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 Intel·ligència artificial, ètica i societat

Artificial Intelligence, Ethics and Society

Index

Foreword

Introduction

1st PART – An overview through the specialised literature

.1.1. What do we mean by artificial intelligence (IA)?

1.2. What do we mean by the ethics of IA?

1.3. The main ethical principles of AI

1.4. Why the emergence of ethical AI?

1.5. What are the main risks of AI?

1.6. The social perception of AI

1.7. What is the institutional response?

1.8. What is the business response?

1.9. How to move towards ethical AI?

1.10. A proposal for a regulatory framework of AI in the EU

1.11. By way of conclusion to the first part

2nd PART – An overview through expert opinions

2.1. Collecting and analysing qualitative information domain

 2.2.1. Ethical considerations of AI: restriction, sub-objective or main objective?

 2.2.2. AI as a factor in human debilitation

 2.2.3. AI as a factor of human empowerment

 2.2.4. The context of ethical considerations in AI

 2.2.5. The impact of AI on younger generations

2.3. Legal domain

 2.3.1. The geopolitics of AI

 2.3.2. AI governance

 2.3.3. The regulation of AI 

 2.3.4. The social justice of AI.

 2.3.5.Transparency in AI

2.4. The future outlook

 2.4.1. The main ethical and social challenges in the long term

 2.4.2. The balance of opportunities and risks of AI in the future

2.5. By way of conclusion to the second part

AI everywhere

 A Citizen’s Guide to Artificial Intelligence

A book on how algorithmic tools are being used in areas ranging from health care, law and social services to social media and business, and it explains the basic technical components of AI in a jargon-free manner.

In chapter 10, on AI regulation:

We need new rules for AI, but they won’t always need to be AI-specific. As AI enters almost every area of our lives, it will come into contact with the more general rules that apply to commerce, transport, employment, healthcare, and everything else.

Nor will we always need new rules. Some existing laws and regulations will apply pretty straightforwardly to AI (though they might need a bit of tweaking here and there). Before we rush to scratch-build a new regulatory regime, we need to take stock of what we already have.

AI will, of course, necessitate new AI-specific rules. Some of the gaps in our existing laws will be easy to fill. Others will force us to revisit the values and assumptions behind those laws. A key challenge in fashioning any new regulatory response to technology is to ensure that it serves the needs of all members of society, not just those of tech entrepreneurs and their clients, or indeed those of any other influential cohort of society.

 

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 The Triumph of Artificial Intelligence. How Artificial Intelligence is Changing the Way We Live Together

Topics of the book: 

How Much and What Kind of Artificial Intelligence Can Humans Bear?

“Do You Know How It Was?”: The History of AI

How Does AI Function?: AI Techniques

How Is AI Being Realized?: AI Determines Our Lives

Are You Still Buying or Are You Already “Influencing”?: Trade 4.0

Where to Go with the “Social Fallow”?: Industry 4.0

How Is Our Togetherness Changing?: Social Implications of AI

Paradise Times or the End of the World?: The Future with AI

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The Feeling Economy. How Artificial Intelligence Is Creating the Era of Empathy

We have seen that artificial intelligence (AI) is in the process of ushering in a new era that will have profound implications for how humans work and live. The emerging “Feeling Economy” is one in which AI assumes many of the mechanical and thinking tasks, leaving humans to emphasize feeling. Just as many people’s lives were transformed in the 1900s by the industrial revolution and automation, people’s lives are now again being transformed.

The transformation in the last century was from physical and mechanical tasks to thinking tasks. In the twenty-first century, the transformation is from thinking tasks to feeling tasks. Artificial intelligence is developing in the order of (a) mechanical, to (b) thinking, to (c) feeling. Mechanical AI is easiest, and is mostly accomplished already. Thinking intelligence is next easiest, and is an area of strong current innovation. Feeling intelligence is the hardest for AI, and competence in that is probably decades away.

The main thesis of this book is: As AI assumes more thinking tasks, humans will emphasize feeling. Our research, both theoretical and empirical, provides initial support for this thesis.

 

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 Atlas of AI. Power, Politics, and the Planetary Costs of Artificial Intelligence

Artificial intelligence is not an objective, universal, or neutral computational technique that makes determinations without human direction. Its systems are embedded in social, political, cultural, and economic worlds, shaped by humans, institutions, and imperatives that determine what they do and how they do it. They are designed to discriminate, to amplify hierarchies, and to encode narrow classifications. When applied in social contexts such as policing, the court system, health care, and education, they can reproduce, optimize, and amplify existing structural inequalities. This is no accident: AI systems are built to see and intervene in the world in ways that primarily benefit the states, institutions, and corporations that they serve. In this sense, AI systems are expressions of power that emerge from wider economic and political forces, created to increase profits and centralize control for those who wield them. But this is not how the story of artificial intelligence is typically told.

The standard accounts of AI often center on a kind of algorithmic exceptionalism—the idea that because AI systems can perform uncanny feats of computation, they must be smarter and more objective than their flawed human creators.

 

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 The Doctor and the Algorithm. Promise, Peril, and the Future of Health AI

AI is a clear and present danger to health, safety, and equity. AI also has the potential to improve clinical care profoundly. Both of these statements are true, and both are false by dint of their incompleteness. This kind of indeterminacy is a common problem in medicine. Famously, pharmakon (the Ancient Greek word at the root of pharmacy) means “drug” but can connote either cure or poison. The Paracelsian maxim that “the dose makes the poison” is likewise a common, albeit misleading, trope of introductory pharmacology. In many ways, AI is a pharmakon. It can be both cure and poison. The indeterminacy of a pharmakon is inarguably a challenge for medicine, but it does not bring healthcare to a halt. Rather, doctors, researchers, and regulators have slowly built up, over the centuries, systems of checks and balances that ideally lead toward more pharmakon-qua-cure than pharmakon-qua-poison. Please do not misunderstand me. This has certainly not been some sort of steady progression toward a better world. I am not trying to sell a story about the inevitability of scientific progress.

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Doing AI: A Business-Centric Examination of AI Culture, Goals, and Values

A common external goal for artificial intelligence is cognitive plausibility. That is, in order to qualify as “real,” a solution must solve intelligence in much the way humans are intelligent. When it is discovered that a solution is not anthropomorphic enough, many dismiss the accomplishment. In other words, how insiders solve puzzles is as important as how they define puzzles.

 Is AI About Simulating the Brain?The answer is sometimes, but not always. However, many insiders believe that if a solution looks like the brain, then it might actually act like the brain. When a solution doesn’t act like the brain, insiders conclude that the solution teaches them nothing about the brain or intelligence. Simulating the brain effectively requires insiders to reverse engineer it. The so-called inverse problem is the process of calculating from a set of observations the causal factors that produced them. In other words: starting with the answer and working backward to the question. However, reverse engineering the brain and studying intelligence will always be an exercise that is more complex, with much longer payoffs, than identifying and solving real-world problems.

 

AI everywhere (5)

Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition

 In  SECTION II of the book, you'll find: Conversations with Today’s Thought Leaders on A.I., and inside there are interviews on health and medicine applications.

AI everywhere (8)

 AI Assistants

A useful introductory book with this contents:

1 What Is a Virtual Assistant?

2 AI and Machine Learning

3 Speech Recognition

4 Natural Language Understanding

5 Natural Language and Speech Generation

6 The Dialog Manager

7 Interacting with an Assistant

8 Conclusions

 

 

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 The Tyranny of Algorithms: Freedom, Democracy, and the Challenge of AI

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 Machine Learning and AI for Healthcare. Big Data for Improved Health Outcomes

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Artificial You: AI and the Future of Your Mind

 

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 Machines Behaving Badly: The Morality of AI

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Rule of the Robots. How Artificial Intelligence Will Transform Everything

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 Technology Is Not Neutral: A Short Guide to Technology Ethics

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 Algorithms Are Not Enough. Creating General Artificial Intelligence

In the last chapter:

An artificial general intelligence agent will need to:

• Address ill-defined problems as well as well-formed problems.

• Find or create solutions to insight problems.

• Create representations of situations and models. What do the inputs look like; how is the problem solution structured (modeled)? What is the appropriate output of the system?

• Exploit nonmonotonic logic, allowing contradictions and exceptions.

• Specify its own goals, perhaps in the context of some overarching long-range goal.

Transfer learning from one situation to another and recognize when the transfer is interfering with the performance of the second task.

• Utilize model-based similarity. Similarity is not just a feature-by- feature comparison but depends on the context in which the judgment is being conducted.

• Compare models. An intelligent agent has to be able to compare the model that it is optimizing with other potential models (representations) that might address the same problem.

• Manage analogies. It must manage analogies to select the ones that are appropriate and to identify the properties of the analogs that are relevant.

• Resolve ambiguity. Situations and even words can be extremely ambiguous.

• Make risky predictions.

• Reconceptualize, reparamaterize, and revise rules and models.

• Recognize patterns in data.

• Use heuristics even if their efficacy cannot be proven.

• Extract overarching principles.

• Employ cognitive biases. Although they can lead to incorrect conclusions, they are often helpful heuristics.

• Exploit serial learning with positive transfer and without catastrophic forgetting.

• Create new tasks.

• Create and exploit commonsense knowledge beyond what is specified explicitly in the problem description. Commonsense knowledge will require the use of new nonmonotonic representations.

I believe that with the right investments, we will be able to develop computer systems that are capable of the full panoply of human intelligence. We cannot limit ourselves to looking where the light is bright and the tasks are easy to evaluate.

At some point, these computational intelligences may be able to exceed the capability of human beings, but it won’t be any kind of event horizon or intelligence explosion. Intelligence depends on content as well as or perhaps more than processing capacity. The need for content and the need for feedback will limit the speed of further developments. If we fail to develop artificial general intelligence, our failure will not be, I think, a technological failure, but one of our own imagination.

Glups! 

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 Artificial Intelligence-based software as a medical device

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 Artificial intelligence in medicine regulation

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A Human Algorithm. How Artificial Intelligence Is Redefining Who We Are 

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 White Paper on Artificial Intelligence applied to health