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Att straffa det oförutsebara – En rättsdogmatisk analys av personlig skuld vid autonoma systemfel

Isaksson Krantz, Simone LU (2025) LAGF03 20252
Department of Law
Faculty of Law
Abstract
The following thesis analyzes criminal liability in accidents caused by self-driving vehicles. These autonomous vehicles are governed by advanced artifi-cial algorithms that make autonomous decisions, which challenges the funda-mental requirements and principles of criminal law. Among other things, the maxim "nulla poena sine culpa" (no punishment without guilt) suffers when the decision-making process at the moment of the act lacks transparency and insight. In light of this, the thesis specifically analyzes the extent to which engineers or system developers, through negligence, can be held liable for causing the death of another pursuant to Chapter 3, Section 7 of the Swedish Penal Code (Brottsbalken). Through this, the thesis illustrates... (More)
The following thesis analyzes criminal liability in accidents caused by self-driving vehicles. These autonomous vehicles are governed by advanced artifi-cial algorithms that make autonomous decisions, which challenges the funda-mental requirements and principles of criminal law. Among other things, the maxim "nulla poena sine culpa" (no punishment without guilt) suffers when the decision-making process at the moment of the act lacks transparency and insight. In light of this, the thesis specifically analyzes the extent to which engineers or system developers, through negligence, can be held liable for causing the death of another pursuant to Chapter 3, Section 7 of the Swedish Penal Code (Brottsbalken). Through this, the thesis illustrates how the lack of transparency in AI systems, hereinafter referred to as the "black box pro-blem," complicates the assessment of criminal liability.
Using a critical legal dogmatic method, it is analyzed whether current criminal law is sufficiently designed to handle technical innovation where decision-making processes remain hidden within complex systems. The elements of a crime in criminal law are constructed on the basis that the perpetrator is a natu-ral person with an individual consciousness to make decisions. The artificial intelligence in self-driving vehicles creates a theoretical artificial perpetrator who lacks human consciousness and whose decision chains remain conce-aled.
A central conclusion is that the black box problem prevents the determination of personal guilt. According to the principle of conformity, criminal liability requires that the individual had the ability and opportunity to comply with the law. Since the engineers behind the systems lack insight into the system's de-cision chains, the prerequisites for establishing negligence through personal insight and blameworthiness are lacking. Furthermore, the standard of proof "beyond a reasonable doubt" is analyzed to illustrate the obstacles faced by the prosecutor due to the lack of insight into the systems. The thesis highlights reasoning from the Supreme Court regarding the requirement that alternative courses of events must be excluded for criminal liability, which the inherent lack of transparency in AI systems prevents. The result of this is a liability vacuum in Swedish criminal law where the legislation cannot punish any hu-man actor for the systems' shortcomings.
Finally, social adequacy (social adekvans) is analyzed as a potential ground for exclusion of liability, in order to illustrate how criminal law should relate to AI innovation. Despite the social benefits of AI, the thesis dismisses the applicability of the doctrine of social adequacy. When opaque decision-making processes in self-driving cars replace and erase human control, the risk-taking in traffic cannot be seen within the framework of what is socially accepted or justifiable.
Lastly, potential solutions for the legislator are presented to address the pot-ential liability vacuum in the legislation. The thesis particularly highlights the introduction of requirements for Explainable AI (XAI). XAI creates the con-ditions to transform hidden algorithms into clear decision chains, which pro-vides better opportunities to demand criminal liability and prevents artificial technology from eroding the purpose of criminal law. (Less)
Abstract (Swedish)
Följande uppsats analyserar det straffrättsliga ansvaret vid olyckor orsakade av självkörande fordon. Dessa självkörande fordon styrs av avancerade artifi-ciella algoritmer som fattar autonoma beslut, vilket utmanar straffrättens grundläggande krav och principer. Bland annat blir maximet ”nulla poena sine culpa” (inget straff utan skuld) lidande när beslutsprocessen i gärningsögon-blicket saknar transparens och insyn. I ljuset av detta analyserar uppsatsen särskilt i vilken utsträckning som ingenjörer eller systemutvecklare, genom oaktsamhet, kan hållas ansvariga för vållande till annans död enligt 3 kap.7§ BrB. Uppsatsen åskådliggör genom detta hur bristen på transparens i AI-systemen, nedan benämnd black box-problematiken, försvårar den... (More)
Följande uppsats analyserar det straffrättsliga ansvaret vid olyckor orsakade av självkörande fordon. Dessa självkörande fordon styrs av avancerade artifi-ciella algoritmer som fattar autonoma beslut, vilket utmanar straffrättens grundläggande krav och principer. Bland annat blir maximet ”nulla poena sine culpa” (inget straff utan skuld) lidande när beslutsprocessen i gärningsögon-blicket saknar transparens och insyn. I ljuset av detta analyserar uppsatsen särskilt i vilken utsträckning som ingenjörer eller systemutvecklare, genom oaktsamhet, kan hållas ansvariga för vållande till annans död enligt 3 kap.7§ BrB. Uppsatsen åskådliggör genom detta hur bristen på transparens i AI-systemen, nedan benämnd black box-problematiken, försvårar den straffrätts-liga ansvarsbedömningen.
Genom en kritisk rättsdogmatisk metod analyseras huruvida gällande straffrätt är tillräckligt utformad för att hantera teknisk innovation där beslutsprocesser-na förblir dolda av de komplexa systemen. Rekvisiten inom straffrätten är konstruerade mot bakgrund av att gärningsmannen är en fysisk människa med ett eget medvetande att fatta beslut. Genom den artificiella intelligensen hos självkörande fordon skapas en teoretisk artificiell gärningsman som saknar mänskligt medvetande och vars beslutskedjor förblir dolda.
En central slutsats är att black box-problematiken hindrar fastställandet av personlig skuld. Enligt konformitetsprincipen förutsätter straffansvar att indi-viden haft förmåga och tillfälle att rätta sig efter lagen. Då ingenjörerna bakom systemen saknar insyn i systemets beslutskedjor, brister förutsättningarna för att konstatera oaktsamhet genom personlig insikt och klander. Vidare analyse-ras beviskravet ”ställt utom varje rimligt tvivel” i syfte att åskådliggöra det hinder som åklagaren ställs inför till följd av bristen på insyn i systemen. Uppsatsen belyser resonemang från Högsta domstolen gällande att alternativa händelseförlopp måste kunna uteslutas för straffrättsligt ansvar, vilket AI-systemens inneboende brist på transparens hindrar. Resultatet av detta blir ett ansvarsvakuum i svensk straffrätt där lagstiftningen inte kan straffa någon mänsklig aktör för systemens brister.

Avslutningsvis analyseras social adekvans som en tänkbar ansvarsfrihets-grund, i syfte att åskådliggöra hur straffrätten bör förhålla sig till AI-innovation. Trots samhällsnyttan av AI, avfärdar uppsatsen social adekvanslä-rans tillämplighet. När ogenomskinliga beslutsprocesser i självkörande bilar ersätter och raderar mänsklig kontroll kan risktagandet i trafiken inte ses inom ramen för socialt accepterat eller försvarligt.
Slutligen presenteras tänkbara lösningar för lagstiftaren i syfte att hantera det potentiella ansvarsvakuumet i lagstiftningen. Uppsatsen belyser särskilt infö-randet av krav på transparent AI (Explainable AI, XAI). XAI skapar förut-sättningar att omvandla dolda algoritmer till tydliga beslutskedjor, vilket skap-ar bättre förutsättningar att utkräva straffrättsligt ansvar och motverkar att arti-ficiell teknik urholkar straffrättens syfte. (Less)
Please use this url to cite or link to this publication:
author
Isaksson Krantz, Simone LU
supervisor
organization
course
LAGF03 20252
year
type
M2 - Bachelor Degree
subject
keywords
straffrätt (en. criminal law)
language
Swedish
id
9217149
date added to LUP
2026-02-09 15:11:34
date last changed
2026-02-09 15:11:34
@misc{9217149,
  abstract     = {{The following thesis analyzes criminal liability in accidents caused by self-driving vehicles. These autonomous vehicles are governed by advanced artifi-cial algorithms that make autonomous decisions, which challenges the funda-mental requirements and principles of criminal law. Among other things, the maxim "nulla poena sine culpa" (no punishment without guilt) suffers when the decision-making process at the moment of the act lacks transparency and insight. In light of this, the thesis specifically analyzes the extent to which engineers or system developers, through negligence, can be held liable for causing the death of another pursuant to Chapter 3, Section 7 of the Swedish Penal Code (Brottsbalken). Through this, the thesis illustrates how the lack of transparency in AI systems, hereinafter referred to as the "black box pro-blem," complicates the assessment of criminal liability.
Using a critical legal dogmatic method, it is analyzed whether current criminal law is sufficiently designed to handle technical innovation where decision-making processes remain hidden within complex systems. The elements of a crime in criminal law are constructed on the basis that the perpetrator is a natu-ral person with an individual consciousness to make decisions. The artificial intelligence in self-driving vehicles creates a theoretical artificial perpetrator who lacks human consciousness and whose decision chains remain conce-aled.
A central conclusion is that the black box problem prevents the determination of personal guilt. According to the principle of conformity, criminal liability requires that the individual had the ability and opportunity to comply with the law. Since the engineers behind the systems lack insight into the system's de-cision chains, the prerequisites for establishing negligence through personal insight and blameworthiness are lacking. Furthermore, the standard of proof "beyond a reasonable doubt" is analyzed to illustrate the obstacles faced by the prosecutor due to the lack of insight into the systems. The thesis highlights reasoning from the Supreme Court regarding the requirement that alternative courses of events must be excluded for criminal liability, which the inherent lack of transparency in AI systems prevents. The result of this is a liability vacuum in Swedish criminal law where the legislation cannot punish any hu-man actor for the systems' shortcomings.
Finally, social adequacy (social adekvans) is analyzed as a potential ground for exclusion of liability, in order to illustrate how criminal law should relate to AI innovation. Despite the social benefits of AI, the thesis dismisses the applicability of the doctrine of social adequacy. When opaque decision-making processes in self-driving cars replace and erase human control, the risk-taking in traffic cannot be seen within the framework of what is socially accepted or justifiable.
Lastly, potential solutions for the legislator are presented to address the pot-ential liability vacuum in the legislation. The thesis particularly highlights the introduction of requirements for Explainable AI (XAI). XAI creates the con-ditions to transform hidden algorithms into clear decision chains, which pro-vides better opportunities to demand criminal liability and prevents artificial technology from eroding the purpose of criminal law.}},
  author       = {{Isaksson Krantz, Simone}},
  language     = {{swe}},
  note         = {{Student Paper}},
  title        = {{Att straffa det oförutsebara – En rättsdogmatisk analys av personlig skuld vid autonoma systemfel}},
  year         = {{2025}},
}