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A Distributional Approach to Controlled Text Generation

arXiv.org Artificial Intelligence

We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, "pointwise" and "distributional" constraints over the target LM -- to our knowledge, this is the first approach with such generality -- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study we show the effectiveness of our adaptive technique for obtaining faster convergence.


Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

arXiv.org Artificial Intelligence

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.


Banks look at 'explainable' AI systems to boost consumer trust - Roll Call

#artificialintelligence

Banks and other financial firms are investing in "explainable" artificial intelligence that lets auditors and analysts trace how decisions about loans and other services are made by financial technologies, experts say. The increasing use of software with AI capabilities such as machine learning and data mining has automated banking operations, increasing efficiency and providing more services. But privacy and civil liberties groups contend that has come at a cost, with bias in the AI systems' algorithms leading to discrimination in the form of loans or other services denied based on sex or ethnicity. This perception of algorithmic bias is a big problem for banks, which are investing in technical solutions to solve the problem, Moutusi Sau, an analyst at research and advisory company Gartner Inc., told CQ Roll Call. That issue is known as the black box problem with AI systems: software decision-making processes that often are opaque to humans, making it difficult or impossible to determine how a decision was made.


Optimal Survival Trees

arXiv.org Machine Learning

Survival analysis methods are required for censored data in which the outcome of interest is generally the time until an event (onset of disease, death, etc.), but the exact time of the event is unknown (censored) for some individuals. When a lower bound for these missing values is known (for example, a patient is known to be alive until at least time t) the data is said to be right-censored. A common survival analysis technique is Cox proportional hazards regression (Cox, 1972) which models the hazard rate for an event as a linear combination of covariate effects. Although this model is widely used and easily interpreted, its parametric nature makes it unable to identify nonlinear effects or interactions between covariates (Bou-Hamad et al., 2011). Recursive partitioning techniques (also referred to as trees) are a popular alternative to parametric models. When applied to survival data, survival tree algorithms partition the covariate space into smaller and smaller regions (nodes) containing observations with homogeneous survival outcomes.


How to Make Artificial Intelligence Less Biased

#artificialintelligence

How could software designed to take the bias out of decision making, to be as objective as possible, produce these kinds of outcomes? After all, the purpose of artificial intelligence is to take millions of pieces of data and from them make predictions that are as error-free as possible. But as AI has become more pervasive--as companies and government agencies use AI to decide who gets loans, who needs more health care and how to deploy police officers, and more--investigators have discovered that focusing just on making the final predictions as error free as possible can mean that its errors aren't always distributed equally. Instead, its predictions can often reflect and exaggerate the effects of past discrimination and prejudice. In other words, the more AI focused on getting only the big picture right, the more it was prone to being less accurate when it came to certain segments of the population--in particular women and minorities.


The State of AI Ethics Report (October 2020)

arXiv.org Artificial Intelligence

The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: AI and society, bias and algorithmic justice, disinformation, humans and AI, labor impacts, privacy, risk, and future of AI ethics. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. These experts include: Danit Gal (Tech Advisor, United Nations), Amba Kak (Director of Global Policy and Programs, NYU's AI Now Institute), Rumman Chowdhury (Global Lead for Responsible AI, Accenture), Brent Barron (Director of Strategic Projects and Knowledge Management, CIFAR), Adam Murray (U.S. Diplomat working on tech policy, Chair of the OECD Network on AI), Thomas Kochan (Professor, MIT Sloan School of Management), and Katya Klinova (AI and Economy Program Lead, Partnership on AI). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.


How to Make Artificial Intelligence Less Biased

WSJ.com: WSJD - Technology

How could software designed to take the bias out of decision making, to be as objective as possible, produce these kinds of outcomes? After all, the purpose of artificial intelligence is to take millions of pieces of data and from them make predictions that are as error-free as possible. But as AI has become more pervasive--as companies and government agencies use AI to decide who gets loans, who needs more health care and how to deploy police officers, and more--investigators have discovered that focusing just on making the final predictions as error free as possible can mean that its errors aren't always distributed equally. Instead, its predictions can often reflect and exaggerate the effects of past discrimination and prejudice. In other words, the more AI focused on getting only the big picture right, the more it was prone to being less accurate when it came to certain segments of the population--in particular women and minorities.


Why Your Board Needs a Plan for AI Oversight

#artificialintelligence

We can safely defer the discussion about whether artificial intelligence will eventually take over board functions. We cannot, however, defer the discussion about how boards will oversee AI -- a discussion that's relevant whether organizations are developing AI systems or buying AI-powered software. With the technology in increasingly widespread use, it's time for every board to develop a proactive approach for overseeing how AI operates within the context of an organization's overall mission and risk management. According to McKinsey's 2019 global AI survey, although AI adoption is increasing rapidly, overseeing and mitigating its risks remain unresolved and urgent tasks: Just 41% of respondents said that their organizations "comprehensively identify and prioritize" the risks associated with AI deployment. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.


Does Palantir See Too Much?

#artificialintelligence

On a bright Tuesday afternoon in Paris last fall, Alex Karp was doing tai chi in the Luxembourg Gardens. He wore blue Nike sweatpants, a blue polo shirt, orange socks, charcoal-gray sneakers and white-framed sunglasses with red accents that inevitably drew attention to his most distinctive feature, a tangle of salt-and-pepper hair rising skyward from his head. Under a canopy of chestnut trees, Karp executed a series of elegant tai chi and qigong moves, shifting the pebbles and dirt gently under his feet as he twisted and turned. A group of teenagers watched in amusement. After 10 minutes or so, Karp walked to a nearby bench, where one of his bodyguards had placed a cooler and what looked like an instrument case. The cooler held several bottles of the nonalcoholic German beer that Karp drinks (he would crack one open on the way out of the park). The case contained a wooden sword, which he needed for the next part of his routine. "I brought a real sword the last time I was here, but the police stopped me," he said matter of factly as he began slashing the air with the sword. Those gendarmes evidently didn't know that Karp, far from being a public menace, was the chief executive of an American company whose software has been deployed on behalf of public safety in France. The company, Palantir Technologies, is named after the seeing stones in J.R.R. Tolkien's "The Lord of the Rings." Its two primary software programs, Gotham and Foundry, gather and process vast quantities of data in order to identify connections, patterns and trends that might elude human analysts. The stated goal of all this "data integration" is to help organizations make better decisions, and many of Palantir's customers consider its technology to be transformative. Karp claims a loftier ambition, however. "We built our company to support the West," he says. To that end, Palantir says it does not do business in countries that it considers adversarial to the U.S. and its allies, namely China and Russia. In the company's early days, Palantir employees, invoking Tolkien, described their mission as "saving the shire." The brainchild of Karp's friend and law-school classmate Peter Thiel, Palantir was founded in 2003. It was seeded in part by In-Q-Tel, the C.I.A.'s venture-capital arm, and the C.I.A. remains a client. Palantir's technology is rumored to have been used to track down Osama bin Laden -- a claim that has never been verified but one that has conferred an enduring mystique on the company. These days, Palantir is used for counterterrorism by a number of Western governments.


Counterfactual Explanations for Machine Learning: A Review

arXiv.org Artificial Intelligence

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine-learning-based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.