In this paper we focus on artificial intelligence (AI) for governance, not governance for AI, and on just one aspect of governance, namely ethics audit. Different kinds of ethical audit bots are possible, but who makes the choices and what are the implications? In this paper, we do not provide ethical/philosophical solutions, but rather focus on the technical aspects of what an AI-based solution for validating the ethical soundness of a target system would be like. We propose a system that is able to conduct an ethical audit of a target system, given certain socio-technical conditions. To be more specific, we propose the creation of a bot that is able to support organisations in ensuring that their software development lifecycles contain processes that meet certain ethical standards.

What does a chair from furniture manufacturer Kartell have in common with a rocket engine by the software powerhouse Hyperganic? They were both created by generative design -- in other words, made by AI. But it's a far cry from simple CAD design, using algorithms created by AI to generate a first set of designs for a product based on certain input parameters. It will then continue to refine these designs with each iteration until the final product materialises. Combined with industrial 3D printing, the result is a technically superior product that weighs less, has better functional features and is often less prone to wear and tear.

Two key components for using ML responsibly provide a prudent "start here" for organizations: model explainability and data transparency. The inability to explain why a model arrived at a particular result presents a level of risk in nearly every industry. In some areas, like healthcare, the stakes are particularly high when a model could be presenting a recommendation for patient care. In financial services, regulators may need to know why a lender is making a loan. Data transparency can ensure there is no unfair or unintended bias in the training data sets used to build the model, which can lead to disparate impact for protected classes – and consumers have what is increasingly a legally protected right to know how their data is being used.

Earlier this month, a high-level, congressionally mandated commission released its long-awaited recommendations for how the United States should approach artificial intelligence (AI) for national security. The recommendations were part of a nearly 800-page report from the National Security Commission on AI (NSCAI) that advocated for the use of AI but also highlighted important conclusions on key risks posed by AI-enabled and autonomous weapons, particularly the dangers of unintended escalation of conflict. The commission identified these risks as stemming from several factors, including system failures, unknown interactions between these systems in armed conflict, challenges in human-machine interaction, as well as an increasing speed of warfare that reduces the time and space for de-escalation. These same factors also contribute to the inherent unpredictability in autonomous weapons, whether AI-enabled or not. From a humanitarian and legal perspective, the NSCAI could have explored in more depth the risks such unpredictability poses to civilians in conflict zones and to international law.

The latest top reads in banking, fintech, payments, cybersecurity, AI, IoT and risk management In this weeks selection; Interesting to know Digital Payments - US still in the Dark Ages Decentralized Finance - What it is and what it means Non-Fungible Tokens - Asset or Scam? Google Faces New Class Action Lawsuit for Privacy ViolationsBanks & Credit Unions Bank of America sees DeFi as potentially more disruptive than bitcoin. The bank breaks down why DeFi is fundamental How Digital Banking Can Help Credit Unions Succeed In 2021 Mobile banking in GCC surges due to Covid-19 Fintech Fintech companies must balance the pursuit of profit against ethical data usage Facebook Finally Explains Its Mysterious Wrist WearablePayments 7 Steps For Mobile Banking App Development UX Case Study: How to Create a Mobile Banking Super App The disruptive trends & companies transforming digital banking services in 2021 Cybersecurity Cybersecurity, skills concerns hamper Singapore SMB digitalisation efforts What exactly does Truecaller do with your data? A hacker's deep dive Building an Embedded Finance strategy: dos and don'ts 3 tips for mitigating cloud-related cybersecurity risks Why America will never be safe from cyberattacksArtificial Intelligence The Spectacular Growth of Artificial Intelligence Today Top 100 Artificial Intelligence Companies in the World 5 Common Pain Points With Machine Learning And How To Solve Them Digital Payments - US still in the Dark Ages Decentralized Finance - What it is and what it means Non-Fungible Tokens - Asset or Scam? Bank of America sees DeFi as potentially more disruptive than bitcoin.

Before COVID hit, women in the U.S. had made significant progress towards overcoming gender inequality. Representation was on the rise in male-dominated industries, and women were outnumbering men in the workforce for the first time since 2010. Unfortunately, 2020 would undo that short-lived victory. In December, the U.S. economy lost around 140,000 jobs -- all of which belonged to women. Beyond the U.S., women accounted for 54% of job losses worldwide, even though they only made up 39% of the global workforce. Before women suffer even greater gender inequality setbacks, we have to pick up the pace in achieving workplace equality and inclusivity.

But how can organizations developing ML models enforce explainability and transparency standards when doing so might mean sharing with the public the very features, data sets, and model frameworks that represent that organization's proprietary intellectual property (IP)? Given machine learning's complexity and interdisciplinary nature, executives should employ a wide variety of approaches to manage the associated risks, which include building risk management into model development and applying holistic risk frameworks that leverage and adapt principles used in managing other types of enterprise risk. Whereas standard technical documentation is created to help practitioners implement a model, documentation focused on explainability and transparency informs consumers, regulators, and others about why and how a model or data set is being used. Such documentation includes a high-level overview of the model itself, including: its intended purpose, performance, and provenance; information about the training data set and training process; known issues or tradeoffs with the model; identified risk mitigation strategies; and any other information that can help contextualize the technology. Similarly, model documentation can become the proxy for sharing the model and its features and data sets with the world as opposed to sharing the actual "cookie recipe."

UiPath, a New York robotics automation company, on Friday said it had filed with the Securities and Exchange Commission for an initial public offering. The move comes not long after UiPath raised fresh capital from investors at a valuation of $35 billion, making the company one of the most valuable privately held tech businesses in the U.S., CNBC reported. The company, which plans to list on the New York Stock Exchange under the ticker symbol PATH, aims to raise $1 billion in the IPO, the SEC Form S-1 says. It has not detailed the number of shares it plans to offer or the estimated price range. In the fiscal year ended Jan.

The right to be forgotten states that a data subject has the right to erase their data from an entity storing it. In the context of machine learning (ML), it requires the ML model provider to remove the data subject's data from the training set used to build the ML model, a process known as \textit{machine unlearning}. While straightforward and legitimate, retraining the ML model from scratch upon receiving unlearning requests incurs high computational overhead when the training set is large. To address this issue, a number of approximate algorithms have been proposed in the domain of image and text data, among which SISA is the state-of-the-art solution. It randomly partitions the training set into multiple shards and trains a constituent model for each shard. However, directly applying SISA to the graph data can severely damage the graph structural information, and thereby the resulting ML model utility. In this paper, we propose GraphEraser, a novel machine unlearning method tailored to graph data. Its contributions include two novel graph partition algorithms, and a learning-based aggregation method. We conduct extensive experiments on five real-world datasets to illustrate the unlearning efficiency and model utility of GraphEraser. We observe that GraphEraser achieves 2.06$\times$ (small dataset) to 35.94$\times$ (large dataset) unlearning time improvement compared to retraining from scratch. On the other hand, GraphEraser achieves up to $62.5\%$ higher F1 score than that of random partitioning. In addition, our proposed learning-based aggregation method achieves up to $112\%$ higher F1 score than that of the majority vote aggregation.

Elected officials and local authorities across the United States and around the world should consider replicating an innovative legislative proposal that would prohibit police from arming robots used in their law enforcement operations. The bill, introduced on March 18 by New York City council members Ben Kallos and Vanessa Gibson, would "prohibit the New York City Police Department (NYPD) from using or threatening to use robots armed with a weapon or to use robots in any manner that is substantially likely to cause death or serious physical injury." The proposed law comes after a social media outcry over the use of an unarmed 70-pound ground robot manufactured by Boston Dynamics in a policing operation last month in the Bronx. US Representative Alexandria Ocasio-Cortez criticized its deployment "for testing on low-income communities of color with under-resourced schools" and suggested the city should invest instead in education. In a statement published in Wired and other news outlets, Boston Dynamics CEO Robert Playter said that the company's robots "will achieve long-term commercial viability only if people see robots as helpful, beneficial tools without worrying if they're going to cause harm."