Download Artificial Moral Agent (AMAs) Prospects and Approaches for Building

Artificial Moral Agent (AMAs)
Prospects and Approaches for Building Computer
Systems and Robots Capable of Making Moral
Decisions
Wendell Wallach
[email protected]
Yale University Institution for Social and Policy Studies
Interdisciplinary Center for Bioethics
December 10, 2006
A New Field of Inquiry
Machine Morality
Machine Ethics
Computational Ethics
Artificial Morality
Friendly AI
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Implementation of moral decisionmaking facilities in artificial agents
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Necessitated by autonomous
systems making choices
Mapping
the Field
Questions
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Do we need artificial moral agents
(AMAs)
• When? For what?
Do we want computers making ethical
decisions?
Whose morality or what morality?
How can we make ethics computable?
• What role should ethical theory play in
defining the control architecture for
systems sensitive to moral
considerations in their choices and
actions?
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Top-down imposition of ethical theory
Bottom-up building of systems that aim at goals
or standards which may or may not be specified
in explicit theoretical terms
Top-Down Theories
• Two main contenders
• Utilitarian - Greatest good of the
greatest number - “Only compute!”
• Duties (Deontology) - Respect for
rational agents - “Consistent Deontic
Logic”
Frame Problem
BOTH have a version of
the frame problem -computation load due to
requirements of:
Psychological knowledge
Knowledge of effects of
actions in the world
Estimating sufficiency of
initial information
Bottom Up Approaches
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Evolution
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Game Theorists and Evolutionary Psychologist
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AI Engineering -- exploit “self-organizing” feature of evolved systems
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Alife
Genetic Algorithms
Evolutionary Robotics
Development and Learning
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“Emergent morality” – The emergence of values in evolutionary terms
Associative Learning Platforms
Behavior Based Robotics
Simulating Moral Development – Piaget, Kohlberg, Gilligan, etc
Fine Tuning a System
Distinction
• Humans -- Biochemical,
• Instinctual, Emotion Platform
• Higher Order Faculties Emerged
• Computers -- Logical Platform
Possible Advantages
• Calculated Morality
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Does the ability of computers to process large
quantities of information, and analyze the potential
results from many courses of action, suggest that
computers will be superior to humans in making
judgments? (Allen, 2002)
• Absence of emotions or base motivations
(dubious
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Is the absence of a nervous system subject to
emotional “highjackings” a moral advantage?
(sexual jealousy)
Base motivations (greed)
Supra-rational Faculties and Social
Mechanisms
• Complex Faculty
• Emotions
• Stoicism vs. Moral Intelligence
• Embodiment
• Learning from Experience
• Sociability
• Understanding and Consciousness
• Theory of Mind
Role of Emotions in Moral DecisionMaking Machines
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Both beneficial and dysfunctional
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AMAs can be reasonable without being
subject to dysfunctional emotional
highjackings
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Will require some affective intelligence
Ability to recognize emotional states of
others
Will humans feel comfortable with machine
sensitive to their emotional states?
Sociability
Appreciation for both the verbal and non-verbal
aspects of human communication.
May be necessary for the acceptance by humans of
AMAs.
Embodied Intelligence v
Moral Reasoning
• A robotic systems learning
from interaction with its
environment and humans,
not the mere application of
reasoning.
Consciousness, Theory
of Mind
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Breaking down complex mental activities or social
mechanisms into faculties which are themselves
composites of lower level skills
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Brian Scassellati – Theory of Mind
Reassembly
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Igor Alexander –Five Axioms – Consciousness/Machine
Consciousness
Do we have working theories?
Will advances in evolutionary robotics facilitate
integration?
Cooperation
• Between AMA’s and humans.
• Training agents to work together in pursuit
of a shared goal, e.g. RoboSoccer
• The development of interface standards
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that will allow agents to interact and
cooperate with other classes of artificial
entities.
Facilitating Trust in Machines
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Will artificial agents need to emulate
the full array of human faculties to
function as adequate moral agents
and to instill trust in their actions?
WHAT HAS BEEN
ENGINEERED SO FAR
• Not Much
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The Gap Between Possibility or Hype and
reality
AAAI
Machine Ethics Symposium, Fall 2005
•DUTY
- pro or con
•- Asimov’s “three laws of robotics” and machine metaethics – Susan Anderson
•- Deontological machine ethics – Tom Powers
•- Toward ethical robots via mechanized deontic logic – Arkoudas, Bringsjord &
Bello
•UTILITY - pro or con
•- There is no ‘I’ in ‘robot’: utilitarians and utilitarian robots – Christopher Grau
•- Utilibot: an autonomous robot based on utilitarianism – Christopher Cloos
•CASE-DRIVEN MODELS
•- Towards machine ethics: Implementing two theories – Anderson, Anderson &
Armen
•- How AI can help us to better understand moral cognition – Marcello Guarini
•- Lessons in machine ethics from two computational models – Bruce McLaren
•DEMOS
•- MedEthEx (Anderson & Anderson)
•- Ethical ANNS (Guarini)
•- Sirocco (McLaren)
AAAI Machine Ethics Symposium
•CHALLENGES AND OVERVIEWS
•- The nature and importance of machine ethics – Jim Moor
•- A robust view of machine ethics – Steve Torrance
•- Technological artifacts as moral carriers and mediators – Lorenzo Magnani
•- Machine morality: bottom-up and top-down approaches – Allen & Wallach
•- What could statistics do for ethics? – Rzepka & Araki
•- Ethical machines: the future can heed us – Selmer Bringsjord
•- Social and scientific implications of patents in AI – Brian O’Connell
•- Thoughts concerning the legal status of a non-biological machine – David
Calverly
•- Ethical robot as grand challenge – James Gips
Other Key Players
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“Beyond AI: Creating the Conscience of the Machine” -- Josh
Storrs Hall
“Artificial Morality” -- Peter Danielson
Friendly AI -- Eliezer Yudkowsky
Moral Agency for Mindless Machines --Luciano Floridi and J.W.
Sanders
Agents for Ethical Assistance -- Catriona Kennedy
Thousands of additional researchers
LIDA Cognitive Cycle
Stan Franklin & Bernie Baars (GWT)
Sensors &
Primitive
Feature
Detectors
Internal Stimulus
External Stimulus
8
SMA
(Subsumption
Network)
9
Action
Taken
Declarative Memory
(Sparse Distributed
Memory)
Interpret
8
1
Perception
Codelets
Effectors
2
Percept
Perceptual
Associative Memory
(Slip Net)
8
Action Selected
Action
Selection
(Behavior Net)
3
Copy Over
3
Cue
Interpret
Sensory-Motor
Memory
(Subsumption Net)
Pre-afference
Consolidation
Working Memory
Preconscious Buffers
(Workspace)
Object
8
Action &
Object
3
Local Associations
3
Cue
Perceptual Learning
Long-term
Working Memory
3
Local Associations
Transient Episodic
Memory
(Sparse Distributed
Memory)
Episodic Learning
4
Look At
Attention
Codelets
4
Coalitions
Attentional Learning
Procedural Memory
(Scheme Net)
6,7
Instantiate, bind,
activate schemes
5
Conscious
Broadcast
Procedural Learning
Conscious
Broadcast
4
Winning
Coalition
Competition for
Consciousness
Related Concerns
• Moral Agency
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For Mindless Systems? (Floridi and Sanders)
Criteria and Tests for Evaluation
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Moral Turing Test? (Allen et al., 2000)
Rights and Responsibilities
• Controls
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Punishment
Monitor Development
Control Reproduction
Futuristic Fears
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Relinquishment
Legal Responsibility
• Who is responsible when an AMA fails to
meet legal and ethical guidelines?
• What recourse is available for punishing
the AMA when it acts immorally or
illegally?
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DOES IT MAKE SENSE TO PUNISH
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A ROBOT?
Dangers
• Which avenues of research in the
development of artificial agents hold
potential dangers that we can foresee, and
how will we address these dangers?
• Which of these dangers can be managed in
the design of the systems and which
dangers may require the relinquishment of
further research?
• What areas of concern will need regulation
and oversight, and how might this be
managed in a manner that does not
interfere with scientific progress?
Self-Adapting and
Self-Healing Systems
• The capacity for a system to learn and to
change its programming in the process
of learning without violating basic moral
tenets.
• The prospect that self-healing systems
will alter their programming and behavior
in an undesirable manner.
Design Strategies for
Restraining AMAs
• Design strategies for building-in restraints
on the behavior of artificial agents.
• Limitations on the reliability and safety of
existing design strategies.
• If there are clear limits in our ability to
develop or manage AMAs, then it will be
incumbent upon us to recognize those
limits so that we can turn our attention
away from a false reliance on
autonomous systems and toward more
human intervention in the decisionmaking process of computers and
robots.
Hybrids
• Eventually we will need AMA’s which
maintain the dynamic and flexible morality
of bottom-up systems that accommodate
diverse inputs, while subjecting the
evaluation of choices and actions to topdown principles that represent ideals we
strive to meet.
Thanks To My
Colleagues
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Professor Colin Allen, Philosophy Dept.,
University of Indiana
Dr. Iva Smit, E&E Consultants,
Netterden, Netherlands