A Computational Model of Moral and Legal Responsibility via Simplicity Theory
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Transcript of A Computational Model of Moral and Legal Responsibility via Simplicity Theory
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15 December 2017, JURIX @ Luxembourg
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with the (supposedly) near advent of autonomous artificial entities, or similar forms of distributed automatic decision making,
to define operationally the notion of responsibility
becomes of primary importance.
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● Traditional research track in AI & Law:
How to compute responsibility?
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● Traditional research track in AI & Law:
– structural (logical) approaches● focus on reasoning constructs: Ontologies [Lehmann et al., 2004],
Inferences [Prakken, 2002] or Stories [Bex et al., 2000]
How to compute responsibility?
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● Traditional research track in AI & Law:
– structural (logical) approaches● focus on reasoning constructs: Ontologies [Lehmann et al., 2004],
Inferences [Prakken, 2002] or Stories [Bex et al., 2000]
– quantitative approaches● focus on relative support of evidence: Bayesian inference [Fenton et
al., 2012], Causal Bayesian Networks [Halpern, 2015]
How to compute responsibility?
![Page 6: A Computational Model of Moral and Legal Responsibility via Simplicity Theory](https://reader033.fdocuments.net/reader033/viewer/2022052915/5a6cee4c7f8b9ac2418b4811/html5/thumbnails/6.jpg)
● Traditional research track in AI & Law:
– structural (logical) approaches● focus on reasoning constructs: Ontologies [Lehmann et al., 2004],
Inferences [Prakken, 2002] or Stories [Bex et al., 2000]
– quantitative approaches● focus on relative support of evidence: Bayesian inference [Fenton et
al., 2012], Causal Bayesian Networks [Halpern, 2015]
– hybrid methods [Vlek et al., 2014], [Verheij, 2014]
How to compute responsibility?
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● Traditional research track in AI & Law:
– structural (logical) approaches● focus on reasoning constructs: Ontologies [Lehmann et al., 2004],
Inferences [Prakken, 2002] or Stories [Bex et al., 2000]
– quantitative approaches● focus on relative support of evidence: Bayesian inference [Fenton et
al., 2012], Causal Bayesian Networks [Halpern, 2015]
– hybrid methods [Vlek et al., 2014], [Verheij, 2014]
● Here we introduce an alternative research direction, building upon cognitive models.
How to compute responsibility?
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● In human societies, responsibility attribution is a spontaneous and seemingly universal behaviour.
Responsibility attribution for humans12 Angry Men, 1956Rashomon, 1950
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● In human societies, responsibility attribution is a spontaneous and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to modern law and seem perfectly sensible nowadays.
Responsibility attribution for humansRashomon, 1950 12 Angry Men, 1956
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● In human societies, responsibility attribution is a spontaneous and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to modern law and seem perfectly sensible nowadays.
→ responsibility attribution may be controlled by fundamental cognitive mechanisms.
Responsibility attribution for humans12 Angry Men, 1956Rashomon, 1950
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● In human societies, responsibility attribution is a spontaneous and seemingly universal behaviour.
● Non-related ancient legal systems bear much resemblance to modern law and seem perfectly sensible nowadays.
→ responsibility attribution may be controlled by fundamental cognitive mechanisms.
Responsibility attribution for humans
Working hypothesis: attributions of moral and legal responsibility share a similar cognitive architecture
12 Angry Men, 1956Rashomon, 1950
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● Experiments show that people are more prone to blame an agent for an action:
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012, pages 947–952]
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● Experiments show that people are more prone to blame an agent for an action:
– the more the outcome is severe,
– the more they are closer to the victims,
– the more the outcome follows the action.
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012, pages 947–952]
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● Experiments show that people are more prone to blame an agent for an action:
– the more the outcome is severe,
– the more they are closer to the victims,
– the more the outcome follows the action.
● The cognitive model of Simplicity Theory predicts these results.
flooded mine dilemma (trolley problem variation)
[A. Saillenfest and J.-L. Dessalles. Role of Kolmogorov Complexity on Interest in Moral Dilemma Stories. CogSCI 2012]
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityconcerning how the world generates the situation
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityconcerning how the world generates the situation
description complexityconcerning how to identify the situation
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityconcerning how the world generates the situation
description complexityconcerning how to identify the situation
The two complexities are defined following Kolmogorov complexity.
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Kolmogorov complexity
length in bits of the shortest program generating a string description of an object
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Kolmogorov complexity
length in bits of the shortest program generating a string description of an object
string equivalent programs
“2222222222222222222222222” = “2” + “2” + … + “2” = “2” * 25
= “2” * 5^2
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Kolmogorov complexity
length in bits of the shortest program generating a string description of an object
depends on the available operators!!
string equivalent programs
“2222222222222222222222222” = “2” + “2” + … + “2” = “2” * 25
= “2” * 5^2
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityabout how the world generates the situation
description complexityabout how to identify the situation
length of shortest program creating the situation
length of shortest program determining the situation
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityabout how the world generates the situation
description complexityabout how to identify the situation
length of shortest program creating the situation
instructions = causal operators
length of shortest program determining the situation
instructions = mental operators
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityabout how the world generates the situation
description complexityabout how to identify the situation
length of shortest program creating the situation
instructions = causal operators
length of shortest program determining the situation
instructions = mental operators
SIMULATION
REPRESENTATION
SIMULATION
REPRESENTATION
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Simplicity theory● Human individuals are highly sensitive to complexity drops: i.e.
to situations that are simpler to describe than to explain.
● Core notion: Unexpectedness
causal complexityabout how the world generates the situation
description complexityabout how to identify the situation
length of shortest program creating the situation
instructions = causal operators
length of shortest program determining the situation
instructions = mental operators
SIMULATION
REPRESENTATION
SIMULATION
REPRESENTATION
for the agent!!!
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Examples
22222222222222 is more unexpected than 21658367193445 (in a fair extraction)
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Examples
22222222222222 is more unexpected than 21658367193445
meeting Obama is more unexpected than meeting Dupont
(in a fair extraction)
Unexpectedness captures plausibility
(or any other famous person) (or any other unknown person)
meeting an old of friend of mine(or any other known person)
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● Focusing on intensity, we can capture anticipation as:
emotionwhat the situation induces to the agent
reward inverse model
unexpectedness
Simplicity Theory: Intention
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● Focusing on intensity, we can capture anticipation as:
● If the agent A expects that the best way to bring about s is via a:
emotionwhat the situation induces to the agent
reward inverse model
unexpectedness
Simplicity Theory: Intention
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● Focusing on intensity, we can capture anticipation as:
● If the agent A expects that the best way to bring about s is via a:
emotionwhat the situation induces to the agent
reward inverse model
unexpectedness
Simplicity Theory: Intention
intention as driven by anticipated emotional effects
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Simplicity Theory: Intention● Focusing on intensity, we can capture anticipation as:
● If the agent A expects that the best way to bring about s is via a:
emotionwhat the situation induces to the agent
reward inverse model
unexpectedness
intention as driven by anticipated emotional effects
inadvertence
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Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is one of point of views.
computed by A
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Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is one of point of views.
computed by A
computed by a model of Acomputed by an observer O
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Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is one of point of views.
computed by A
computed by a model of Acomputed by an observer O
prescribed role, reasonable standard
reward inverse model
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Simplicity Theory: Moral responsibility
● Difference between intention and moral responsibility is one of point of views.
● Introducing causal responsibility
computed by A
computed by a model of Acomputed by an observer O
prescribed role, reasonable standard
reward inverse model
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Simplicity Theory: Moral responsibility
actualized emotion
causalresponsibility
conceptualremoteness inadvertence
+ + – –for observer O attributed to A attributed to Afor observer O
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Simplicity Theory: Moral responsibility
actualized emotion
causalresponsibility
conceptualremoteness inadvertence
+ + – –for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law (e.g. the “death of a star” case)
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Simplicity Theory: Moral responsibility
actualized emotion
causalresponsibility
conceptualremoteness inadvertence
+ + – –for observer O attributed to A attributed to Afor observer O
● From moral to legal responsibility:
– equity before the law (e.g. the “death of a star” case)
– law, as a reward system, defines emotion
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Example 1: Negligent huntersSummers v. Tice (1948), 33 Cal.2d 80, 199 P.2d 1
Two hunters shot at the same time harming their guide.
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Example 1: Negligent huntersSummers v. Tice (1948), 33 Cal.2d 80, 199 P.2d 1
they thought the harm was impossible
Two hunters shot at the same time harming their guide.
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Example 1: Negligent huntersSummers v. Tice (1948), 33 Cal.2d 80, 199 P.2d 1
they thought the harm was impossible
but it was reasonable to consider the danger
Two hunters shot at the same time harming their guide.
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Example 1: Negligent huntersSummers v. Tice (1948), 33 Cal.2d 80, 199 P.2d 1
they thought the harm was impossible
but it was reasonable to consider the danger
therefore they're (morally) equally responsible.
Two hunters shot at the same time harming their guide.
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Example 1: Negligent huntersSummers v. Tice (1948), 33 Cal.2d 80, 199 P.2d 1
they thought the harm was impossible
but it was reasonable to consider the danger
therefore they're (morally) equally responsible.
negligence
Two hunters shot at the same time harming their guide.
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Example 2: Navigating oilOverseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
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Overseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
Example 2: Navigating oil
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Overseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
with poor maintenance, sea contamination by oil leakage predictable
Example 2: Navigating oil
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Overseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
fire after oil leakage in sea difficult to occur
Example 2: Navigating oil
with poor maintenance, sea contamination by oil leakage predictable
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Overseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
fire after oil leakage in sea difficult to occur
therefore, defendant is not responsible
Example 2: Navigating oil
with poor maintenance, sea contamination by oil leakage predictable
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Overseas Tankship (UK) Ltd v. Morts Dock and Eng. Co Ltd – “Wagon Mound (No. 1)” (1961), UKPC 2.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
fire after oil leakage in sea difficult to occur
therefore, defendant is not responsible
foreeseability
Example 2: Navigating oil
with poor maintenance, sea contamination by oil leakage predictable
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Example 3: Navigating oil, continuedOverseas Tankship (UK) Ltd v The Miller Steamship Co – “Wagon Mound (No. 2)” (1967), 1 AC 617.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
NEW EVIDENCE: flammable objects in the water.
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Example 3: Navigating oil, continuedOverseas Tankship (UK) Ltd v The Miller Steamship Co – “Wagon Mound (No. 2)” (1967), 1 AC 617.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
NEW EVIDENCE: flammable objects in the water.
with poor maintenance, sea contamination by oil leakage predictable
fire after oil leakage possible, because of flammable objects
therefore, defendant is responsible
1st argument: foreseeability
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Overseas Tankship (UK) Ltd v The Miller Steamship Co – “Wagon Mound (No. 2)” (1967), 1 AC 617.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
NEW EVIDENCE: flammable objects in the water.
Example 3: Navigating oil, continued
2nd argument: weighting of risks(anticipations)
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Overseas Tankship (UK) Ltd v The Miller Steamship Co – “Wagon Mound (No. 2)” (1967), 1 AC 617.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
NEW EVIDENCE: flammable objects in the water.
risk
Example 3: Navigating oil, continued
2nd argument: weighting of risks(anticipations)
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Overseas Tankship (UK) Ltd v The Miller Steamship Co – “Wagon Mound (No. 2)” (1967), 1 AC 617.
At a landing stage oil was spilled for days in the sea.
It was then ignited during works on a ship nearby.
NEW EVIDENCE: flammable objects in the water.
risk
risk as generalization of foreseeability: Hart and Honoré’s view!
Example 3: Navigating oil, continued
2nd argument: weighting of risks(anticipations)
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Conclusions● Our contribution attempts to open an alternative research
track for the computation of responsibility in AI & Law.
![Page 57: A Computational Model of Moral and Legal Responsibility via Simplicity Theory](https://reader033.fdocuments.net/reader033/viewer/2022052915/5a6cee4c7f8b9ac2418b4811/html5/thumbnails/57.jpg)
Conclusions● Our contribution attempts to open an alternative research
track for the computation of responsibility in AI & Law.
● Underlying model derived from general cognitive functions (SIMULATION, REPRESENTATION, REWARD INVERSE MODEL)
![Page 58: A Computational Model of Moral and Legal Responsibility via Simplicity Theory](https://reader033.fdocuments.net/reader033/viewer/2022052915/5a6cee4c7f8b9ac2418b4811/html5/thumbnails/58.jpg)
Conclusions● Our contribution attempts to open an alternative research
track for the computation of responsibility in AI & Law.
● Underlying model derived from general cognitive functions (SIMULATION, REPRESENTATION, REWARD INVERSE MODEL)
● It enables a smoother transition from moral to legal reasoning, and provides grounds to quantify legal concepts.
![Page 59: A Computational Model of Moral and Legal Responsibility via Simplicity Theory](https://reader033.fdocuments.net/reader033/viewer/2022052915/5a6cee4c7f8b9ac2418b4811/html5/thumbnails/59.jpg)
Conclusions● Our contribution attempts to open an alternative research
track for the computation of responsibility in AI & Law.
● Underlying model derived from general cognitive functions (SIMULATION, REPRESENTATION, REWARD INVERSE MODEL)
● It enables a smoother transition from moral to legal reasoning, and provides grounds to quantify legal concepts.
● Computation integrates quantitative and structural aspects: potential ground for unifying other approaches, e.g. exploiting explicit knowledge and probabilistic information.
– further work is needed for a complete operationalization and for detailed comparisons