This paper presents a taxonomy of explainability in human-agent systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related... more
This paper presents a taxonomy of explainability in human-agent systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.
This paper presents objective metrics for how explainable artificial intelligence (XAI) can be quantified. Through an overview of current trends, we show that many explanations are generated post-hoc and independent of the agent's logical... more
This paper presents objective metrics for how explainable artificial intelligence (XAI) can be quantified. Through an overview of current trends, we show that many explanations are generated post-hoc and independent of the agent's logical process, which in turn creates explanations with limited meaning as they lack transparency and fidelity. While user studies are a known basis for evaluating XAI, studies that do not consider objective metrics for evaluating XAI may have limited meaning and may suffer from confirmation bias, particularly if they use low fidelity explanations unnecessarily. To avoid this issue, this paper suggests a paradigm shift in evaluating XAI that focuses on metrics that quantify the explanation itself and its appropriateness given the XAI goal. We suggest four such metrics based on performance differences, D, between the explanation's logic and the agent's actual performance, the number of rules, R, outputted by the explanation, the number of features, F , used to generate that explanation, and the stability, S, of the explanation. We believe that user studies that focus on these metrics in their evaluations are inherently more valid and should be integrated in future XAI research.
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Internet social networks have become a ubiquitous application allowing people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit and LinkedIn. We present an extensive study of... more
Internet social networks have become a ubiquitous application allowing people to easily share text, pictures, and audio and video files. Popular networks include WhatsApp, Facebook, Reddit and LinkedIn. We present an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. In order to better understand people's use of the network, we provide an analysis of over 6 million messages from over 100 users, with the objective of building demographic prediction models using activity data. We performed extensive statistical and numerical analysis of the data and found significant differences in WhatsApp usage across people of different genders and ages. We also inputted the data into the Weka data mining package and studied models created from decision tree and Bayesian network algorithms. We found that different genders and age demographics had significantly different usage habits in almost all message and gr...
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This paper presents an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. It is based on the analysis of over 4 million messages from nearly 100 users... more
This paper presents an extensive study of the usage of the WhatsApp social network, an Internet messaging application that is quickly replacing SMS messaging. It is based on the analysis of over 4 million messages from nearly 100 users that we collected in order to understand people’s use of the network. We believe that this is the first in-depth study of the properties of WhatsApp messages with an emphasis of noting differences across different age and gender demographic groups. It is also the first to use statistical and data analytic tools in this analysis. We found that different genders and age demographics had significantly different usage habits in almost all message and group attributes. These differences facilitate the development of user prediction models based on data mining tools. We illustrate this by developing several prediction models such as for a person’s gender or approximate age. We also noted differences in users’ group behavior. We created group behaviorial mod...
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SummaryDesigners of robotic groups are faced with the formidable task of creating effective coordination architectures that can deal with collisions due to changing environment conditions and hardware failures. Communication between... more
SummaryDesigners of robotic groups are faced with the formidable task of creating effective coordination architectures that can deal with collisions due to changing environment conditions and hardware failures. Communication between robots is a mechanism that can at times be helpful in such systems, but can also create a time, energy, or computation overhead that reduces performance. In dealing with this issue, different communication schemes have been proposed ranging from those without any explicit communication, localized algorithms, and centralized or global communicative methods. Finding the optimal communication act is typically an intractable problem in real-world problems. As a result, we argue that at times group designers should use computationally bounded team communication approaches. We propose two such approaches: an algorithm selection approach to communication whereby robots choose between a known group of communication schemes and a parameterized communication frame...
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Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system.... more
Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the oper...
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Abstract: IntroductionTo date, only limited work has been performed on studyinghow performance scales with the addition of robots togroups. Our work focuses on studying this issue withinone domain, robotic foraging. Foraging has been... more
Abstract: IntroductionTo date, only limited work has been performed on studyinghow performance scales with the addition of robots togroups. Our work focuses on studying this issue withinone domain, robotic foraging. Foraging has been extensivelystudied, and is formally defined as consisting of locatingtarget items from a search region S, and deliveringthem to a goal region G [3]. Previous work by Rybski
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In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information that is not explicitly represented in the network, or is not visible to all. To date, this... more
In recent years, social networks have surged in popularity. One key aspect of social network research is identifying important missing information that is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on finding the connections that are missing between nodes, a challenge typically termed as the link prediction problem . This article introduces the missing node identification problem, where missing members in the social network structure must be identified. In this problem, indications of missing nodes are assumed to exist. Given these indications and a partial network, we must assess which indications originate from the same missing node and determine the full network structure. Toward solving this problem, we present the missing node identification by spectral clustering algorithm (MISC), an approach based on a spectral clustering algorithm, combined with nodes’ pairwise affinity measures that were adopted from...
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Abstract. Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Individual drivers have... more
Abstract. Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Individual drivers have different driving styles and preferences. Current systems do not distinguish among the users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the number of interactions ...
Effectively modeling an agent's cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these... more
Effectively modeling an agent's cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many elements from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive ...
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Robust autonomous agents should be able to cooperate with new teammates effectively by employing ad hoc teamwork. Reasoning about ad hoc teamwork allows agents to perform joint tasks while cooperating with a variety of teammates. As the... more
Robust autonomous agents should be able to cooperate with new teammates effectively by employing ad hoc teamwork. Reasoning about ad hoc teamwork allows agents to perform joint tasks while cooperating with a variety of teammates. As the teammates may not share a communication or coordination algorithm, the ad hoc team agent adapts to its teammates just by observing them. Whereas most past work on ad hoc teamwork considers the case where the ad hoc team agent has a prior model of its teammate, this paper is the ...
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Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Individual drivers have different... more
Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Individual drivers have different driving styles and preferences. Current systems do not distinguish among the users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can save on the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While accepted packages such as Weka were successful in learning drivers’...
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ABSTRACT Barrett's oesophagus (BE) is the pre-cursor for oesophageal adenocarcinoma. Endoscopic surveillance is performed to detect dysplasia in BE as it is likely to be amenable to curative treatment. Current surveillance relies... more
ABSTRACT Barrett's oesophagus (BE) is the pre-cursor for oesophageal adenocarcinoma. Endoscopic surveillance is performed to detect dysplasia in BE as it is likely to be amenable to curative treatment. Current surveillance relies on white-light endoscopy to obtain 4-quadrant biopsies through every 2cm of the BE segment. This samples less than 5% of the BE epithelium and is likely to miss dysplasia. A novel endoscopic image enhancement technology, i-Scan (PENTAX HOYA, Japan), has been developed to improve lesion recognition in the gastrointestinal tract (GIT). i-Scan uses post-processing light filtering to provide real-time analysis and enhancement of the mucosa and microvasculature. We evaluated the accuracy of i-Scan using a mucosal (M) and vascular (V) classification system for BE amongst 3 expert (consultant) endoscopists. Machine learning (ML) generates simple rules, known as a decision tree, to improve dysplasia detection and validate our classification system. To our knowledge, ML has never been applied for dysplasia detection in the GIT.