies base their analysis on subjective assessment of sustainability reports, not provided by every company. Furthermore, due to human labor involved, rating agencies are currently facing the challenge to scale up the coverage in a timely manner. In order to alleviate these challenges and contribute to the overall goal of supporting sustainability, we propose a heterogeneous ensemble model to predict ESG ratings using fundamental data. This model is based on feedforward neural network, CatBoost and XGBoost ensemble members. Given the public availability of fundamental data, the proposed method would allow cost-efficient and scalable creation of initial ESG ratings (also for companies without sustainability reporting). Using our approach we are able to explain 54% of the variation in ratings R2 using fundamental data and outperform prior work in this area.

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.

Atos has teamed up with French-startup, DreamQuark, to launch a digital solution for banks and insurers dedicated to socially responsible investing "SRI". The launch of the new Sustainable Investment Brain platform coincides with the publication of proposed new European rules around transparent artificial intelligence, with which it complies. DreamQuark is a financial services focused, AI technology specialist and a member of the Atos Scaler accelerator program. Sustainable Investment Brain utilises leverages a variety of financial data, including ESG (environmental social and governance) information provided by Atos to provide insights on potential customers and the most suitable assets and investment products. There is growing interest in responsible investing whilst ESG is an also an area of scrutiny for financial services institutions.

Artificial intelligence (AI) has potential to drive game-changing improvements for underserved communities in global health. In response, The Rockefeller Foundation and USAID partnered with the Bill and Melinda Gates Foundation to develop AI in Global Health: Defining a Collective Path Forward. Research began with a broad scan of instances where artificial intelligence is being used, tested, or considered in healthcare, resulting in a catalogue of over 240 examples. This grouping involves tools that leverage AI to monitor and assess population health, and select and target public health interventions based on AI-enabled predictive analytics. It includes AI-driven data processing methods that map the spread and burden of disease while AI predictive analytics are then used to project future disease spread of existing and possible outbreaks.

Let's face it: sustainability reports are important, but they're usually quite dry reads. Microsoft might have a way to reel you in, however. According to The Verge, Microsoft has released a free Minecraft map that brings the goals of its latest sustainability report to life. "Sustainability City" lets you walk through eco-friendly food production, tour an energy-efficient home and explore concepts ranging from alternative energy to water outflow. You can find the map in the Minecraft Marketplace's "Education Collection," and six lessons are available through Minecraft: Education Edition for teachers who want to share those environmental goals.