Jazmia Henry July 18, 2022 Summary Discussions about reducing the bias present in algorithms have been on the rise since the mid 2010s. AI ethicists, DEI practitioners, Sociologists, Data Scientists and Social Justice Advocates have decried the lack of understanding of the harms that algorithms pose to people who belong to historically marginalized groups. These cries have become increasingly accepted in industry since 2020, but little is understood of how algorithm and Machine Learning (ML) model builders should go about mitigating bias in models that are intended for deployment. This chapter is written with the Data Scientist or MLOps professional in mind but can be used as a resource for policy makers, reformists, AI Ethicists, sociologists, and others interested in finding methods that help reduce bias in algorithms. I will take a deployment centered approach with the assumption that the professionals reading this work have already read the amazing work on the implications of algorithms on historically marginalized groups by Gebru, Buolamwini, Benjamin and Shane to name a few. If you have not read those works, I refer you to the "Important Reading for Ethical Model Building " list at the end of this paper as it will help give you a framework on how to think about Machine Learning models more holistically taking into account their effect on marginalized people. In the Introduction to this chapter, I root the significance of their work in real world examples of what happens when models are deployed without transparent data collected for the training process and are deployed without the practitioners paying special attention to what happens to models that adapt to exploit gaps between their training environment and the real world. The rest of this chapter builds on the work of the aforementioned researchers and discusses the reality of models performing post production and details ways ML practitioners can identify bias using tools during the MLOps lifecycle to mitigate bias that may be introduced to models in the real world. Introduction "Whether AI will help us reach our aspirations or reinforce the unjust inequalities is ultimately up to us." - Joy Buolowini, 'Facing the Coded Gaze' AI: More than Human Whether you're driving your car using a GPS system, call on Alexa or Siri to turn on your favorite tune, go on social media to perform a well-earned scroll down memory lane, or go to Google search to find a gift to buy for a friend, you have encountered a Machine Learning model.

The robustness of deep neural networks (DNN) models has attracted increasing attention due to the urgent need for security in many applications. Numerous existing open-sourced tools or platforms are developed to evaluate the robustness of DNN models by ensembling the majority of adversarial attack or defense algorithms. Unfortunately, current platforms do not possess the ability to optimize the architectures of DNN models or the configuration of adversarial attacks to further enhance the robustness of models or the performance of adversarial attacks. To alleviate these problems, in this paper, we first propose a novel platform called auto adversarial attack and defense ($A^{3}D$), which can help search for robust neural network architectures and efficient adversarial attacks. In $A^{3}D$, we employ multiple neural architecture search methods, which consider different robustness evaluation metrics, including four types of noises: adversarial noise, natural noise, system noise, and quantified metrics, resulting in finding robust architectures. Besides, we propose a mathematical model for auto adversarial attack, and provide multiple optimization algorithms to search for efficient adversarial attacks. In addition, we combine auto adversarial attack and defense together to form a unified framework. Among auto adversarial defense, the searched efficient attack can be used as the new robustness evaluation to further enhance the robustness. In auto adversarial attack, the searched robust architectures can be utilized as the threat model to help find stronger adversarial attacks. Experiments on CIFAR10, CIFAR100, and ImageNet datasets demonstrate the feasibility and effectiveness of the proposed platform, which can also provide a benchmark and toolkit for researchers in the application of automated machine learning in evaluating and improving the DNN model robustnesses.

OpenAI researchers collaborated with Georgetown University's Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. As generative language models improve, they open up new possibilities in fields as diverse as healthcare, law, education and science. But, as with any new technology, it is worth considering how they can be misused.

Ten European software industry associations have called on the EU to scrap plans to include the regulation of general-purpose AI including natural language processing and chatbots in its new AI Act, describing it as a "fundamental departure from its original objective" and saying that it could stifle innovation and hit the open source community. The European Union AI Act aims to establish a framework to regulate the use of artificial intelligence, taking a "risk-based" approach to its use and establish a worldwide standard. The Act includes core provisions including tighter regulations in high-risk areas such as healthcare and transparency requirements, focusing on specific-purpose narrow AI. However, the group of industry associations, led by BSA, the software alliance, has published a joint statement urging EU institutions to reject recent additions to the Act that include regulation of general purpose AI and instead "maintain a risk-based approach". The objective of general purpose AI is to create machines that can reason and think like a human.

When I was a much younger person, there were a couple of popular movies offering warnings about the dangers of unchecked artificial intelligence. The first is 1983′s "WarGames," in which a young computer hacker played by Matthew Broderick accidentally triggers a countdown to the launch of the full arsenal of the United States nuclear stockpile at the Soviet Union because the Pentagon had handed control of the intercontinental ballistic missile system to a computer program, following the failure of humans to execute launch orders during a training exercise. The second one is 1984′s "The Terminator," where killer robot Arnold Schwarzenegger is dispatched back in time by the sentient artificial intelligence (called Skynet) in order to assassinate the hero of the resistance that is fighting the artificial intelligence in the future. I think I have that right. I honestly never understood the whole time travel aspect of the "Terminator" franchise.

Over 100 days of nationwide protests in Iran have demonstrated the greatest pushback against the decades-old regime and its repressive policies, showing the world that the people demand rights they have long been denied. Iranian authorities may be using new technology to help enforce the country's strict dress code for women, expanding the use of facial recognition technology to issue fines and other penalties for those breaking the rules. "Many people haven't been arrested in the streets," Shaparak Shajarizadeh, who fled from Iran to Canada in 2018 after multiple violations of Iran's strict laws and became an activist, told Wired in a report Tuesday. "They were arrested at their homes one or two days later." Shajarizadeh is one of several observers of Iran who fear that the country's Islamist regime has begun to weaponize facial recognition technology to find and punish women who flaunt laws about their dress and appearance in public, a setback for activists amid months of protesting for women's rights and regime change.

Meta said Monday that the Variance Reduction System, the machine learning-powered technology it initially discussed last June to ensure the equitable distribution of ads on its platforms, is now live for housing ads in the U.S. Vice president of civil rights and deputy general counsel Roy L. Austin Jr. said in a Newsroom post Monday that the plan is to extend VRS to credit and employment ads in the U.S. at some point in 2023. The Department of Justice reached a settlement with Meta last June regarding a complaint filed in August 2018 with the Department of Housing and Urban Development over discriminatory uses of ad targeting options from then-Facebook. The HUD complaint was related to housing ads, but the same issues were raised regarding credit and employment ads, and Meta said at the time that it would apply plans it shared in the settlement to all three categories. DOJ civil rights division assistant attorney general Kristen Clarke said in a statement, "This development marks a pivotal step in the Justice Department's efforts to hold Meta accountable for unlawful algorithmic bias and discriminatory ad delivery on its platforms. The Justice Department will continue to hold Meta accountable by ensuring that the Variance Reduction System addresses and eliminates discriminatory delivery of advertisements on its platforms. Federal monitoring of Meta should send a strong signal to other tech companies that they, too, will be held accountable for failing to address algorithmic discrimination that runs afoul of our civil rights laws."

Establish a centralized location and format where DoD can assess the state of the art in available data, analytics, digital and AI/ML technology solutions. Establish a centralized location and format where industry, academic organizations, and individuals can promote their data, analytics, digital and AI/ML technology research, products, and services to the Government. Provide an environment and process for rapid procurement of available solutions, increasing speed-to-contract for DoD organizations, and providing contracting pathways for solution providers, including traditional, nontraditional, and new entrants to the defense marketplace. Establish a centralized location and format where DoD can assess the state of the art in available data, analytics, digital and AI/ML technology solutions. Establish a centralized location and format where industry, academic organizations, and individuals can promote their data, analytics, digital and AI/ML technology research, products, and services to the Government.

Demis Hassabis stands halfway up a spiral staircase, surveying the cathedral he built. The DNA sculpture, spanning three floors, is the centerpiece of DeepMind's recently opened London headquarters. It's an artistic representation of the code embedded in the nucleus of nearly every cell in the human body. "Although we work on making machines smart, we wanted to keep humanity at the center of what we're doing here," Hassabis, DeepMind's CEO and co-founder, tells TIME. This building, he says, is a "cathedral to knowledge." Each meeting room is named after a famous scientist or philosopher; we meet in the one dedicated to James Clerk Maxwell, the man who first theorized electromagnetic radiation. "I've always thought of DeepMind as an ode to intelligence," Hassabis says. Hassabis, 46, has always been obsessed with intelligence: what it is, the possibilities it unlocks, and how to acquire more of it.

But the gold rush was doomed from the start. In January 2022, these issues boiled over into mass protests. Within weeks, the government effectively cut miners off from the national grid, bringing the boom to an abrupt end. It hopes it can eventually restore the industry--but the future looks highly uncertain, given the volatility in the global crypto sector. For decades, high-end precision-strike American aircraft dominated drone warfare.