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2021 | Buch

AI in Our Society Kapitel lesen Erstes Kapitel lesen

Towards Sustainable Artificial Intelligence

A Framework to Create Value and Understand Risk

verfasst von: Ghislain Landry Tsafack Chetsa

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization’s demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles.

This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems.

The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term.

What You Will Learn

See the relevance of ethics to the practice of data science and AI Examine the elements that enable AI within an organization Discover the challenges of developing AI systems that meet certain human or specific standards Explore the challenges of AI governance Absorb the key factors to consider when evaluating AI systems

Who This Book Is For

Decision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role.

Inhaltsverzeichnis

Frontmatter
Chapter 1. AI in Our Society
Abstract
Up to the early 2000s, artificial intelligence (AI) was perceived as a utopia outside of the restricted AI research and development community. A reputation that AI owed to its relatively poor performance at the time. In the early 2000s, significant progress had been made in the design and development of microprocessors, leading to computers capable of efficiently executing AI tasks. Additionally, the ubiquity of the Internet had led to data proliferation, characterized by the continuous generation of large volumes of structured and unstructured data at an unprecedented rate. The combination of the increasing computing power and the availability of large data sets stimulated extensive research in the field of AI, which led to successful deployments of the AI technology in various industries. Through such success, AI earned a place in the spotlight, as organizations continue to devote significant effort to integrate it as an integral part of their day-to-day operational strategy. However, the complex nature of AI often introduces challenges that organizations must efficiently address to fully realize the potential of the AI technology.
Ghislain Landry Tsafack Chetsa
Chapter 2. Ethics of the Data Science Practice
Ethics in Data Science
Abstract
The above quote describes chaos as an instrument for accessing and maintaining power. A strategy that is employed by various protagonists for their own gain throughout the HBO drama Game of Thrones. Game of Thrones presents an attention-grasping depiction of what human civilization looked like centuries ago, before the introduction of laws and rules deterring people from acting in a manner that negatively affects others. The drama highlights how people with power use this to their advantage. Today’s society is far different from that of Game of Thrones. Importantly, one no longer gets away with just any behavior. Instead, there are rules governing one’s actions and behavior.
Ghislain Landry Tsafack Chetsa
Chapter 3. Sustainable AI Framework (SAIF)
Overview of SAIF Framework
Abstract
The next few chapters discuss components of the sustainable artificial intelligence framework (SAIF). As highlighted in Chapter 1, SAIF relies on four pillars: the human factor, the intra-organizational understanding of AI, governance, and performance measurement. As seen in Figure 3-1, these four pillars form the conceptual founding block the operating model of the SAIF framework is built on. Through this operating model, an organization can dictate the ultimate behavior of an AI system. Specifically, the SAIF operating model aims to allow an organization to
Ghislain Landry Tsafack Chetsa
Chapter 4. Intra-organizational Understanding of AI: Toward Transparency
Abstract
Mainstream public perception of AI varies greatly depending on an individual's perspective and experiences. On the one hand, from a user perspective, it can be perceived as a set of services that rely on data to enable new levels of innovations, insights, and organizational performance. On the other hand, from a more technical perspective, it can be perceived as a technology using mathematical frameworks, computing infrastructures along with associated software, and processing tools for analyzing and/or extracting patterns in large volumes of data. Yet, each of these perspectives differs from our definition in “The Need for Artificial Intelligence” section of Chapter 1.
Ghislain Landry Tsafack Chetsa
Chapter 5. AI Performance Measurement: Think Business Values
Abstract
The film Minority Report starring Tom Cruise is one of the few movies that explore the potential impact of technology on everyday life. Beside its idealistic view of the future ahead of us, the sci-fi movie features an almost crime-free future where a special police unit known as the “pre-crime department” identifies and arrests criminals based on foreknowledge provided by “precogs.” For the sake of facilitating the understanding of the point we illustrate through this movie, the following is a short overview of the movie’s main story:
Ghislain Landry Tsafack Chetsa
Chapter 6. SAIF in Action: A Case Study
Abstract
Financial institutions in the United States and Europe are traditionally known to be biased against minority groups. Such bias is often observed in an organization’s choice of who is offered a credit/loan or who is denied. Similarly, these minorities are often charged higher interest than their counterparts (Stefan et al. 2018; Bartlett et al. 2019; Aldén and Hammarstedt 2016; Lloyd, Bo, and John 2005; Solomon, Alper, and Philip 2013; Patatouka and Fasianos 2015). At the time of this writing, most of the leading financial institutions are doing little to nothing to combat the problem of systemic bias observed in the banking sector and more generally in access to finance. Additionally, financial institutions are increasingly relying on AI for improving operational efficiency through automation. Such reliance on AI technology is likely to amplify bias against minorities. This is because most if not all AI systems created in the banking sector as of today must rely on historical data that inevitably reflects in some way or form the current situation in the industry. Stated differently, the data is likely to incorporate the same flaws that one may be trying to combat. But more importantly, as discussed in the “Beyond Traditional AI Performance Metrics” section of Chapter 5, bias resulting from an algorithm’s behavior operates at the institution’s level and has a wider reach than bias exhibited by humans.
Ghislain Landry Tsafack Chetsa
Chapter 7. Alternative Avenues for Regulating AI Development
AI and Regulations
Abstract
Thus far, we have discussed the many potential ethical consequences associated with AI systems. More importantly, we presented SAIF, a methodology to help organizations and their stakeholders carry out AI due diligence to prevent, identify, better understand, and mitigate undesirable consequences resulting from the DS practice throughout the development and deployment of AI systems. We have also seen that some countries, mainly developed countries, have introduced regulatory measures such as data protection regulations like GDPR. Such regulations require service providers to explicitly get informed consent from their users before collecting their data, including data resulting from their interactions with the service. We argued that, as of today, such regulations are ineffective for multiple reasons including the following:
Ghislain Landry Tsafack Chetsa
Chapter 8. AI in the Medical Decision Context
AI Impact on Clinical Decision-Making
Abstract
This famous quote from the 19th-century French poet, novelist, and dramatist Victor Hugo somewhat summarizes the complexity of any decision-making process. Typically, a straight line defines the shortest path between two points or objects. These objects need not be physical objects and may simply be an abstract concept, such as an objective or a resolution, in which case the straight line is assimilated to the shortest process one would take to achieve such objective. Through the straight-line metaphor, Victor Hugo attempts to establish the necessity of careful examination of the implications of every decision to be made. Stated differently, it is in the decision maker's best interest to understand, probably to the best of their abilities, the implications or consequences that may happen as a result of the decision being made.
Ghislain Landry Tsafack Chetsa
Chapter 9. Conclusions and Discussion
Abstract
Over the past few years, there has been an increasingly enthusiastic interest in artificial intelligence (AI) or data science (DS). This is because DS impacts almost every aspect of our lives and often outperforms traditional approaches in solving complex problems we face daily. Consequently, an increasing number of organizations all around the globe are devoting significant efforts to incorporate AI in their operational strategy as they continue to witness its transformative potential. However, realizing this potential often gives rise to unforeseen ethical consequences including, but not limited to, fairness/bias, trust, transparency, and privacy.
Ghislain Landry Tsafack Chetsa
Backmatter
Metadaten
Titel
Towards Sustainable Artificial Intelligence
verfasst von
Ghislain Landry Tsafack Chetsa
Copyright-Jahr
2021
Verlag
Apress
Electronic ISBN
978-1-4842-7214-5
Print ISBN
978-1-4842-7213-8
DOI
https://doi.org/10.1007/978-1-4842-7214-5