Building Ethically Bounded AI

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar

The Head and the Heart

Ope

A Rat in a Cage

Ope

Incorporating Behavioral Constraints in Online AI Systems

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.Comment: 9 pages, 6 figure

Potential Impacts of Changes in Climate on Water Quality in New York City\u27s Ashokan

This thesis investigates an approach for determining water resources vulnerability caused by climate change and applies it to a case-study for the New York City ater Supply System (NYCWSS).The results provide potential responses of the system to changes in climate and guidance that can inform short and long-term planning decisions. This research models the hydrology and operations of the NYCWSS and includes a statistical model of turbidity concentration in the Ashokan Reservoir. Using a stochastic weather generator, incremental changes are made to precipitation and temperature and used to drive the coupled hydrology-simulation model. The results are aggregated and examined to show the sensitivity of the system, and in particular Ashokan Reservoir turbidity, to changes in climate. The results are briefly compared with the latest GCM data to provide insight into expected changes in turbidity over the next half-century

Stochastic Volatility: Univariate and Multivariate Extensions

Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models (ARCH). Advances in the literature now offer well tested estimators for a basic univariate SVOL model. However, the basic model is too restrictive for many economic and finance applications. The use of the basic model can lead to biased volatility forecasts especially around crucial periods of high volatility. We extend the basic SVOL needs to allow for the leverage effect, through a correlation between observable and variance errors, and fat-tails in the conditional distribution. We develop a Bayesian Markov Chain Monte Carlo algorithm for this extended model. We also provide an algorithm to analyze a multivariate factor SVOL model. The method simultaneously performs finite sample inference and smoothing. We document the performance of the estimator and show why the extensions are warranted. We provide the researcher with a range of model diagnostics, such as the identification of outliers for stochastic volatility models or the assessment of the normality of the conditional distribution. We implement this methodology on a number of univariate financial time series. There is strong evidence of (1) non-normal conditional distributions for most series, and (2) a leverage effect for stock returns. We illustrate the robustness of the results to the choice of the prior distributions. These results have policy implications on decisions based upon prediction of volatility, especially when dealing with tail prediction as in risk management. Les modèles de volatilité stochastique, alias SVOL, sont plus durs à estimer que les modèles traditionnels de type ARCH. La littérature récente offre des estimateurs éprouvés pour un modèle SVOL univarié de base. Ce modèle est trop contraignant pour une utilisation en économie financière. Les prévisions de volatilité qu'il produit peuvent etre biaisées, particulièrement quand la volatilité est élevée. Nous généralisons le modèle de base en y ajoutant des effets de levier par le biais d'une corrélation entre les chocs observables et de variance, et la possibilité de distributions conditionnelles à queues épaisses. Nous développons un algorithme bayésien à chaînes markoviennes de Monte Carlo. Nous développons aussi un algorithme pour l'analyse d'un modèle SVOL multivarié à facteurs. Ces estimateurs permettent une inférence en échantillon fini pour les paramètres et les volatilités. Nous documentons les performances de l'estimateur et montrons que les extensions sont nécessaires. Nous testons la normalité des distributions conditionnelles. Cette méthode est mise en oeuvre sur plusieurs séries financières. Il y a une forte évidence (1) de distributions conditionnelles à queues épaisses, et (2) d'effets de levier pour les actifs financiers. Les résultats sont robustes et ont d'importantes implications sur les décisions fondées sur les prédictions de volatilité, particulièrement pour la gestion de risques.Stochastic volatility, ARCH, MCMC algorithm, leverage effect, risk management, fat-tailed distributions, Volatilité stochastique, ARCH, algorithme MCMC, effets de levier, gestion de risque, distributions à queues épaisses

A Combinatorial Approach to Elucidating Tribochemical Mechanisms

A new type of combinatorial tribological experiment is presented, which explores a series of tribological conditions, such as load and relative velocity, spatially separated as a "library” on one single sample. As an example, a library displaying the results of tribological testing of an additive under a series of different loads has been prepared and analyzed. The tribological information acquired during the testing has been correlated with spectroscopic information from the tribologically stressed surface. The use of imaging and small-area X-ray photoelectron spectroscopy has allowed the identification of the different tribologically stressed areas and the acquisition of detailed spectroscopic information. The composition and the thickness of the tribofilm were found to be dependent on the applied load. The use of the combinatorial approach shows the potential to greatly facilitate rapid characterization of new lubricant additive

Substituent Effect on the Reactivity of Alkylated Triphenyl Phosphorothionates in Oil Solution in the Presence of Iron Particles

The effect of the substituent attached to the phenyl rings on the reactivity of alkylated triphenyl phosphorothionates (t-butyl TPPT (b-TPPT) and p-nonyl TPPT (n-TPPT)) in oil solution at high temperature (423 and 473K) was investigated by means of Fourier-transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR) spectroscopy and X-ray photoelectron spectroscopy (XPS). The FT-IR and NMR results show that the alkylated TPPTs were highly thermally stable and did not completely decompose in oil, even upon heating at 423K for 168h and at 473K for 72h, with and without steel filings and iron particles (both metallic iron and iron oxide particles). The reaction of alkylated TPPTs was found to start with the scission of the P=S bond to yield alkylated triphenyl phosphate. The kinetics of the thermo-oxidative reaction was slower when steel filings and iron particles were added to the oil solutions during the heating experiments. The reactivity of the unsubstituted molecule (TPPT) was higher than that of alkylated TPPTs at 423K, while at 473K TPPT and n-TPPT were more reactive than b-TPPT. In the case of the experiments performed at 473K in the presence of steel filings or metallic iron or iron oxide particles, the reactivity of the alkylated TPPT molecules decreased with the length of the alkyl chain bound to the phenyl rings. The XPS results show that a reaction layer consisting of carbon, oxygen, phosphorus and iron was formed on the 100Cr6 steel filings immersed for 72h in oil solutions containing alkylated TPPTs and heated at 473K. Sulphur was neither detected on the surface nor in the composition vs depth profile. During the heating experiments, the base oil (PAO) was oxidized. At 423K, the alkylated TPPTs had a strong antioxidant effect, which was found to be more pronounced upon increasing the length of the alkyl chain bound to the phenyl rings. At 473K, the TPPTs did not inhibit the oxidation of the base oil as effectively as at 423