In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasizing the explainability to enhance its trustworthiness. Our objective is to create a system that not only optimises the allocation of teams to scheduled tasks but also provides clear, understandable explanations for its decisions, particularly in cases where the problem is infeasible. By incorporating human-in-the-loop mechanisms, the tool aims to enhance user trust and facilitate interactive conflict resolution. We implemented our approach on a prototype tool/digital demonstrator intended to be evaluated on a real industrial scenario both in terms of performance and user acceptability.

A novel New York City law that penalizes employers for bias in artificial intelligence hiring tools is leaving companies scrambling to audit their AI programs before the law takes effect in January. The law, which requires employers to conduct an independent audit of the automated tools they use, marks the first time employers in the US will face heightened legal requirements if they wish to use those any automated decision-making tools. Such tools--which can range from algorithms built to find ideal candidates to software that assesses body language--have faced scrutiny in recent years for their potential to perpetuate bias against protected groups. But without guidance from the city, employers aren't clear what, exactly, is expected of them and how to prepare. "Notably, the law does not define who or what is meant by an'independent auditor,'" said Danielle J. Moss, a partner at Gibson Dunn & Crutcher LLP.

This op-ed was written by Mona Sloane, a sociologist and senior research scientist at the NYU Center for Responsible A.I. and a fellow at the NYU Institute for Public Knowledge. Her work focuses on design and inequality in the context of algorithms and artificial intelligence. We have a new A.I. race on our hands: the race to define and steer what it means to audit algorithms. Governing bodies know that they must come up with solutions to the disproportionate harm algorithms can inflict. This technology has disproportionate impacts on racial minorities, the economically disadvantaged, womxn, and people with disabilities, with applications ranging from health care to welfare, hiring, and education.

The complete blood count (CBC) performed by automated hematology analyzers is one of the most ordered laboratory tests. It is a first-line tool for assessing a patient's general health status, or diagnosing and monitoring disease progression. When the analysis does not fit an expected setting, technologists manually review a blood smear using a microscope. The International Consensus Group for Hematology Review published in 2005 a set of criteria for reviewing CBCs. Commonly, adjustments are locally needed to account for laboratory resources and populations characteristics. Our objective is to provide a decision support tool to identify which CBC variables are associated with higher risks of abnormal smear and at which cutoff values. We propose a cost-sensitive Lasso-penalized additive logistic regression combined with stability selection. Using simulated and real CBC data, we demonstrate that our tool correctly identify the true cutoff values, provided that there is enough available data in their neighbourhood.

While artificial Intelligence (AI) can take many forms, machine learning is one area that has received a lot of attention. In the healthcare space, the machine learning approach has been leveraged with clinical decision-making. Existing data is utilized to "train" decision-making algorithms that are leveraged to help healthcare professionals make diagnostic or treatment decisions. The power of these tools to make difficult diagnostic decisions is impressive. But what role should AI play in clinical decision-making, both now and moving forward?

KLM Royal Dutch Airlines is the world's oldest international airline still operating under its original name. On its 100th anniversary, FTE spoke to Daan Debie, Director Engineering & Architecture, KLM Royal Dutch Airlines, who outlined how the airline has embraced innovation through its "pioneering and entrepreneurial spirit". Indeed, KLM's vigorous digital transformation strategy is largely due to recognising and leveraging the advantages of modern technology. Debie, who will speak in the Premium Conference at FTE-APEX Asia EXPO 2019 (12-13 November, Singapore), explains: "Digital transformation does not just mean replacing paper with apps. For us it means getting the right information to the right people at the right time to enable well-informed decision-making in an increasingly complex environment, supported by digital tooling. "Key to this is to be truly data-driven, working from a single-source-of-truth and applying cutting-edge technology and algorithms to make sense of the complex operations." KLM is currently investing heavily in building automated decision-making tools to improve operations. In June last year, the airline embarked on a unique partnership with Boston Consulting Group (BCG) which has the potential to "revolutionise global airline operations". The project is a result of a close collaboration between KLM Operations Decision Support and Operations frontline teams, BCG's consulting team, and members of BCG Gamma, an artificial intelligence and advanced analytics entity of data scientists, data engineers and software developers, who have developed a solution based on artificial intelligence, machine learning, and advanced optimisation that addresses all elements of the airline operations, while having a positive impact on customer experience and operating costs. With these tools, KLM and other airlines will be able to tackle the most complex decisions pertaining to fleet, crew, ground services and network, with a focus on breaking down the typical silos across these departments. Earlier this year, Brazilian low-cost carrier GOL became the first airline customer of the KLM-BCG joint venture which will help GOL deliver better on-time performance to its customers while maintaining low costs. As Director Engineering & Architecture for the Department of Operations Decision Support (ODS) at KLM, Debie is responsible for creating and maintaining a cohesive overall architecture and technological vision for the products and platforms developed at ODS, but also for other clients within the partnership between KLM and BCG. "I help teams within ODS and BCG/KLM teams at Partnership clients to build their products in accordance with the architectural vision," he explains. "Additionally, I'm responsible for ensuring that we maintain high engineering standards in our development efforts.

A Hong Kong venture capitalist fund credits a single member of its management team with pulling it back from the brink of bankruptcy. But the executive is not a seasoned investment professional, nor even a human being. It is an algorithm known as Vital. Dmitry Kaminskiy, managing partner of Deep Knowledge Ventures, believes that the fund would have gone under without Vital because it would have invested in "overhyped projects." Vital, which stands for Validating Investment Tool for Advancing Life Sciences, helped the board to make more logical decisions, he said.

Leading provider of aerial imagery and data analytics expands data extraction capabilities for local government, insurance and infrastructure sectors. Bothell, WA (April 26, 2017) – EagleView, the leading provider of aerial imagery and data analytics for government and commercial industries, is proud to announce the acquisition of OmniEarth, developer of machine learning technologies and decision-making tools for the water resource management, energy and insurance markets. With this acquisition, EagleView gains OmniEarth's machine learning capabilities, resulting in higher accuracy and precision of existing automated datasets. OmniEarth's ability to extract data from geospatial imagery will enhance EagleView's property reports and Pictometry imagery classification of land areas such as impervious surfaces or irrigated farmland. It will also better identify roof shape and condition, tree overhang, decks, pools and other notable property features.