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Libros > Derecho / Nuevas Tecnologías / ADMINISTRACION ELECTRONICA > Algorithmic Governance in Digital Health

Algorithmic  Governance in Digital Health

Algorithmic  Governance in Digital Health
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  • ISBN: 978-84-1085-425-3
  • EAN: 9788410854253
  • Editorial: Aranzadi
  • Fecha de la edición:
  • Número de la edición: 1ª
  • Colección: Estudios
  • Nº Pág.: 170
  • Idiomas: Inglés

Materias:

Provides an in-depth exploration of how AI can ethically interact with digital health. Combining both theory and practice, the book presents an algorithmic governance model rooted in bioethics and human rights, examining the ethical, legal, and social impact of machine learning.

Each chapter addresses the dichotomy between technological advancement and ethical principles, advocating for alignment that centers individual autonomy in AI deployment. Additionally, it offers a mechanism to assess autonomy throughout the entire algorithmic lifecycle, ensuring technology genuinely serves human interests.

The book offers:

Critical analysis of the current state of artificial intelligence.
Proposal for a governance framework based on bioethics and human rights.
 Methodology to operationalize the principle of autonomy within the algorithmic cycle.

PROLOGUE.............................................................................................. 15
ITZIAR DE LECUONA
INTRODUCTION .................................................................................... 23
I. FRONTIERS OF ARTIFICIAL INTELLIGENCE
TECHNOLOGY IS NOT NEUTRAL .................................................. 33
1.1. The need for interdisciplinarity.................................................. 33
1.2. Dynamic values.............................................................................. 36
1.3. Bioethics and human rights......................................................... 39
1.3.1. The scope ................................................................................. 40
1.3.2. The substance.......................................................................... 43
1.4. The governance of artificial intelligence................................... 45
NAVIGATING ARTIFICIAL INTELLIGENCE ................................ 51
2.1. Foundation in artificial intelligence........................................... 51
2.2. The life cycle of machine learning ............................................. 56
2.2.1 The world................................................................................. 57
2.2.2. Knowledge representation ....................................................... 60
Data............................................................................................. 60
Representation.......................................................................... 61
2.2.3. Machine learning model.......................................................... 62
2.2.4. Prediction ................................................................................ 65
CONCEPTIONS OF ARTIFICIAL INTELLIGENCE....................... 67
3.1. Introduction .................................................................................... 67
3.2. The value of data............................................................................ 70
3.3. A misunderstood superior agent ................................................ 73
3.4. Passive agents................................................................................. 76
3.5. Solutionism..................................................................................... 78
3.6. A form of power............................................................................. 81
3.7. Machine learning in practice....................................................... 83
3.7.1. Demand for multidisciplinary work........................................ 84
3.7.2. Regulatory ignorance.............................................................. 85
3.7.3. Technosolutionism................................................................... 89
3.7.4. Challenges in collaboration ..................................................... 92
Keynotes ................................................................................................... 93
II. THE VALUE OF NORMS
THE EUROPEAN LANDSCAPE ......................................................... 99
4.1. The role of norms........................................................................... 99
4.2. Reference works............................................................................. 102
05
NORMATIVE FRAMEWORK REVIEW............................................ 111
5.1. The scope......................................................................................... 111
5.2. Measures for safety ....................................................................... 115
5.3. Measures to govern artificial intelligence................................. 119
5.4. A perspective on autonomy ......................................................... 124
LOST IN TRANSLATION .................................................................... 129
6.1. Shortcomings from a technical perspective.............................. 129
6.2. Shortcomings of the present framework................................... 131
KEYNOTES.............................................................................................. 137
III. AUTONOMY IN PRACTICE
THE VALUE OF AUTONOMY ............................................................ 143
7.1. Introduction .................................................................................... 143
7.2. Definition ........................................................................................ 146
THE DECISION-MAKING PROCESS............................................... 155
8.1. Autonomous decision-making.................................................... 155
8.2. Modeling the process.................................................................... 157
8.3. The impact of machine learning on autonomy........................ 160
THE ASSESSMENT ............................................................................... 165
9.1. The concept..................................................................................... 165
9.2. Autonomy when capturing the world ....................................... 167
9.3. Autonomy in knowledge representation.................................. 170
9.4. Autonomy in the machine learning model............................... 173
9.5. Autonomy in the prediction ........................................................ 176
Keynotes ................................................................................................... 179
CLOSING .................................................................................................. 177
GLOSSARY ............................................................................................... 189
BIBLIOGRAPHY...................................................................................... 193

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