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Toward Machines
that behave Ethically
Better than Humans

Matthijs Pontier
Johan Hoorn
Outline of this presentation
•   Background
•   Existing approaches
•   Our moral reasoning system
•   Results
•   Discussion
•   Future




Maastricht, October 25th 2012   BNAIC 2012   2
Background
• Machines interact more with people
• Machines are becoming more autonomous
• Rosalind Picard (1997): ‘‘The greater the freedom of
  a machine, the more it will need moral standards.’’
• We should manage that machines do not harm us or
  threaten our autonomy




Maastricht, October 25th 2012   BNAIC 2012               3
Existing approaches
Bottom-up: Casuistry
•   Look at previous (similar) cases and use statistical methods to
    make a moral decision

      • Based on the internet (Rzepka & Ariki, 2005)
         • But: will never be better than humans
      • Based on training examples in a neural network
         • But: reclassification is problematic (Guarini, 2006)

•   Conclusion: Casuistry alone is not enough (Guarini, 2006)




Maastricht, October 25th 2012   BNAIC 2012                            4
Existing approaches
Top-down: Two competitors
• Utilitarianism:
      • Try to maximize the total amount of utility in the world


• Ethics of rights and duties
      • Individuals have rights and duties
      • Learn rule-based decision procedure via machine-learning
        to make moral decisions (Anderson, Anderson & Armen,
        2006)


• Are these two competitors so different?



Maastricht, October 25th 2012   BNAIC 2012                         5
Hybrid approach:
Top-down & Bottom-up
• Wallach, Franklin & Allen (2010):
  "Approach Anderson, Anderson & Armen (2006) can’t
  handle complexity human decisions”
• Combine top-down and bottom-up:
  Neural network with Top-down processes to interpret
  situation and predict possible results actions

• But: not fully implemented yet




Maastricht, October 25th 2012   BNAIC 2012              6
Domain: Medical Ethics

• Within SELEMCA, we develop caredroids
• Patients are in a vulnerable position. Moral behavior
  of robot is extremely important.
 We focus on Medical Ethics

• Conflicts between:
1. Beneficence
2. Non-maleficence
3. Autonomy



Maastricht, October 25th 2012   BNAIC 2012                7
Our moral reasoning system
• Combination top-down / bottom-up:
  Weighted association network




Maastricht, October 25th 2012   BNAIC 2012   8
Calculating morality action
• Morality(Action) =
ΣGoal( Belief(facilitates(Action, Goal)) * Ambition(Goal))
Moral Goal                                   Ambition level
                                             0.74
Non-Maleficence
                                             0.52
Beneficence
                                             1
Autonomy
 IF   Belief(facilitates(Action, autonomy) =
max_value
 THEN Moralilty(Action) = Morality(Action) + 2

Maastricht, October 25th 2012   BNAIC 2012                    9
Results
Behavior system matches experts medical ethics
Experiment 1        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5                1           1       0.76
Accept               0.5               -1          -1      -0.8
Experiment 2        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5                1           1      0.76
Accept               1                 -1          -1      1.70
Experiment 3        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5                0.5         0.5    0.13
Accept               1                 -0.5        -0.5    2.37
Experiment 4        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5               0            0.5    -0.26
Accept               0.5               0           -0.5     0.26
Experiment 5        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5                0.5         0.5     0.13
Accept               0.5               -0.5        -0.5    -0.13
Experiment 6        Autonomy           Non-Malef   Benef   Morality
Try Again           -0.5               0           1        0.04
Accept               0.5               0           -1      -0.04



Maastricht, October 25th 2012   BNAIC 2012
Discussion
• Often there exists no consensus on the correct option
  with moral dilemma’s
• Dependent on context / application
   • Entertainment: ‘bad’ characters can be enjoyable
   • Companion robot / Virtual friend: Morality action
     one of several influences
   • Decision-support: Strict moral code

• Does full autonomy exist?

• Daniel Dennett (2006) “AI makes philosophy honest”
Maastricht, October 25th 2012   BNAIC 2012                11
Robots that behave ethically
better than humans
•   Human behavior is typically far from being morally ideal
•   Humans are not very good at making impartial decisions
•   Machines can be good at making impartial decisions
•   Machines could behave ethically better than humans
•   Machines may inspire us to behave ethically better ourselves
    (Anderson & Anderson, 2010)




Maastricht, October 25th 2012   BNAIC 2012                         12
Limitations moral reasoning
• Wallack, Franklin & Allen (2010): “even agents who
  adhere to a deontological ethic or are utilitarians may
  require emotional intelligence as well as other ‘‘supra-
  rational’’ faculties, such as a sense of self and a
  theory of mind”
• Tronto (1993): “Care is only thought of as good care
  when it is personalized”
• Only moral reasoning results in very cold decision-
  making, only in terms of rights and duties




Maastricht, October 25th 2012   BNAIC 2012                   13
Add Emotional Intelligence

• Previously, we developed Silicon Coppelia, a model
  of emotional intelligence. This can also be projected
  in others, for Theory of Mind

 Connect Moral Reasoning to Silicon Coppelia
   • More human-like moral reasoning
   • Personalize moral decisions and communication
     about moral reasoning




Maastricht, October 25th 2012   BNAIC 2012                14
Future: Applications




Maastricht, October 25th 2012   BNAIC 2012   15
Future: Applications

• Persuasive Technology. Moral dilemmas about:
   • Helping vs Manipulating

• Choose actions that:
   • Improve autonomy patient
   • Improve well-being patient
   • Do not harm patient
   • Distribute resources equally among patients




Maastricht, October 25th 2012   BNAIC 2012         16
Questions?




Maastricht, October 25th 2012   BNAIC 2012   17

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Toward machines that behave ethically better than humans do - Slides BNAIC 2012

  • 1. Toward Machines that behave Ethically Better than Humans Matthijs Pontier Johan Hoorn
  • 2. Outline of this presentation • Background • Existing approaches • Our moral reasoning system • Results • Discussion • Future Maastricht, October 25th 2012 BNAIC 2012 2
  • 3. Background • Machines interact more with people • Machines are becoming more autonomous • Rosalind Picard (1997): ‘‘The greater the freedom of a machine, the more it will need moral standards.’’ • We should manage that machines do not harm us or threaten our autonomy Maastricht, October 25th 2012 BNAIC 2012 3
  • 4. Existing approaches Bottom-up: Casuistry • Look at previous (similar) cases and use statistical methods to make a moral decision • Based on the internet (Rzepka & Ariki, 2005) • But: will never be better than humans • Based on training examples in a neural network • But: reclassification is problematic (Guarini, 2006) • Conclusion: Casuistry alone is not enough (Guarini, 2006) Maastricht, October 25th 2012 BNAIC 2012 4
  • 5. Existing approaches Top-down: Two competitors • Utilitarianism: • Try to maximize the total amount of utility in the world • Ethics of rights and duties • Individuals have rights and duties • Learn rule-based decision procedure via machine-learning to make moral decisions (Anderson, Anderson & Armen, 2006) • Are these two competitors so different? Maastricht, October 25th 2012 BNAIC 2012 5
  • 6. Hybrid approach: Top-down & Bottom-up • Wallach, Franklin & Allen (2010): "Approach Anderson, Anderson & Armen (2006) can’t handle complexity human decisions” • Combine top-down and bottom-up: Neural network with Top-down processes to interpret situation and predict possible results actions • But: not fully implemented yet Maastricht, October 25th 2012 BNAIC 2012 6
  • 7. Domain: Medical Ethics • Within SELEMCA, we develop caredroids • Patients are in a vulnerable position. Moral behavior of robot is extremely important.  We focus on Medical Ethics • Conflicts between: 1. Beneficence 2. Non-maleficence 3. Autonomy Maastricht, October 25th 2012 BNAIC 2012 7
  • 8. Our moral reasoning system • Combination top-down / bottom-up: Weighted association network Maastricht, October 25th 2012 BNAIC 2012 8
  • 9. Calculating morality action • Morality(Action) = ΣGoal( Belief(facilitates(Action, Goal)) * Ambition(Goal)) Moral Goal Ambition level 0.74 Non-Maleficence 0.52 Beneficence 1 Autonomy IF Belief(facilitates(Action, autonomy) = max_value THEN Moralilty(Action) = Morality(Action) + 2 Maastricht, October 25th 2012 BNAIC 2012 9
  • 10. Results Behavior system matches experts medical ethics Experiment 1 Autonomy Non-Malef Benef Morality Try Again -0.5 1 1 0.76 Accept 0.5 -1 -1 -0.8 Experiment 2 Autonomy Non-Malef Benef Morality Try Again -0.5 1 1 0.76 Accept 1 -1 -1 1.70 Experiment 3 Autonomy Non-Malef Benef Morality Try Again -0.5 0.5 0.5 0.13 Accept 1 -0.5 -0.5 2.37 Experiment 4 Autonomy Non-Malef Benef Morality Try Again -0.5 0 0.5 -0.26 Accept 0.5 0 -0.5 0.26 Experiment 5 Autonomy Non-Malef Benef Morality Try Again -0.5 0.5 0.5 0.13 Accept 0.5 -0.5 -0.5 -0.13 Experiment 6 Autonomy Non-Malef Benef Morality Try Again -0.5 0 1 0.04 Accept 0.5 0 -1 -0.04 Maastricht, October 25th 2012 BNAIC 2012
  • 11. Discussion • Often there exists no consensus on the correct option with moral dilemma’s • Dependent on context / application • Entertainment: ‘bad’ characters can be enjoyable • Companion robot / Virtual friend: Morality action one of several influences • Decision-support: Strict moral code • Does full autonomy exist? • Daniel Dennett (2006) “AI makes philosophy honest” Maastricht, October 25th 2012 BNAIC 2012 11
  • 12. Robots that behave ethically better than humans • Human behavior is typically far from being morally ideal • Humans are not very good at making impartial decisions • Machines can be good at making impartial decisions • Machines could behave ethically better than humans • Machines may inspire us to behave ethically better ourselves (Anderson & Anderson, 2010) Maastricht, October 25th 2012 BNAIC 2012 12
  • 13. Limitations moral reasoning • Wallack, Franklin & Allen (2010): “even agents who adhere to a deontological ethic or are utilitarians may require emotional intelligence as well as other ‘‘supra- rational’’ faculties, such as a sense of self and a theory of mind” • Tronto (1993): “Care is only thought of as good care when it is personalized” • Only moral reasoning results in very cold decision- making, only in terms of rights and duties Maastricht, October 25th 2012 BNAIC 2012 13
  • 14. Add Emotional Intelligence • Previously, we developed Silicon Coppelia, a model of emotional intelligence. This can also be projected in others, for Theory of Mind  Connect Moral Reasoning to Silicon Coppelia • More human-like moral reasoning • Personalize moral decisions and communication about moral reasoning Maastricht, October 25th 2012 BNAIC 2012 14
  • 15. Future: Applications Maastricht, October 25th 2012 BNAIC 2012 15
  • 16. Future: Applications • Persuasive Technology. Moral dilemmas about: • Helping vs Manipulating • Choose actions that: • Improve autonomy patient • Improve well-being patient • Do not harm patient • Distribute resources equally among patients Maastricht, October 25th 2012 BNAIC 2012 16

Notas del editor

  1. Belangrijkheid doelen op basis van literatuur Regel toegevoegd, omdat volledig autonome beslissingen nooit ge-questioned mogen worden.
  2. Robot in verschillende afdelingen ziekenhuis: - Post-natale afdeling: Robot is geweldig - Oncologie afdeling: Robot is bot, gedraagt zich ongepast, irritant  wordt geschopt Autonomie  Vrij van interne / externe constraints Dennett  Hidden mechanisms can be overlooked als je ze niet expliciet maakt