Skip to main content
main-content

2023 | Buch

Trustworthy Artificial Intelligence Implementation

Introduction to the TAII Framework

share
TEILEN
insite
SUCHEN

Über dieses Buch

Rapidly developing Artificial Intelligence (AI) systems hold tremendous potential to change various domains and exert considerable influence on societies and organizations alike. More than merely a technical discipline, AI requires interaction between various professions. Based on the results of fundamental literature and empirical research, this book addresses the management’s awareness of the ethical and moral aspects of AI.

It seeks to fill a literature gap and offer the management guidance on tackling Trustworthy AI Implementation (TAII) while also considering ethical dependencies within the company. The TAII Framework introduced here pursues a holistic approach to identifying systemic ethical relationships within the company ecosystem and considers corporate values, business models, and common goods aspects like the Sustainable Development Goals and the Universal Declaration of Human Rights. Further, it provides guidance on the implementation of AI ethics in organisations without requiring a deeper background in philosophy and considers the social impacts outside of the software and data engineering setting. Depending on the respective legal context or area of application, the TAII Framework can be adapted and used with a range of regulations and ethical principles.

This book can serve as a case study or self-review for c-level managers and students who are interested in this field. It also offers valuable guidelines and perspectives for policymakers looking to pursue an ethical approach to AI.

Inhaltsverzeichnis

Frontmatter
1. Introduction
Abstract
This book aims to generate awareness of ethical challenges for Artificial Intelligence (AI) systems and to analyze the management perspective and understanding of ethics for their AI product or service. While analyzing existing ethical standards and principles for AI systems, the work will generate a better understanding and improvement of ethical principles and moral values for the AI era. This work will contribute to research in the field of ethical standards for AI systems and will represent the management perspective (C-level, executives, etc.). The work will also be a beneficial contribution to generating and implementing future ethical AI guidelines and procedures. Working on ethical and moral understanding regarding AI systems and especially the implementation, is still in an early stage. It is not only about defining what is right and what is wrong as it is such a complex work that can also not be done by a small group of professionals. But it is timely and important to discuss, agree, and standardize common principles.
This book will answer the defined question in Sect. 1.1. Therefore, the document will start to give an extract of the most important terms, as there is no central definition of AI. This book is structured in fundamental literature and in empirical research part. The literature research in Chap. 2 describes the most used understandings of AI, the current state, and visible challenges of AI. Followed by Chap. 3, which analyses existing literature and research outcomes related to ethics and moral values. Therefore, the focus is on training, challenges, and guidelines. Chapter 4 describes the perspective of the management regarding AI implementations, strategy, and decisions. Followed by Chap. 5 which describes the used method and research setting for the empirical research, which is done via expert interviews. The findings of the first empirical research are summarized in Chap. 6. Finally, the findings were transformed into further research and for the generation of the TAII Framework described in Chap. 7.
Josef Baker-Brunnbauer
2. Artificial Intelligence
Abstract
This chapter gives an AI overview by starting to list and compare different terms and definitions in Sect. 2.1. It shows, how important it is to clarify terms beforehand, as there are not always unique definitions and understanding by humans about AI. It highlights that an AI system does not represent a robot or physical machine. The software can be operated on different kinds of hardware systems or platforms but does not require to have any physical shape. Section 2.2 gives an overview of different AI technology aspects. It shows the differences in data computation and progress between humans and computer systems. Section 2.3 describes several AI technologies and highlights the challenge of generated bias. If the AI algorithm gets trained with an already biased data set whether that can happen with consciousness or not, it will not generate a “better human.”
The next Sect. 2.4 lists some AI challenges. A current main challenge is that humans still see AI systems as robots or machines and think of physical threats or opportunities. As AI systems can be only software modules, it is recommended to shift the broad society discussions from an attacking Terminator robot to broader ethics and responsibility discussion. Every citizen should take responsibility and proactive actions to shape “Friendly AI” systems. Like every technology, also AI can get abused and used in a not general friendly way. Section 2.5 describes opportunities and risks of AI technology. As humans stopped being goal driven,
Section 2.6 highlights the scenario, where future AI systems might overtake the definition of goals for humans. To generate a broad awareness within society to develop and implement AI systems in daily life, it needs acceptance, transparency, and understanding. Section 2.7 gives an overview of a humans-based classification of evolution.
Josef Baker-Brunnbauer
3. Ethics and Moral
Abstract
This chapter focuses on ethics and moral based on an AI environment. It sums up that all past societies had ethical standards with the central goal to survive. Ethics itself goes back a long time in history. Already Aristotle was speaking about ethics, and it will be an even more central topic for the future of humanity. Will AGI consider the needs and understandings of the lower-developed humans? How will humans handle “intelligence explosion” and recursive scenarios, where AI or AGI will replicate itself constantly? Humans reshaped the planet earth to gain benefits, will this be done by an AI system as well?
This chapter lists an overview of different AI ethics frameworks and lists different viewpoints for the situation that AI systems will have their own moral status. Section 3.1 describes approaches to how to train AI systems in ethics. One major challenge is, if AI systems will get trained with already biased data sets. Overall, it is still unclear how to teach ethics to AI systems. Section 3.2 lists criteria for Product Development of AI systems and describes the importance of testing environments. Next, Sect. 3.3 analyses some ethical and moral challenges. It shows that moral decision-making is not about logic and rationality, instead, it is about psychologically acceptable explanations. Therefore, it requires a deep understanding of humans and their behavior.
Other challenges are accountability (who is responsible for a failure?), law adaption, quick reproduction of AI systems, and the not predictable social impact. Section 3.4 dives deeper into existing guidelines. A survey shows the importance of the requirement of transparency and safety for users and consumers. It describes the outcome of the summary of an overview of 47 values and principles that are based on several manifestos. The AIHLEG of the European Commission defines Trustworthy AI by seven key requirements and offers an AI assessment list.
Josef Baker-Brunnbauer
4. Business and Management Perspective
Abstract
To build the bridge from academic research to companies that develop and use AI technologies for their product, service, or customer experience, this chapter focuses on C-level perspective. Section 4.1 describes the current state of AI implementations. Therefore, an international management report shows that most of the companies that have invested in AI did not get any impact from it up to now. Another aspect is that difficulties in generating value from AI are organizational and not technologically based. Another literature research outcome shows that companies with a clear AI focus on data and technology are gaining less value than those who actively align AI owners, process owners, and business owners. Also, companies with an AI IT focus generate less value than those with a broad strategic focus.
Section 4.2 goes deeper into business strategy. It describes that an increasing number of managers see AI not only as an opportunity, but they also define it as a strategic risk, which has been increasing over the last years. The two important strategic approaches are integrating AI with strategic digital initiatives and the AI focus on revenue generation instead of cost reduction. For the future, management needs to focus on human resource and talent strategy to ensure the development of AI systems. Section 4.3 gives an overview of some C-level statements about their AI implementation experience and status.
Josef Baker-Brunnbauer
5. Empirical Study
Abstract
The outcome of the literature research shows that definitions and AI guidelines are differently understood between humans as well as within countries and cultures. Many papers describe AI technologies, social AI topics, and different approaches at academic research level. Nevertheless, there is no signed, committed, and implemented common European or global AI guideline now. Therefore, it depends on the countries itself, if and how they push AI technology. For the moment, companies of all maturity levels are developing AI systems based on their idea to generate business and to shape AI technology applications. Addressing ethical and moral topics related to AI is still in an early stage as it is not about “good or bad” or “right or wrong.” Moral and ethical issues regarding AI are critical and important for the society. Therefore, it needs an open discussion on different levels and a broad general understanding of the benefits and the risks. To build a bridge between academic research and AI business drivers, this empirical research analyses the understanding and awareness about the social impact of AI products and services from a management perspective. The focus of this book is to answer the question “What kind of awareness does the management have about the social impact of their Artificial Intelligence (AI) product or service?” by conducting expert interviews. The aim of this research is to further investigate the management understanding by answering the research question based on five sub-questions.
Josef Baker-Brunnbauer
6. Findings
Abstract
This chapter describes the outcome of the empirical research. Therefore, the results are structured and grouped to give answers to the defined sub-questions. The guidance was that the interview participants are speaking about their AI product or service and not about a general AI perspective.
Josef Baker-Brunnbauer
7. TAII Framework
Abstract
Organizations and companies need practical tools and guidelines to kick off the implementation of Trustworthy Artificial Intelligence (TAI) systems. AI development companies are still at the beginning of this process or have not even started yet. The findings of the research address to decrease the entry-level barrier to AI ethics implementation by introducing the Trustworthy Artificial Intelligence Implementation (TAII) Framework. The outcome is comparatively unique given that it considers a meta-perspective of implementing TAI within organizations. As such, this research aims to fill a literature gap for management guidance to tackle trustworthy AI implementation while considering ethical dependencies within the company. The TAII Framework takes a holistic approach to identifying the systemic relationships of ethics for the company ecosystem and considers corporate values, business models, and common good aspects like the Sustainable Development Goals and the Universal Declaration of Human Rights. The TAII Framework creates guidance to initiate the implementation of AI ethics in organizations without requiring a deep background in philosophy and considers the social impacts outside of a software and data engineering setting. Depending on the legal regulation or area of application, the TAII Framework can be adapted and used with different regulations and ethical principles.
Josef Baker-Brunnbauer
Metadaten
Titel
Trustworthy Artificial Intelligence Implementation
verfasst von
Josef Baker-Brunnbauer
Copyright-Jahr
2023
Electronic ISBN
978-3-031-18275-4
Print ISBN
978-3-031-18274-7
DOI
https://doi.org/10.1007/978-3-031-18275-4

Premium Partner