AI bias continues to be a prevailing problem when it comes to ensuring proper implementation of artificial intelligence (AI) in many industries. Since the technology has been implemented across several verticals, some of its use cases have been causing… unpleasantness among users. One of the biggest worries surrounding the sticky issue of AI bias, is in facial recognition solutions. As AI works purely on analyzing data inputs that it has access to, the algorithms may at times not provide entirely accurate results. In the case of facial recognition, the particular AI recognized certain races as criminals, causing an uproar in society.

The European Association for Biometrics (EAB) in cooperation with German Biometrics Standardization Group at DIN are hosting a workshop on the Artificial Intelligence Act (AIA). In April of this year the European Commission presented the proposal for an Artificial Intelligence Act (AIA) and later in August the International Standardization Joint Technical Committee 1 presented new work item proposal ISO/IEC 9868 for a related international standard. This workshop regarding the AIA and international standardization of AI gathers the biometrics and AI standardization community in order to learn and discuss possible implications. The talks and break out sessions will be moderated by Alexander Goschew of German biometrics standardization group (DIN) and Christoph Busch of Hochschule Darmstadt (H-DA). Attendance is free of charge but registration is required.

Said Vladimir Puzyrev of Curtin Universitys Oil and Gas Innovation Centre and the School of Earth and Planetary Sciences, "This project is an important step towards adding value to existing digital geochemical datasets." Researchers at Australia's Curtin University and the Geological Survey of Western Australia are using deep learning to analyze geochemical data as part of an effort to expand mineral exploration in the region. The Western Australia Mineral Exploration (WAMEX) database contains more than 50 million samples, making manual analysis cost prohibitive and time consuming. Curtin's Vladimir Puzyrev said, "The ultimate aim of this research project is to help identify new mineral deposits in Western Australia by analyzing big geochemical data using deep learning methods."

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the'what' and the'how' of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice.

Broadly, the guidelines aim to help integrate AI ethics into the entirety of the AI lifecycle. The guidelines set forth six (or eight original) fundamental ethics rules for AI development. These include: (1) that AI should aim to enhance the well-being of humankind; (2) that AI should promote fairness and justice and protect the legitimate rights and interests of all relevant stakeholders; (3) that AI should protect the privacy and security of its users and their data; (4) that AI should be developed in such a way as to ensure human controllability, transparency, and trustworthiness; (5) that AI should be designed to be accountable; and (6), that the Chinese government should aim to generally improve AI ethics literacy.

Without machine learning, it is difficult to deploy robust cybersecurity solutions. Machine learning can be used in tandem if there isn't a rich, thorough, and complete approach to the data. Cybersecurity systems can use MI to recognize patterns and learn from them to detect repeated attacks and adapt to new behavior. It is a useful tool for cybersecurity teams to be more proactive in responding to threats and preventing them from happening again. By reducing time spent on mundane tasks, it can help businesses make strategic use of their resources. Cyber Security analysts may use ML in a variety of areas to improve their security procedures.

In April of 2021, the U.S. Federal Trade Commission -- in its "Aiming for truth, fairness, and equity in your company's use of AI" report -- issued a clear warning to tech industry players employing artificial intelligence: "Hold yourself accountable, or be ready for the FTC to do it for you." Likewise, the European Commission has proposed new AI rules to protect citizens from AI-based discrimination. These warnings, and impending regulations, are warranted. Machine learning (ML), a common type of AI, mimics patterns, attitudes and behaviors that exist in our imperfect world, and as a result, it often codifies inherent biases and systemic racism. Unconscious biases are particularly difficult to overcome, because they, by definition, exist without human awareness.

The Department of Defense's Office of the Chief Information Officer, or DoD CIO, is pursuing several efforts to make sure the U.S. combatant commands have the fundamental tools to enable artificial intelligence and machine learning to aid their operational command and control. The DoD CIO's efforts naturally hinge on data and data management, an appropriate transport layer and future cloud capabilities, solutions that will benefit a broad range of warfighters not just at the commands, said Kelly Fletcher, who is performing the duties of the department's chief information officer on behalf of John Sherman, the nominated CIO who is currently going through his confirmation process for the position and testifying tomorrow in front of the U.S. Senate. A senior executive service official, Fletcher has been working in the office since 2020. She presented a keynote address during AFCEA International's TechNet Cyber conference in Baltimore on October 27. Fletcher emphasized that the DoD CIO's office supports more than 40 major combatant commands, services and agencies, "and they all have unique requirements," she said.

The Army recently scanned 7,200 km across four states on the eastern seaboard and used artificial intelligence to find and destroy specific simulated targets in an area the size of a 10-square-foot box. It was all part of the Army's XVIII Airborne Corp artificial intelligence-enabled live-fire target identification exercise on Thursday that used nearly 20 platforms and units from each of the other branches. The event was the fourth of its kind for the Scarlet Dragon program, which began in 2020. The Corps, assisted by elements of the Navy, Air Force and Marine Corps, worked with various platforms in all domains. But a key ingredient was the National Geospatial-Intelligence Center, which provided satellite imagery for software to sift through and find targets.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.