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Explainable Agency in Artificial Intelligence by Silvia Tulli,David W. Aha Pdf
This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: Contributes to the topic of explainable artificial intelligence (XAI) Focuses on the XAI subtopic of explainable agency Includes an introductory chapter, a survey, and five other original contributions
Explainable, Transparent Autonomous Agents and Multi-Agent Systems by Davide Calvaresi,Amro Najjar,Michael Schumacher,Kary Främling Pdf
This book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2019, held in Montreal, Canada, in May 2019. The 12 revised and extended papers presented were carefully selected from 23 submissions. They are organized in topical sections on explanation and transparency; explainable robots; opening the black box; explainable agent simulations; planning and argumentation; explainable AI and cognitive science.
Explainable Agency in Artificial Intelligence by Silvia Tulli,David W. Aha Pdf
This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: • Contributes to the topic of explainable artificial intelligence (XAI) • Focuses on the XAI subtopic of explainable agency • Includes an introductory chapter, a survey, and five other original contributions
Explainable, Transparent Autonomous Agents and Multi-Agent Systems by Davide Calvaresi,Amro Najjar,Michael Winikoff,Kary Främling Pdf
This book constitutes the proceedings of the Second International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2020, which was due to be held in Auckland, New Zealand, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 8 revised and extended papers were carefully selected from 20 submissions and are presented here with one demo paper. The papers are organized in the following topical sections: explainable agents; cross disciplinary XAI; explainable machine learning; demos.
Explainable Human-AI Interaction by Sarath Sarath Sreedharan,Anagha Anagha Kulkarni Pdf
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek,Grégoire Montavon,Andrea Vedaldi,Lars Kai Hansen,Klaus-Robert Müller Pdf
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Explainable Artificial Intelligence by Luca Longo Pdf
This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.
AI and Human Agency: Balancing Challenges and Opportunities by AQEEL AHMED Pdf
"AI and Human Agency: Balancing Challenges and Opportunities" Summary: In Summary, the topic of AI and the eventual loss of human agency poses significant concerns and challenges. Human agency, or the ability to make decisions and act autonomously, is necessary for personal autonomy and the survival of democratic society. However, as AI becomes more prevalent in decision-making processes, there is growing concern about the erosion of human agency. Several key areas of concern have been identified, including the possibility of bias in AI algorithms, a lack of transparency and interpretability in AI systems, the collection and analysis of personal data, job displacement, and issues of accountability and responsibility in AI-driven decisions. Instead of considering AI as a threat, another viewpoint emphasizes the possibility for collaboration between humans and AI systems. AI technologies have the potential to complement human capacities by giving vital insights and decision-making support. Individuals can reap the benefits of AI without completely relinquishing their autonomy by leveraging AI's strengths while preserving human judgment. The necessity of responsible AI development, openness, fairness, privacy, and accountability is emphasized in this collaborative approach. A comprehensive approach is required to combat the potential loss of human agency. This includes programs to provide humans with the information and skills necessary to engage with AI technologies, as well as the promotion of responsible AI development methods. To ensure transparency, fairness, and accountability, comprehensive rules and ethical principles are required to govern the appropriate deployment and use of AI systems. Collaboration and inclusivity are critical in generating various viewpoints and incorporating stakeholder input into AI technology development and regulation. Continuous regulation evaluation and adaptation are required to keep up with the growing AI field and solve emerging concerns. To summarize, while there are legitimate concerns about the impact of AI on human agencies, it is critical to approach this topic with a balanced viewpoint. Depending on how technology is developed, implemented, and regulated, AI has the ability to both augment and undermine human agency. Society can manage the challenges of AI and protect human agencies while reaping the benefits of this transformative technology by encouraging collaboration, responsible development, transparency, and responsibility. In recent years, artificial intelligence (AI) has made enormous advances, altering many aspects of our life. However, there are concerns that increasing reliance on AI would result in a loss of human agency, raising concerns about our ability to make independent decisions and control our own life. Some claim that as AI systems get more advanced and prevalent, we are increasingly ceding control and decision-making capacities to robots. This possible degradation of human agency is a significant issue that must be thoroughly examined and considered. The impact of AI on human agency is most visible in automated decision-making. Artificial intelligence algorithms are increasingly being used in critical fields such as banking, employment, healthcare, and criminal justice. While automation has benefits such as increased efficiency and scalability, it also raises questions about accountability, prejudice, and the declining role of human judgment. Delegating decision-making to AI systems may result in a loss of control over outcomes with significant personal, social, and ethical ramifications. Bias and discrimination are serious worries about AI's impact on human agency. AI algorithms are trained on large datasets that may contain societal biases. As a result, these algorithms have the potential to perpetuate and magnify prejudices, resulting in biased outcomes. When decisions that affect people's lives are predominantly influenced by biased algorithms, it undermines human agency and perpetuates societal injustices. Individuals' potential loss of agency is exacerbated when they are subjected to decisions impacted by biases that they do not understand or question. Another obstacle to human agency is the transparency and interpretability of AI systems. Many AI systems function as "black boxes," making judgments without explaining their reasons. Because individuals are unable to understand or question the judgments made by AI systems that affect their lives, this opacity can diminish trust and agency. Individuals are disempowered by the lack of transparency since they are unable to exercise their agency or hold the AI accountable for its acts. Furthermore, AI systems' collecting, and analysis of personal data raises issues about privacy, manipulation, and the ability to influence human behavior. AI-powered systems collect massive amounts of data, enabling tailored experiences and targeted advertising. This data-driven manipulation, however, has the potential to constrain human agencies by quietly guiding individuals toward specific choices, so altering their decision-making processes and potentially limiting their autonomy. Concerns have been raised concerning the influence of AI on jobs and the workforce, as well as the weakening of human agency. There is concern about job displacement and economic inequality as a result of work automation. The replacement of human workers by AI systems has the potential to reduce job satisfaction, limit chances for personal growth and innovation, and expand the divide between those who control AI technology and those who are influenced by them. Loss of agency in the job can have serious social and psychological consequences, impacting people's sense of control and self-worth. To address the potential loss of human agencies in the AI era, a diversified approach is required. It needs the development of strong human monitoring and control mechanisms for AI systems. Transparency, explain ability, and accountability should be stressed in the design and implementation of AI algorithms. Ethical principles and regulatory frameworks can assist in ensuring that AI technologies are developed and used ethically, balancing innovation and human agency. Furthermore, education and empowerment are critical in reducing the loss of human agency. Individuals must be provided with the knowledge and skills necessary to comprehend AI systems, evaluate their implications critically, and actively participate in decision-making processes. Individuals may keep their agency and effectively navigate the AI-driven landscape through increasing digital literacy and a deeper grasp of AI technologies. Human Agency Defined Human agency refers to an individual's natural ability to make choices and act independently, allowing them to build their lives based on their own values, beliefs, and aspirations. It includes the ability to think critically, analyze alternatives, and make decisions based on personal preferences and views. Human agency requires more than just making decisions; it also entails accepting responsibility for the consequences of those decisions. The concept of personal autonomy is central to human agency. Autonomy is defined as the ability to manage oneself without external pressure or excessive influence. It enables people to express their distinctive identities, achieve their goals, and live lives that are consistent with their beliefs and ideas. Human agency is inextricably linked to personal autonomy because it allows individuals to express their autonomy by making choices that reflect their own goals and preferences. Another important feature of human agency is self-determination. It entails the ability to shape one's own destiny and direct one's own life. Making decisions that accord with one's personal vision of a meaningful and rewarding life is what self-determination entails. It enables people to forge their own paths, pursue their interests, and seek personal improvement and fulfillment. Human agencies are critical to the survival of democratic society. Individuals are granted political rights and liberties in democratic regimes, and their participation in decision-making processes is deemed crucial. Individuals can actively participate in the democratic process, exercise their voting rights, and express their thoughts and preferences thanks to human agency. It promotes civic duty, gives residents the ability to express their concerns, and holds governments accountable for their actions. Furthermore, human agency is inextricably tied to the concept of personal responsibility. Individuals assume responsibility for the results and repercussions of their decisions and actions when they exercise their agency. It entails realizing that one's decisions affect not only oneself but also others and society as a whole. Accepting responsibility for one's choices and actions is an essential component of personal development, maturity, and ethical behavior. Human agency is important for reasons other than individual well-being. Human agencies are valued and respected in societies that support innovation, creativity, and diversity. Individuals who are free to express their agency can provide fresh perspectives, ideas, and contributions to a variety of fields, including science, the arts, politics, and business. Individual freedom and expression thrive in a lively and dynamic community that recognizes and promotes human agency. Concerns arise, however, when the concept of human agency collides with the rapid progress of AI technologies. As AI systems become more sophisticated and prevalent, there is rising concern that dependence on AI may result in a loss of human agency. The rising automation of decision-making processes, as well as the delegation of choices to AI algorithms, raises concerns about individuals' ability to retain control and autonomy over their life. While artificial intelligence has the potential to improve human capabilities and provide significant insights, there are risks involved with relying too much on AI-driven decision-making. AI biases, a lack of transparency in decision-making processes, and the possibility of algorithmic manipulation can weaken human agencies. If AI systems make judgments that have a major impact on people's lives, it can erode personal autonomy, limit self-determination, and perpetuate inequality. Personal autonomy, self-determination, and the operation of democratic society all rely on human action. It empowers people to make choices, act independently, and accept responsibility for the consequences of their actions. While the advent of AI raises concerns about the potential loss of human agency, it is critical to guarantee that AI technologies are created and implemented in such a way that individual autonomy, empowerment, and the ability to shape one's own life are respected and preserved. Balancing the benefits of AI while preserving human agency is a crucial challenge that necessitates careful thought and ethical decision-making.
Artificial Intelligence by Harvard Business Review Pdf
Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.
Computational Theories of Interaction and Agency by Philip Agre,Stanley J. Rosenschein Pdf
Over time the field of artificial intelligence has developed an "agent perspective" expanding its focus from thought to action, from search spaces to physical environments, and from problem-solving to long-term activity. Originally published as a special double volume of the journal Artificial Intelligence, this book brings together fundamental work by the top researchers in artificial intelligence, neural networks, computer science, robotics, and cognitive science on the themes of interaction and agency. It identifies recurring themes and outlines a methodology of the concept of "agency." The seventeen contributions cover the construction of principled characterizations of interactions between agents and their environments, as well as the use of these characterizations to guide analysis of existing agents and the synthesis of artificial agents.Artificial Intelligence series.Special Issues of Artificial Intelligence
Foundations of Rational Agency by Michael Wooldridge,A. Rao Pdf
This volume represents an advanced, comprehensive state-of-the-art survey of the field of rational agency as it stands today. It covers the philosophical foundations of rational agency, logical and decision-theoretic approaches to rational agency, multi-agent aspects of rational agency and a number of approaches to programming rational agents. It will be of interest to researchers in logic, mainstream computer science, the philosophy of rational action and agency, and economics.
Explainable and Transparent AI and Multi-Agent Systems by Davide Calvaresi,Amro Najjar,Michael Winikoff,Kary Främling Pdf
This book constitutes the proceedings of the Third International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021, which was held virtually due to the COVID-19 pandemic. The 19 long revised papers and 1 short contribution were carefully selected from 32 submissions. The papers are organized in the following topical sections: XAI & machine learning; XAI vision, understanding, deployment and evaluation; XAI applications; XAI logic and argumentation; decentralized and heterogeneous XAI.
AI 2019: Advances in Artificial Intelligence by Jixue Liu,James Bailey Pdf
This book constitutes the proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019, held in Adelaide, SA, Australia, in December 2019. The 48 full papers presented in this volume were carefully reviewed and selected from 115 submissions. The paper were organized in topical sections named: game and multiagent systems; knowledge acquisition, representation, reasoning; machine learning and applications; natural language processing and text analytics; optimization and evolutionary computing; and image processing.
