Ethics of algorithms: the autonomy perspective

 Algorithms and Autonomy. The Ethics of Automated Decision Systems

This is a reference book on the key topics of ethics of artificial intelligence. Selected statements from the last chapter of this open access book:

We have argued that three broad facets of autonomy are affected by algorithmic systems. First, algorithmic systems are relevant to what we owe each other as autonomous agents. That is the focus of Chapters 3 and 4. In Chapter 3 we addressed the material conditions that we owe others and argued that respecting people as autonomous demands that any algorithmic system they are subjected to must be one that they can reasonably endorse. It does not require that they value particular outcomes or that they not bemade worse off by such systems. Rather, systemsmust either comport with agents’ own ends or be consistent with fair terms of social cooperation.We argued that persons being able to reasonably endorse a system turns on the system’s reliability, responsibility, stakes, and relative burden. Chapter 4 turned to the issues of what information we owe others. There we argued that people are owed information as a function of their practical agency (i.e., their ability to act and carry out plans in accord with their values) and as a function of their cognitive agency (i.e., their ability to exercise evaluative control over mental states, including beliefs, desires, and reactive responses). We offered several principles for information access grounded in agency. The second connection between algorithmic systems and autonomy is ensuring the conditions under which people are autonomous. 

In Chapter 5 we considered the relationship between algorithmic systems and freedom.We explained that algorithms bear upon negative, positive, and republican freedomand offered a general account of freedom as ecological non-domination. Key to understanding that ecology is recognizing three key challenges to freedom: affective challenges, deliberative challenges, and social challenges. In Chapter 6 we offered some suggestions for addressing some facets of those challenges. Specifically, we argue that a kind of epistemic paternalismis both permissible and (under some conditions) obligatory. Chapters 7 and 8 shift focus to the responsibilities of agents in light of the fact that they are autonomous. In Chapter 7 we argue that algorithmic systems allow agents deploying such systems to undermine a key component of responsibility, viz., providing an account for actions for which they are responsible. Specifically, we argue that complex systems create an opportunity for “agency laundering,” which involves a failure to meet one’s moral responsibility for an outcome by attributing causal responsibility to another person, group, process, or technology. Chapter 8 addresses a different facet of responsibility. Citizens within democratic states have a responsibility to exercise their autonomy in order to legitimate political authority. That is, they have a responsibility to help ensure that governments, laws, policies, and practices are justifiable. However, some kinds of algorithmic systems hinder citizens’ abilities to do that. They can do so by undermining the epistemic conditions necessary to underwrite the “normative authority” path to legitimacy or by undermining the exercise of autonomy necessary to underwrite the “democratic will” path to legitimacy.

 

 

A must read health policy textbook

 Understanding Health Policy: A Clinical Approach

If somebody asks me about a health policy textbook for students, I would say inevitably this one. Now in its 8th edition has updated many issues. Unfortunately we don't have a similar text with an european perspective. Many politicians and MP should read it to change their biased minds on health policy issues. The debate would improve notoriously.

AI everywhere (6)

 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

The welfare state as a social insurance mechanism

 Probable Justice. Risk, Insurance, and the Welfare State

This book is a review of the role of social insurance, from mutual insurance to the development of current welfare policies. Too often we forget that we have our public coverage of risk as the efficient solution for an intractable issue at individual level.

Key take-aways from last chapter:

I have advanced three principal claims about probability theory and its relationship to welfare thinking. The first is that mathematical probability is frequently, if not inherently, normative in its character. We saw that the very project of quantifying probabilities grew out of a moral and legal question, namely the need to apportion fair shares in an interrupted game of chance. Each subsequent account of probability has in turn both reflected and furthered the practical aims of its exponents. This should not be  surprising, given that the discipline is at its core an attempt to guide good judgment and quantify equality, both of which are normative efforts, closely linked to views about the ends of human action and justice broadly understood.

The second claim, which follows from the first, is that theorists of mathematical probability have long tried to reconcile individual choice with some account of the common good. Not long after the  founding of the discipline, probabilists began to recognize a potential disconnect between personal prudence, or common sense, and contractual fairness as defined by their calculations. Many subsequent contributions to the theory attempted to resolve this problem in its various forms. Each account had a different character and resulted in different proposals. Yet they shared the promise of harmonizing individual judgments with aggregate regularities, which respectively correspond to the two sides of probability itself.

Finally, I have argued that the answers to this problem that emerged in connection with probability theory, from roughly the end of the eighteenth century through the twentieth, played a crucial role in the development of the modern welfare state. Statistical insurance was the first practice in which philosophers of probability sought, and in their view found, the means to reconcile individual benefit with a common good. The application of insurance principles on a social scale therefore promised to extend such harmony well beyond isolated associations to the polity as a whole. Insurance would refl ect the free choices of individuals while simultaneously securing social order. It would give each citizen her due while promoting the aggregate benefit. And it would distribute resources on the basis of both personal responsibility and equal vulnerability or need, accommodating the two principles without clearly favoring either one.

And a reflection, 

Any book about social insurance must address, at least briefly, the most pressing political controversies surrounding the welfare state today: namely, the problem of finance and the question of personal responsibility. If at one point the rubric of insurance invoked an image of fi scal restraint, promising to limit what the state distributes to the amount that it collects in contributions, the welfare state has come to be identifi ed among critics with out- of- control spending and government debt. And if mutual insurance was originally touted as a reflection of prudence and a means to propertied independence, it is now commonly associated with what economists refer to as moral hazard, meaning the  encouragement of risky and expensive behaviors, as well as dependence on the state. It is true that, in most advanced welfare states, social expenditures increased over the course of the twentieth century, not only in absolute terms but also as a percentage of gross domestic product. Some scholars have explained this phenomenon as a product of Wagner’s Law, which predicts that the share of government spending relative to GDP will increase with rising incomes.  As citizens grow wealthier and live longer, this argument goes, they will increasingly seek out the kind of quality- of- life improvements provided by the risk- pooling and consumption- smoothing functions of the welfare state, including healthcare,  pensions, and education.

Genetic luck

 The Genetic Lottery: Why DNA Matters for Social Equality

In The Genetic Lottery, Harden introduces readers to the latest genetic science, dismantling dangerous ideas about racial superiority and challenging us to grapple with what equality really means in a world where people are born different. Weaving together personal stories with scientific evidence, Harden shows why our refusal to recognize the power of DNA perpetuates the myth of meritocracy, and argues that we must acknowledge the role of genetic luck if we are ever to create a fair society.

Reclaiming genetic science from the legacy of eugenics, this groundbreaking book offers a bold new vision of society where everyone thrives, regardless of how one fares in the genetic lottery.

State of the art in cost-effectiveness

 How COVID Can Help Us Refocus On The How And Why Of Value Assessment

An excellent summary on cost-effectiveness, an update on conventional knowledge.

The COVID pandemic has challenged us to reevaluate the role of health care in our personal lives and our society, with important implications for how we approach value as a guide for allocating limited health care resources. We should not squander these insights as the initial shock of the pandemic fades.

Rather, examining our health care system through the economic lens we’ve described—seeing it as a fundamentally flawed market failing to deliver the societal welfare that it could—can guide ongoing efforts to innovate in value assessment and health care delivery writ large.

Banksy in Barcelona

 

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.

Vaccine makers

 Vaxxers: The Inside Story of the Oxford AstraZeneca Vaccine and the Race Against the Virus

The inside story of the Oxford AstraZeneca vaccine, from two of the leading scientists who created it.

Beyond the vaccines:

It seems to me that there are three broad areas that limited our response to Covid-19, and that we need to improve in order to be in a better place the next time: infrastructure (including research and manufacturing), systems (including surveillance, stockpiling and travel bans) and global cooperation and collaboration. The solutions are not necessarily cheap or easy: but nor is dealing with a pandemic. We invest heavily in armed forces and intelligence and diplomacy to defend against wars. In the same way, we need to invest in pandemic preparedness to defend against pandemics.

 

Digital health: the day after

 Digital Health: Unlocking Value in a Post-Pandemic World

This article presents 3 categories of digital health and their relationships to value metrics: (1) telehealth or direct care delivery, (2) digital access tools, and (3) digital monitoring. An evidence-based discussion reveals past successes, current promises, and future challenges in reducing defects in value through digital care. In the coming years, value transformation will become more crucial to the success of health care systems. By using the taxonomy in this article, health systems can better implement digital tools with a value-driven purpose. Defining the role of digital health in the post-pandemic world is needed to assist health systems and practices to build a bridge to value-based care.

 

Payment systems for long-term care

 Pricing long-term care for older persons

Flores M. Increasing beneficiaries and the decline in informal care in the Spanish long-term care system for older persons.
WKC Policy Series on Long-Term Care No. 7: Spain

Further information on this site.

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Rule of the Robots. How Artificial Intelligence Will Transform Everything

Health systems performance

Mirror, Mirror 2021: Reflecting Poorly. Health Care in the U.S. Compared to Other High-Income Countries

Summary of the report : Norway has the best health care system among selected western countries, according to this research

Matching supply and demand for plasma

 From deficit to contribution: the case for voluntary compensated plasma collections in Spain

Summary of the report:

The Covid pandemic has exposed the threat to security of plasma supply that national blood operators have been warning about for years, resulting in a 25% decline in plasma donations in the United States. Since the US provides 75% of the world’s supply of plasma for the manufacture of plasma therapies, such a decline represents a threat to patients all over the world. Today only five countries allow the voluntary compensation of plasma donations: US, Austria, Czech Republic, Hungary, and Germany. These countries represent 5% of the world’s population but contribute to over 90% of plasma used for therapies. If the pharmaceutical industry can attend to current demand for plasma products like albumin and immunoglobulin, it is only thanks to the plasma collected in these countries.

Despite being home to Grífols, a world leader in plasma therapies, and despite the innovative approach that characterizes its organ donation and transplantation system, Spain and Catalonia employ an antiquated, inefficient, and ineffective model for plasma collection. The demand of immunoglobulin in Spain has increased 11.7% per year on average between 2015 and 2019. Relying entirely on non-compensated donors will lead to shortages and negatively impact R&D on new plasma-derived therapies.

The policy report published by The Ostrom Institute and coordinated by Peter Jaworski, Associate Teaching Professor at Georgetown University, provides empirical evidence on why Spain should allow the voluntary compensation of plasma donors and proposes a series of policy reforms. In short, Spain and Catalonia should mimic the Czech model of plasma collections and reform the Royal Decree 1088/2005 to allow voluntary compensation of plasma donors.

Food for thought.

 

 

AI everywhere (3)

 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!