An institutional statement describes expected actions for actors within the presence or absence of particular constraints, or parameterizes features of an institutional system. Institutional statements convey information that contextualizes their applicability. They vary in prescriptiveness and force, as reflected by the presence of information that more or less strongly compels behavior and by the presence of information that specifies payoffs for compliance, or noncompliance, with statements instructions. Varying in the inclusion of these various kinds of information, institutional statements typically take two functional forms: constitutive and regulative. Constitutive statements constitute features of a system (e.g., actor positions and roles, processes, venues, etc). Regulative statements describe actions linked to specific actors within certain contextual parameters. According to the IG 2.0, institutional statements are commonly comprised of a set of syntactic components, with individual components associating with unique information, and which combine to convey a statement's institutional meaning. Regulative statements are composed of some or all of the following components with the corresponding syntactic labels: (i) an Actor, referred to as an Attribute; (ii) action associated with actor, referred to as an Aim; (iii) action context, referred to as Context; (iv) a receiver of action, referred to as an Object; (v) a prescriptive operator that describes how strongly an action is compelled or restrained, referred to as a Deontic; and (vi) an incentive linked to action, referred to as an Or else. Constitutive statements are composed of some or all of the following components with the corresponding syntactic labels: (i) the entity that is being constituted within a statement, referred to as a Constituted Entity; (ii) an action that constitutes the Constituted Entity, called the Constitutive Function; (iii) the constitution context, referred to as Context; (iv) properties that serve as input to the Constitutive Function, called Constituting Properties; (iv) A prescriptive operator that defines to what extent the action of an institutional statement is compelled, restrained, or discretionary, referred to as a Deontic; and (vi) an incentive linked to action, referred to as an Or else.

Should firms that apply machine learning algorithms in their decision-making make their algorithms transparent to the users they affect? Despite growing calls for algorithmic transparency, most firms have kept their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm's predictive power. We develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent benefits the firm. We show that, in some cases, even the predictive power of machine learning algorithms may increase if the firm makes them transparent. By contrast, users may not always be better off under algorithmic transparency. The results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve on them is close to zero. Overall, our results show that firms should not view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.

Are your AI Algorithms locked down and safe? In my prior Forbes blogs, the business imperative for Board Directors and CEOs to advance their governance practices to lead forward with AI was framed. This blog shares the insights from a recent interview with Cathy Cobey, the EY global trusted AI leader, where we explore: how practicing responsible AI is stacking up, the impact of data bias and key board director questions to ensure CEO's are managing the new risks that AI presents. One of the key insights Cathy shared is that from all of her global client interactions to date, she has yet to find any organization, large or small, which has a robust inventory management process to easily identify or inventorize their AI models. This also mirrors my global research that Board Directors and CEO's don't know where their AI algorithms are.

Artificial intelligence (AI) is the most disruptive innovation of our lifetime. Its adoption has grown 60 percent in the last year, according to an April 2020 report by Narrative Science. The report's authors say the technology is having a "significant and imminent impact on everything from company strategy, to business operations, to job functions." So what are some of AI's implications in the new normal, one in which American entrepreneurs find themselves saving cash, working from home and wearing masks everywhere they go? Currently, for entrepreneurs, the most popular AI-powered solutions deal with predictive analytics (24 percent), machine learning (21 percent), language processing (14 percent) and voice recognition and response (14 percent), according to the same Narrative Science report.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Like fabric softener and football, banks -- or at least banks as physical spaces -- have been cited as yet another industry that's being killed by those murderous Millennials.

Advances in Artificial Intelligence (AI) and Machine Learning (ML) that are being applied to legal efforts have raised controversial questions about the existent restrictions imposed on the practice-of-law. Generally, the legal field has sought to define Authorized Practices of Law (APL) versus Unauthorized Practices of Law (UPL), though the boundaries are at times amorphous and some contend capricious and self-serving, rather than being devised holistically for the benefit of society all told. A missing ingredient in these arguments is the realization that impending legal profession disruptions due to AI can be more robustly discerned by examining the matter through the lens of a framework utilizing the autonomous levels of AI Legal Reasoning (AILR). This paper explores a newly derived instrumental grid depicting the key characteristics underlying APL and UPL as they apply to the AILR autonomous levels and offers key insights for the furtherance of these crucial practice-of-law debates.

Relevant research has been standing out in the computing community aiming to develop computational models capable of predicting occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crimes, and analyzing crimes according to time. This, due to the social impact and also the complex origin of the data, thus showing itself as an interesting computational challenge. This research presents a computational model for the prediction of homicide crimes, based on tabular data of crimes registered in the city of Bel\'em - Par\'a, Brazil. Statistical tests were performed with 8 different classification methods, both Random Forest, Logistic Regression, and Neural Network presented best results, AUC ~ 0.8. Results considered as a baseline for the proposed problem.

We present several completely general diversity metrics to quantify the problem-solving capacity of any public policy decision making process. This is performed by modelling the policy process using a declarative process paradigm in conjunction with constraints modelled by expressions in linear temporal logic. We introduce a class of traces, called first-passage traces, to represent the different executions of the declarative processes. Heuristics of what properties a diversity measure of such processes ought to satisfy are used to derive two different metrics for these processes in terms of the set of first-passage traces. These metrics turn out to have formulations in terms of the entropies of two different random variables on the set of traces of the processes. In addition, we introduce a measure of `goodness' whereby a trace is termed {\it good} if it satisfies some prescribed linear temporal logic expression. This allows for comparisons of policy processes with respect to the prescribed notion of `goodness'.

In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms. One key technical challenge for directly applying maximum likelihood estimation (MLE) to censored data is that evaluating the objective function and its gradients with respect to model parameters requires the calculation of integrals. To address this challenge, we recognize that the MLE for censored data can be viewed as a differential-equation constrained optimization problem, a novel perspective. Following this connection, we model the distribution of event time through an ordinary differential equation and utilize efficient ODE solvers and adjoint sensitivity analysis to numerically evaluate the likelihood and the gradients. Using this approach, we are able to 1) provide a broad family of continuous-time survival distributions without strong structural assumptions, 2) obtain powerful feature representations using neural networks, and 3) allow efficient estimation of the model in large-scale applications using stochastic gradient descent. Through both simulation studies and real-world data examples, we demonstrate the effectiveness of the proposed method in comparison to existing state-of-the-art deep learning survival analysis models.

Last October, a couple of days before joining Stanford University as the international policy director at the Cyber Policy Center, Marietje Schaake, a former member of the European Parliament, spoke alongside Eric Schmidt, the ex-C.E.O. of Google, to a large audience of tech employees and academics. It was the keynote event at a conference hosted by the newly launched Stanford Institute for Human-Centered Artificial Intelligence (H.A.I.), at which Schaake would also have a co-appointment. Beneath the scalloped panels of a blond wood ceiling, people sipped coffee and typed on laptops in the plush chairs of a new auditorium at the heart of campus. Schmidt spoke first, striking expected notes. He said that artificial intelligence would power "extraordinary gains" in the next five years and stressed just how central Google--which had helped fund H.A.I.--would be to those advances. He acknowledged that China's use of A.I. for surveillance, especially in the Xinjiang region, was concerning.