The common good at the risk of algorithms What to do with AI? April 9th2021 E. de Rocquigny - ERIAC
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The commongood at the risk of algorithms
What to do with AI?
April 9th 2021
E.de Rocquigny
E. de Rocquigny MINI-CV
President of OperationData Inc. and advisor of half a dozen
algorithmic companies, founder think-tank Espérance & Algorithmes,
Form er Full Profes s or in Applied Mathem atics & Deputy Dean of
Res earch at the Ecole Centrale Paris -Univers ité Paris -Saclay, Form er
Senior Scientis t EDF R&D, Expert at the National Res earch Agency,
European Com m is s ion, Uni Bocconi.
Links
● How to reinvent your business with data andAI : MOOC avec
Bpifrance (in French)
● Managing uncertainty & creating value with robust algorithms :
Reference Book
● Manifes to www.es perance-algorithmes .org
edr@operationdata.fr
Plan
Algorithms - what are we talking about?
The need for algorithmic betting
Discernment: conditions for well-balanced
algorithmic services
Real-world tensions, levels of stakes -
creative compromise
Employment and AI
DISAPPEARANCE
OF WORK
Filtering
bubbles
Surveillance
Dis crim ina tion
Algorithms
Models, data and algorithms have now
invaded ALL the economy
Universal digital machines
◆Sales/customer experience
Wine, parking
◆Innovation/product design
manufacturing
◆production
Co-operatives, human services, recruitment
◆Supply Chain
Construction, car fleet
◆Control/ back office
Accounting, law, business services
◆...
Scalability & monopolies
Automation -
optimization
(radical)
Turnover
customization
(massive)
Operating
expenses
Servicification
(dominant)
What should be do ?
To the common good - with all our intelligence
Toda y I ta ke hea ven a nd ea rth a s a witnes s
a ga ins t you: I put before you life or dea th, If nothing should be done for the certain, (...)
bles s ing or curs e. So choos e life, s o tha t nothing should be done at all because nothing is
you a nd your des cenda nts m a y live,... certain (...) But when we work for tomorrow and
By loving the Lord your God, lis tening to his for the uncertain, we act with reason: because we
voice, a tta ching yours elf to Him ; this is must work for the uncertain by the rule of parties
where your life is . - Deuteronomy 30: 19-20 that is demonstrated. - Pascal, Pensées
Indeed, creation eagerly awaits the revelation of the sons of God. (...) she kept the hope of
being freed from the slavery of degradation, to know the freedom of glory given to the
children of God. As we well know, the whole creation groans, it passes through the pains
of a birth that lasts even longer. (...). For we have been saved, but it is in hope - St-Paul,
Romans 8: 19-24
"Choose life..."
CO2 housing -100% by 40% bullshit jobs How can we avoid 2021 = 2020?
2050? +x% unemployed/ covid
Deep excluding poverty 3500 dead/ yr, 80% human "stupidity"
Sustainable
agriculture #1 - BENEFIT: Algorithms are a benefit for the co-creator and responsible man who
discerns the good uses - Manifesto Espérance & AlgorithmesDecision s u p p o r t , a u t o m a t a , AI ...
persona (humana) ex - machina
From the DSS (inc. (Algorithm ic) Decis ion
assisted driving): human Support - DSS
elementary choice (inc. a
Decision in the full sense: reason + free will
few clicks)
(Aristotle), therefore the prerogative of man
..... Autom aton
(actuator)
alone, neither of the beast nor of the machine ...
Who decides, who's responsible?
AI Robot Designer (or his Employer, his s/t ...)
(interactive) Seter
up to the learning agent:
Data purveyor
non-human elementary
certifier
choice, ... but its setting
Consultant
the rest Rational end user
agent
(learner)
... cf RGPD,Europ. Parliament/high riskA necessary bet ... Is it fa ir ?
1 Benefit / Garden / What are the benefits? Who decides the good (for
others?)? What compromises?
Vocation
…. cf. Common Good
2 Efficient / Reliable / this s et of s ocial conditions that enable both groups and
Robust / Risks their m em bers to achieve their perfection in a m ore total
and eas y way. - Soc. Doc. Church 164-170
Systemic feedback
3 Explainable - Shared
Control - Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberationA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Vocation
2 Efficient / Reliable /
Robust / Risks
Systemic feedback
3 Explainable - Shared
Control - Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberation96% a cc. t o o u r M a ch in e Le a r n in g ... – t r u e ?
Dis cern the red of blue over pa s t da ta to gues s the color of a future ca s e
Feature# 2
Feature# 32
Feature# 1
Gizem Yetiş, Oza n Yetkin - A Novel Approa ch for Cla s s ifica tion of Structura l Elem ents in
a 3D Model by Supervis ed Lea rning Septem ber 2018 Conference: Com puting for a better
tom orrow - Proceedings of the 36th eCAADe Conference Volum e: 1
Feature# 25Trustworthy ML models ? Dis cern the red of blue on pa s t da ta to gues s the color of a future ca s e ... Gizem Yetiş, Ozan Yetkin- A Novel Approa ch for Cla s s ifica tion of Structura l Elem ents in a 3D Model by Supervis ed Lea rning Septem ber 2018 Conference: Com puting for a better tom orrow - Proceedings of the 36th eCAADe Conference Volum e: 1 How does this dis cernm ent evolve a ccording to: ina ccura cy of the dots ? m ultiplica tion of da ta ? ... The blue-red interfa ce: how thick, wha t decis ion, wha t ris k?
Paradigm of decision support via
model/algo /AI or human expertise
Xj Observables j-ith past state of the world Yj Decisional event j
data
... ...
Science (ph. laws + init. cond.) Machine learning- IA
Min Distance {Data ; Model (Xj,Yj)}
m odel
Observables: present state of the world diagnosis projected / Decision reco.
Hum a n expert
predictability = regula rity + obs erva bilityInside generalisability error A good model : falsifiable & well understood
failures
regular determinist x -> y : same causes > same effects (in the future, as in Permanence of physical /phenomenological laws, rules ...
ity the past)
Stationnarity F(X; Y|x) remains stable: causes > effects at comparable Permanence of profiles - user behaviors etc.
(static) frequencies - stable frequency of causes (in the future, as
in the past)
Stationnarity The cause-effect relationship F (X)t; Y|x,t) drifts according Climate change rising temperatures
(dynamic) to a stable trend in frequency (hence learnable ...) Pb of human or environmental rapid feedback systems
Interpolability A causes not observed but "close" to observed causes Simulation/Forward-Looking Requirements
correspond to "close" effects Limitations of chaotic systems
observ Fundamental & The state (and its variations): observable non- Scale >> Heisenberg uncertainty
ability destructive Pb of economic forecasts
technological Key observables are measurable with sufficient Difficult for climate, underground etc.
precision/sampling via sensors
economical Key observables are measurable at a reasonable Difficult for the human body, cultural preferences, ...
cost/precisionPredictability, performance , u n ce r t a in t y ...
good news for free will !
fingerprint
Single outcome
Low uncertainty
Accounting automaton
ST weather forecast,
GPS router
Real estate credit Auto translation
score
Cultural
MT weather
recommendation
forecast
Single outc. Multiple outc.
uncertain uncertain
Driving car
Breast cancer
screeningA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Vocation
2 Efficient / Reliable /
Robust / Risks
Systemic feedback
3 Explainable - Shared
Control - Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
Increase Confidence in Decision
transparency?
7 Sobriety - Explicable process?Readablemarginal sensitivity? Complete decision-making rules?
contemplation 6 Human encounter
Explicability - n/p reliability–? Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberationA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Comprehensive consent ... including preferences, bias, risk, content filtering?
Vocation
Autonomy, including on the medical emergency?
2 Sacrifice / common
Efficient good:/ eg. sharing health data, vaccination...
/ Reliable
Freedom - competition, algo-monopolies
Robust / Risks
Systemic feedback
3 Explainable - Shared
Control - Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberationA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Vocation
2 Efficient / Reliable /
Robust / Risks
Systemic feedback
3 Explainable - Shared
Sans biais
4 indésirable,
Autonomy ou-discriminant
consent/ … selon quelles
Control - Responsibilitypopulations
- ?Freedom
Quelle justice ? Qui en décide
Confidence Of Conscience - ?
Competition
Cf. Option préférentielle pour le pauvre
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberationA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Vocation
2 Efficient / Reliable /
Robust / Risks
Systemic feedback Releasing repetitive tasks to serve better (even if you lose some skills?)
Increase skills by expanding experience/possible/cultural diversity
3 ExplainableDon't
- Shared
say it all... or not rightaway
Control - Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberationA necessary bet ... Is it fa ir ?
1 Benefit / Garden /
Vocation
2 Efficient / Reliable /
Robust / Risks
Systemic feedback
Sober in automation i.e. elevating use for both the expert and the end user
Without
3 deceptive anthropomorphism,
Explainable - Shared virtual addiction or quantitative totalitarianism,...
"Man doesn't live only
Control on scores"
- Responsibility - 4 Autonomy - consent/
Confidence Freedom Of Conscience -
Competition
7 Sobriety -
contemplation 6 Human encounter – Future Justice/discrimination -
5
Anthropomorphism of work - maturation - subsidiarity - participation
deliberation... theoretical expectations that are neither a lw a ys fe a s ib le n o r n e ce s s a r ily d e s ir a b le ...
The Re a l w o r ld - Et h ica l Te n s io n s in De cis io n
Su p p o r t
Performance rate - ST accuracy Explainability
Personalization/contractual Non-discrimination /commutative
equity justice
Comfort - efficiency - ST value Personal achievement - MT value
Performance intimacy
Common good (incl third parties) Consent/autonomy
Efficiency (rapidity - massivity) Security (ST and MT)
efficiency... freedom...Decision - m a k in g n a t u r e s a n d DSS s t a k e s
Modera te ris k Undeterm ined ris k High ris k
weather forecas t Recommendation cultural content Driving autonom ous vehicle
Travel recom m endation Documentary research off-s ite clean
Advertis ing dis play Recommendation meeting / CV Military robots
Autom ation of s upport Real estate credit Facial recognition
s ervices Weather forecast / performance / Medical em ergency diagnos is
Chatbot/ com m ercial call sales / geotech ... ...
centre Thermal comfort steering
... Fraud detection
Non-urgent medical diagnosis
....
Open exploration - forecast in law Multiple reco. - point forecast Autonomous agent – single
recommendationWhat ethics s h o u ld u lt im a t e ly discern t h e
co m m o n g o o d
Discerning via “smart” Predictive analys is / proba
interactions Man -Man calculation. / learning on a
supported by algo - expected utility function
prudence
Utilitarian ethics
Decision
Virtue ethics
framework
supported by
algos
Verification/ certification/ s af
ety proces s by determ inis tic
calculations
Deontological ethicsEmployment and AI
Risks
Short-term job cuts, or even jobs, through automation
Medium-term cancellations of Luddites/competitive delays
Dehumanizing work through excessive digital mediation
Destroying skills by over-delegation to algorithms
Increase career/exclusion silos
Promises:
Fluidizing the labour market
Enlarge recruitment basis/bias of experience silos, atypical career brakes, long-term
exclusion of people
Deepen people's vocation and therefore joy at work
Raising work by automating "stunning" tasksEmployment and AI
"Creative destruction" is not necessarily as quick as announced
47% endangered jobs(2013) => +10%jobs (2020 pre-covid)
It is those who seize transformations rather than undergo or repel them that shape
the best opportunitiesCreative
Benefit Human Encounter
co m p r o m is e s t o b e Shared Mastery Work
fo u n d Garden Freedom of
Consciousness
(…) Vocation - t h e Subsidiarity Contemplation
in im it a b le cr e a t ivit y o f
t h e h u m a n p e r s o n is Vocation Participation
n e ce s s a r y t o p u t Human Person Competition
a lg o r it h m s a t t h e s e r vice Risks Universal Destination
o f t h e co m m o n g o o d (…)Annexes
Déterministe, probabiliste, prédictible,
in ce r t a in , r is q u é ...
Déterminé / déterministe / causal Indéterminé / probabiliste / ouvert
Système sous-jacent observable - prédictible Système indéterminé - observabilité partielle -
Dynamique incertaine
Algo en phase prédictive Aléa données/processus d’apprentissage
Prédiction randomisée
Interactions / rétroactions MT
Certification déterministe: ... un leurre ? Certification probabiliste: jamais de garantie
(complexité des systèmes, biais sélection ...) certaine/ni acceptable a posteriori
Biais cognitifs humains Liberté de l’agir humain / ouverture
Expérience bornée du meilleur expert Perception humaine étendueManifeste
Bienfait Rencontre Humaine
Co m m e n t e n t r e p r e n d r e à
Maîtrise Partagée Travail
l’è r e d e s a lg o r it h m e s
p o u r s e r vir le b ie n Jardin Liberté de Conscience
co m m u n s a n s a s s e r vir Subsidiarité Contemplation
Vocation Participation
Personne Concurrence
Risques Destination
UniverselleOAD - b ie n d é t e r m in é , u n ivo q u e , p e u in ce r t a in
En théorie: x(t°) -> D=y(t°) est univoque.
X(t°) Données observées / percepts
Signes, données, résultats de mesures au temps observé Pour bien décider, il suffit d’avoir
Diagnostic / action réglée > les données x(t°)
> Et l’apprentissage de la règle x->D
D = Y(X(t°)) Estimation de l’état réel sous -jacent & Un algo peut mieux discerner que le meilleur expert
décision univoque selon état
humain
Comptabiliser un mouvement - déclencher l’antispam -
reconnaître une empreinte digitale ... Discernement pratique
Précision & robustesse (généralisabilité, représentativité,
stabilité num., observabilité, stationnarité, …)
Contrôlabilité des erreurs et équité de traitement
Développement des compétences / employabilité
Taux de Perform ance - précis ion Explicabilité
CT
Confort - efficacité - valeur CT Réalis ation pers onnelle - valeur MTOAD - b ie n d é t e r m in é , u n ivo q u e m a is in ce r t a in
X(t°) Données observées / percepts
En réalité: X(t°) observe imparfaitement l’état réel x(t°)
Signes, données, résultats de mesures au temps observé
et/ou la règle x-> d= y(t°) est insuffisamment apprenable
Diagnostic faute d’assez de données fiables et/ou la règle x-> d est
Y(t°) Estimation de l’état réel sous -jacent (au ambigüe, … et la décision porte sur un état futur y(tf)
regard de la décision à prendre) dépendant d’une dynamique au pronostic non univoque
Pronostic / évaluation
La personne a-t-elle telle maladie ? S’agit-il d’un mouvement selon y(t°)
frauduleux ? La personne est-elle coupable de tel acte ? ...
d’impact
Y(tf , d); L(Y(.)) Projection de l’état futur vraisemblable Discernement pratique
et des conséquence (selon la décision) Périmètre d’observabilité machine vs. par l’agent humain
Décision contextuelle
Selon le traitement/la décision, que deviendra -t-elle (et son
environnement ?) ?
Contrôlabilité des erreurs et équité de traitement
Temps de la décision et espace relation humaine/ de
d(t°) Décision prise
maturation
Personnalisation / équité Non-discrimination / justice
contractuelle commutative
Précision Intimité
Efficacité (rapidité & massivité) Consentement / autonomieOAD - b ie n m u lt ivo q u e
En réalité: x(t°) -> D=y(t°) est multivoque, soit par
X(t°) Données observées / percepts insuffisante observabilité du réel soit par dilemmes de
Signes, données, résultats de mesures au temps observé préférence. Pour bien décider, il faut
Diagnostic / action réglée > une appréciation fidèle des possibles Yi(t°)
> un modèle accepté de préférence aux conséquences Di
D1 = Y1(X(t°)) Estimation de l’état réel sous -jacent & Un outil peut permettre à une personne de mieux décider
D2 = Y2(X(t°))
... décision multivoques
Recommandation culturelle / rencontres, moteur de traduction
automatique, prévision météo à 3 jours, ... Discernement pratique
Consentement au modèle de préférences
Ajustement des préférences selon l’horizon /
asservissement aux silos / rétroaction sur l’apprentissage
Elargissement vs. appauvrissement de l’expérience
décisionnelle
Confort - efficacité - valeur CT Réalis ation pers onnelle - valeur MT
Précis ion Intim itéTypes d’algorithmes et enjeux d’OAD
Enjeu fort ??? Enjeu faible
Contrôle/commande de Exploration de données Exploration de données
systèmes automatisés “fermée” (exfiltrage, réduction “ouverte”
irréversible …)
Recommandation (ou score, Prévision explicite en
traduction/résumé, …) mono- Recommandation (ou score, incertitude
résultat / classif. binaire traduction/résumé, …) multi-
résultat ...
Sélection automatisée
... Prévision de tendance simple
….Prédictibilité, incertitudes, performance ...
Univoque/ peu Prévision météo CT, routeur GPS Prédictibilité forte à CT, peu d’attente explicabilité perte de
incertain compétences
Annotation comptable Multivocité en réalité indésirable
Reconnaissance empreinte ...
Univoque / incertain Crédit immobilier Observ. Moyenne, dynamique incertaine
Prévision météo MT Faible observ, dynamique incertaine
Diag géotechnique Faible observabilité, dynamique certaine
Diag dépistage cancer sein Faible observ, dynamique incertaine
Multivoque / Recommandation culturelle, rencontres, recrutement ... Faible observabilité, Rétroaction forte,
incertain
Drug discovery Observabilité forte, dynamique incertaine
Traduction automatique, résumé, analyse autom. CV, ... Observabilité moyenne, préférences incertaines
Arrêt automatique véhicule autonome / dilemme tramway Observabilité moyenne (mais > l’humaine ?), préférences
incertaines, dynamique très incertaine, rétrooaction
comportementale forteLibération ou asservissement ? Espionnage ou Suppression ou transformation Alliance ? des emplois ?
Machines numériques universelles
Vin, parking/ expérience client
◆Ventes
◆Innovation
Fabrication / conception produits
◆Production
Coopératives, services à la personne, recrutement
BTP, flottesChain
◆Supply véhicules
Compta, droit,/services
◆Contrôle back auxoffice
DATA
entreprises
Modèle = industrialise la décision ◆...
Algorithme = démultiplie l’exécution
Int. Artificielle = rend apprenante l’interactionYou can also read
