Abstract: As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous.

The nature, scale, and diversity of the cybersecurity threats that the modern organization faces means leveraging the power of automated security tools is a necessity. Large enterprises can generate billions of distinct system logs and events each day. Manually poring through such information is impossible. Security software and automated tools make the process of sifting through such security data quick and efficient. Among the different categories of cybersecurity tools an organization could use to enforce their security policies, security analytics software is among the most critical.

A bill that U.S. Sen. Cory Gardner has co-sponsored to develop and guide the use of artificial intelligence in the federal government passed out of committee on Wednesday. "Our bill will bring agencies, industry, and others to the table to discuss government adoption of artificial intelligence and emerging technologies," Gardner said in a statement. The AI in Government Act defines artificial intelligence as any type of computer programming that would enable the computer to carry out tasks of the sort that "would require intelligence if performed by a human." The bill would create an AI Center of Excellence within the General Services Administration, which would coordinate AI use in the public interest and house the government's technical expertise. The center's responsibility would also include analyzing the ethical and civil liberties implications of artificial intelligence, helping state and local governments as needed.

These are two examples of how NASA hopes to use artificial intelligence. As far-fetched as the concept sounds, the agency is already using AI in missions on both Earth and Mars. And there are other missions in the works that could see AI exploring icy moons in search of life. This bot-friendly future stands counter to some of the fuss in the press this past week, after Facebook shut down an experiment because two artificially intelligent bots began communicating in a shorthand language instead of English. Many in the media portrayed the bots as coming up with their own language.

The rapid growth of text data has motivated the development of machine-learning based automatic text summarization strategies that concisely capture the essential ideas in a larger text. This study aimed to devise an extractive summarization method for A-133 Single Audits, which assess if recipients of federal grants are compliant with program requirements for use of federal funding. Currently, these voluminous audits must be manually analyzed by officials for oversight, risk management, and prioritization purposes. Automated summarization has the potential to streamline these processes. Analysis focused on the "Findings" section of ~20,000 Single Audits spanning 2016-2018. Following text preprocessing and GloVe embedding, sentence-level k-means clustering was performed to partition sentences by topic and to establish the importance of each sentence. For each audit, key summary sentences were extracted by proximity to cluster centroids. Summaries were judged by non-expert human evaluation and compared to human-generated summaries using the ROUGE metric. Though the goal was to fully automate summarization of A-133 audits, human input was required at various stages due to large variability in audit writing style, content, and context. Examples of human inputs include the number of clusters, the choice to keep or discard certain clusters based on their content relevance, and the definition of a top sentence. Overall, this approach made progress towards automated extractive summaries of A-133 audits, with future work to focus on full automation and improving summary consistency. This work highlights the inherent difficulty and subjective nature of automated summarization in a real-world application.

Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the notion of adversarial examples in the tabular domain. We propose a formalization based on the imperceptibility of attacks in the tabular domain leading to an approach to generate imperceptible adversarial examples. Experiments show that we can generate imperceptible adversarial examples with a high fooling rate.

The debate on AI ethics largely focuses on technical improve ments and stronger regulation to prevent accidents or misuse of AI, with soluti ons relying on holding individual actors accountable for responsible AI devel opment. While useful and necessary, we argue that this "agency" approach disrega rds more indirect and complex risks resulting from AI's interaction with the soci o-economic and political context. This paper calls for a "structural" approach to assessing AI's effects in order to understand and prevent such systemic risks where no individual can be held accountable for the broader negative impacts. This i s particularly relevant for AI applied to systemic issues such as climate change and f ood security which require political solutions and global cooperation. To pro perly address the wide range of AI risks and ensure'AI for social good', agency-foc used policies must be complemented by policies informed by a structural approa ch.

Even after the machine-based computers came online, humans were still very much part of the equation, utilizing their skills to define the right theories and strategies. Depending on your business, there are two types of automation/AI that a finance organization can start employing: machine learning and RPA (Robotic Process Automation). First, let's get one thing straight--robots are not stealing our jobs. When NASA's Apollo program hired human computers to help decipher the math for the moon landing--so compellingly presented in the film "Hidden Figures"--today's machines were not readily available. In fact, the math for the computational questions they needed to answer hadn't even been invented.

The Stanford University Institute for Human-Centered Artificial Intelligence is calling for the U.S. government to make a $120 billion investment in the nation's AI ecosystem over the course of the next 10 years. The report calls efforts by the Trump administration, like the call for near $1 billion in U.S. non-defense research and development spending in 2020, "encouraging, but not nearly enough." The national AI vision report specifically calls for $2 billion in annual spending to support entrepreneurs and expand innovation, $3 billion on education, and $7 billion on interdisciplinary research to discover breakthrough advances in the field. The report was written by center directors John Etchemendy and Dr. Fei-Fei Li, and it calls underfunding of AI a threat to U.S. global leadership and a "national emergency in the making." Li is a leader at the Stanford Computer Vision Lab, creator of ImageNet, and until last year, served as chief AI scientist for Google Cloud.

I spend a disproportionate amount of time reading and talking to two somewhat niche groups of people in American politics: democratic socialists of the Sen. Bernie Sanders variety (or maybe a bit to the left of that), and left-libertarians from the Bay Area who are interested in effective altruism. These are both small groups, but they have social and intellectual influence bigger than their numbers. And while from a distance they look similar (I'm sure they both vote for Democrats in general elections, say), there's a big issue on which they part ways where collaboration could be productive: artificial intelligence safety. Effective altruists have, for complex sociological reasons I explored in a podcast episode, become very interested in AI as a potential "existential risk": a force that could, in extreme circumstances, wipe out humanity, just as nuclear war or asteroid strikes could. Kelsey Piper has a comprehensive Vox explainer of these arguments, and I take them seriously, but most friends to my left do not.