Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Sabrina
October 21, 2023

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Sabrina

October 21, 2023
Tweet

More Decks by Sabrina

Other Decks in Research

Transcript

  1. Trust, Accountability, and Autonomy
    in
    Knowledge Graph-based AI
    for Self-determination
    Sabrina Kirrane
    Habilitation Lecture 20.10.2023

    View full-size slide

  2. Artificial Intelligence (AI)
    It can complete writing tasks and some coding challenges, but in both
    cases human expertise is still required since its output is not always
    precise. Its expertise is general, and it lacks deep knowledge in your
    domain.”
    https://www.zdnet.com/article/chatgpt-vs-bing-chat-vs-
    google-bard-which-is-the-best-ai-chatbot/
    https://technative.io/exploring-a-knowledge-graph-based-solution-to-
    chatgpts-inherent-limitations/
    “Artificial intelligence (AI) has transformed how we work
    and play in recent months, giving almost anyone the ability
    to write code, create art, and even make investments.” 2

    View full-size slide

  3. Knowledge Graphs (KGs)
    3

    View full-size slide

  4. Self-Determination
    4
    Ibáñez, L., Domingue, J., Kirrane, S., Seneviratne, O., Third, A., Vidal, M., 2023. Trust, Accountability, and Autonomy in Knowledge
    Graph-based AI for Self-determination. Transactions on Graph Data and Knowledge (TGDK) (revised and resubmitted)
    How can we ensure that
    individuals are aware of who
    knows what about them and can
    influence data processing that
    concerns them (otherwise known
    as self-determination)?
    4

    View full-size slide

  5. Motivating Scenario
    European Health Data Spaces
    https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52022PC0197
    Ensure that "natural persons in the EU have increased control in
    practise over their electronic health data"
    Facilitate access to health data by various stakeholders in order to
    "promote better diagnosis, treatment and well-being of natural
    persons, and lead to better and well-informed policies". https://learnertrip.com/geography/european-union-countries/
    Ibáñez, L., Domingue, J., Kirrane, S., Seneviratne, O., Third, A., Vidal, M., 2023. Trust, Accountability, and Autonomy in Knowledge
    Graph-based AI for Self-determination. Transactions on Graph Data and Knowledge (TGDK) (revised and resubmitted)
    5

    View full-size slide

  6. Motivating Scenario
    European Health Data Spaces
    6
    Ibáñez, L., Domingue, J., Kirrane, S., Seneviratne, O., Third, A., Vidal, M., 2023. Trust, Accountability, and Autonomy in Knowledge
    Graph-based AI for Self-determination. Transactions on Graph Data and Knowledge (TGDK) (revised and resubmitted)
    6

    View full-size slide

  7. KG-based AI for Self-Determination
    The Vision
    KG-based AI for Self-determination Conceptualisation
    • The three pillar research topics - trust, accountability, and
    autonomy - represent the desired goals for how AI can benefit
    society and facilitate self-determination
    • The pillars combine fundamental principles of the proposed EU AI
    Act and self-determination theory.
    • The pillars are supported via four foundational research topics that
    represent the tools and techniques needed to support the three
    research pillars:
    • machine-readable norms and policies
    • decentralised infrastructure
    • decentralised KG management
    • explainable and neuro-symbolic AI
    7
    Ibáñez, L., Domingue, J., Kirrane, S., Seneviratne, O., Third, A., Vidal, M., 2023. Trust, Accountability, and Autonomy in Knowledge
    Graph-based AI for Self-determination. Transactions on Graph Data and Knowledge (TGDK) (revised and resubmitted)
    7

    View full-size slide

  8. KG-based AI for Self-Determination
    The Pillars
    8
    Ibáñez, L., Domingue, J., Kirrane, S., Seneviratne, O., Third, A., Vidal, M., 2023. Trust, Accountability, and Autonomy in Knowledge
    Graph-based AI for Self-determination. Transactions on Graph Data and Knowledge (TGDK) (revised and resubmitted)
    § Machine-readable policies must
    faithfully represent human
    policies and norms
    § Enforcement and compliance
    checking:
    v (semi-)automated
    techniques
    v auditing and tracing
    v trusted execution
    environments
    v certification mechanisms
    § Detecting if any party violated
    policies and norms
    § Facilitating learning
    transparency
    § Providing explanations for
    recommendations and decisions
    § Integrating, querying, and
    aggregating knowledge from
    disparate sources
    § Controlling who has access to
    our personal data
    § Negotiating terms of use
    § Fostering collaboration via
    aggregation and strong privacy
    guarantees (e.g.,
    anonymisation)
    § Continuous monitoring via
    auditing, tracing, and
    certification
    § Self-sovereign identities
    Trust Accountability Autonomy
    8

    View full-size slide

  9. KG-based AI for Self-Determination
    The Foundations
    How does my work contribute to KG-based AI for Self-Determination?
    9

    View full-size slide

  10. Machine-readable norms and policies
    KG-based AI for Self-determination Conceptualisation
    Bonatti, P.A., Kirrane, S., Petrova, I.M. and Sauro, L., 2020. Machine
    understandable policies and GDPR compliance checking. KI-Künstliche
    Intelligenz.
    Fernández, J.D., Sabou, M., Kirrane, S., Kiesling, E., Ekaputra, F.J.,
    Azzam, A. and Wenning, R., 2020. User consent modeling for ensuring
    transparency and compliance in smart cities. Personal and Ubiquitous
    Computing.
    Kirrane, S., Fernández, J.D., Dullaert, W., Milosevic, U., Polleres, A.,
    Bonatti, P.A., Wenning, R., Drozd, O. and Raschke, P., 2018. A scalable
    consent, transparency and compliance architecture. In The Semantic Web:
    ESWC 2018 Satellite Events.
    Agarwal, S., Steyskal, S., Antunovic, F., and Kirrane, S., 2018. Legislative
    Compliance Assessment: Framework, Model and GDPR Instantiation.
    Proceedings of the 7th Annual Privacy Forum.
    10

    View full-size slide

  11. 11
    Machine-readable norms and policies
    Consent as a Legal Basis for Data Processing
    Personal KG
    Bob Usage Policies
    Data Subject
    Data Controller
    Bonatti, P.A., Kirrane, S., Petrova, I.M. and Sauro, L., 2020. Machine understandable policies and GDPR compliance checking. KI-Künstliche Intelligenz.
    Fernández, J.D., Sabou, M., Kirrane, S., Kiesling, E., Ekaputra, F.J., Azzam, A. and Wenning, R., 2020. User consent modeling for ensuring transparency
    and compliance in smart cities. Personal and Ubiquitous Computing.
    KG-based
    AI models
    Medical KG
    Medicomp
    Bob makes his data available only for medical purposes
    The Data Controller processes data in accordance with the data
    subjects consent
    Usage Policies
    Consent UI
    Consent UI
    11

    View full-size slide

  12. Bonatti, P.A., Kirrane, S., Petrova, I.M. and Sauro, L., 2020. Machine understandable policies and GDPR compliance checking. KI-Künstliche Intelligenz.
    Fernández, J.D., Sabou, M., Kirrane, S., Kiesling, E., Ekaputra, F.J., Azzam, A. and Wenning, R., 2020. User consent modeling for ensuring transparency
    and compliance in smart cities. Personal and Ubiquitous Computing.
    Machine-readable norms and policies
    Usage Policy Language
    § We propose a usage policy language that can be
    used to express:
    v data subject consent
    v data controllers usage requests
    v fragments of the GDPR
    v processing requirements as business policies
    § We extensively re-uses standards based privacy-
    related vocabularies
    § Policies are expressed using the Web Ontology
    Language (OWL), thus we are able to leverage
    existing OWL reasoners out of the box
    SPECIAL’s Usage Policy Language Grammar
    12

    View full-size slide

  13. 13
    Machine-readable norms and policies
    Log Vocabulary
    Bonatti, P.A., Kirrane, S., Petrova, I.M. and Sauro, L., 2020. Machine understandable policies and GDPR compliance checking. KI-Künstliche Intelligenz.
    Fernández, J.D., Sabou, M., Kirrane, S., Kiesling, E., Ekaputra, F.J., Azzam, A. and Wenning, R., 2020. User consent modeling for ensuring transparency
    and compliance in smart cities. Personal and Ubiquitous Computing.
    § We propose a log vocabulary that reuses
    well-known vocabularies such as PROV for
    representing provenance metadata
    § Log entries are used to represent:
    v Data processing events
    v Policy events
    § Optional components are provided for:
    v Immutability
    v Business process management (BPM)
    13

    View full-size slide

  14. 14
    Machine-readable norms and policies
    Compliance Checking Architecture
    Kirrane, S., Fernández, J.D., Dullaert, W., Milosevic, U., Polleres, A., Bonatti, P.A., Wenning, R., Drozd, O. and Raschke, P., 2018. A scalable consent,
    transparency and compliance architecture. In The Semantic Web: ESWC 2018 Satellite Events.
    • Data processing and sharing event logs are
    stored in the Kafka distributed streaming
    platform
    • We assume that consent updates are infrequent
    and as such usage policies and the respective
    vocabularies are represented in a virtuoso triple
    store
    • The compliance checker, which includes an
    embedded HermiT reasoner uses the consent
    saved in Virtuoso together with the application
    logs provided by Kafka to check that data
    processing and sharing complies with the
    relevant usage control policies
    • As logs can be serialized using JSON-LD, it is
    possible to benefit from the faceting browsing
    capabilities of Elasticsearch and the out of the
    box visualization capabilities provided by Kibana
    14

    View full-size slide

  15. 15
    Machine-readable norms and policies
    Assessing Effectiveness
    Bonatti, P.A., Kirrane, S., Petrova, I.M. and Sauro, L., 2020. Machine understandable policies and GDPR compliance checking. KI-Künstliche Intelligenz.
    Fernández, J.D., Sabou, M., Kirrane, S., Kiesling, E., Ekaputra, F.J., Azzam, A. and Wenning, R., 2020. User consent modeling for ensuring transparency
    and compliance in smart cities. Personal and Ubiquitous Computing.
    Telco and Financial Services Pilots
    Cyber-Physical Social Systems Project
    15

    View full-size slide

  16. Decentralised infrastructure
    KG-based AI for Self-determination Conceptualisation
    Basile, D., Di Ciccio, C., Goretti, V. and Kirrane, S., 2023. Blockchain
    based Resource Governance for Decentralized Web Environments.
    Frontiers in Blockchain.
    Basile, D., Di Ciccio, C., Goretti, V. and Kirrane, S., 2023. A Blockchain-
    driven Architecture for Usage Control in Solid. Proceedings of the 1st
    Workshop on Fintech and Decentralized Finance (FiDeFix) @ the 43rd
    IEEE International Conference on Distributed Computing Systems.
    16

    View full-size slide

  17. 17
    Decentralised infrastructure
    Decentralised Data Market
    Data Consumer
    Bob sets up a personal online datastore (POD)
    Alice asks the market for medical data
    Contacts the personal online datastore (POD)
    Uses the retrieved resources in her trusted execution environment (TEE)
    Makes his resources available only for medical purposes
    Gets a remuneration according to the number of accesses
    Personal KG
    Bob
    Usage Policies
    Personal KG
    Alice
    Usage Policies
    Data Producer
    POD
    POD
    TEE
    Trusted
    App
    TEE
    Trusted
    App
    Basile, D., Di Ciccio, C., Goretti, V. and Kirrane, S., 2023. Blockchain based Resource Governance for Decentralized Web Environments. Frontiers in
    Blockchain.
    17

    View full-size slide

  18. 18
    Decentralised infrastructure
    Architectural overview of our ReGov framework
    Basile, D., Di Ciccio, C., Goretti, V. and Kirrane, S., 2023. Blockchain based Resource Governance for Decentralized Web Environments. Frontiers in
    Blockchain.
    18

    View full-size slide

  19. Basile, D., Di Ciccio, C., Goretti, V. and Kirrane, S., 2023. Blockchain based Resource Governance for Decentralized Web Environments. Frontiers in
    Blockchain.
    19
    Decentralised infrastructure
    Assessing Effectiveness
    Remix IDE Gas
    Profiler plugin
    Deployment of the DTindexing smart contract on the
    Ethereum public network = 187.09 EUR.
    Deployment of the DTindexing smart contract on the
    Avalanche platform = 2.17 EUR.
    Deployment of the DTindexing smart contract on the
    Polygon platform = 0.65 EUR.
    Privacy
    Affordability
    Security
    § The state of distributed applications can only be changed by
    transactions marked by a digital signature
    § The Intel SGX Trusted Execution Environment is hardened
    against injection of malicious code
    § Oracles establish secure communication protocols that
    enable on-chain and off-chain computations
    § With usage control users can benefit from a greater level of
    privacy, as they have a way to determine how their resources are
    being used.
    § Despite the possibility of specifying private variables in smart
    contracts, the method invocations thanks to which those
    variables are set are recorded in publicly readable transactions.
    § The SGX-PFS allows for files to be stored in a secure, encrypted
    format, even when the operating system is not running.
    19

    View full-size slide

  20. Decentralised KG management
    KG-based AI for Self-determination Conceptualisation
    Kirrane, S., 2021. Intelligent software web agents: A gap analysis. Web
    Semantics.
    Kampik, T., Mansour, A., Boissier, O., Kirrane, S., Padget, J., Payne,
    T.R., Singh, M.P., Tamma, V. and Zimmermann, A., 2022. Governance of
    Autonomous Agents on the Web: Challenges and Opportunities. ACM
    Transactions on Internet Technology.
    Fernández, J.D., Kirrane, S., Polleres, A. and Steyskal, S., 2020.
    HDTcrypt: Compression and encryption of RDF datasets. Semantic Web.
    Fernández, J.D., Kirrane, S., Polleres, A. and Steyskal, S., 2017. Self-
    Enforcing Access Control for Encrypted RDF. Proceedings of the 14th
    Extended Semantic Web Conference.
    20

    View full-size slide

  21. Decentralised KG management
    Vaccination Appointment Scheduling
    Physician
    Personal KG
    Bob Preferences
    Personal KG
    Alice
    Preferences
    Patient
    AI Assistant
    AI Assistant
    Bob’s assistant agent is in charge of managing personal data on his
    behalf.
    Alice’s physician agent is in charge of managing administrative
    tasks.
    Bob’s assistant agent uses his appointment calendar and his
    medical history to schedule a vaccination appointment.
    Alice’s physician agent provides recommendations on vaccinations
    and makes appointments based on preference classes.
    21

    View full-size slide

  22. Kampik, T., Mansour, A., Boissier, O., Kirrane, S., Padget, J., Payne, T.R., Singh, M.P., Tamma, V. and Zimmermann, A., 2022. Governance of Autonomous
    Agents on the Web: Challenges and Opportunities. ACM Transactions on Internet Technology.
    22
    Decentralised KG management
    Governance Conceptual Framework
    Policies
    • Non-autonomous entities in the environment
    • Policies state who can access things/services
    and constraints on their usage
    Preferences
    • Entities that autonomously perceive and act
    upon their environment (i.e., things and
    services) and interact with the other entities
    • Agents have preferences that inform and
    constrain their actions with respect to things,
    web services and other agents.
    Norms
    • Organisations are first-class abstractions that
    group agents and their governance (i.e., norms)
    • Logical grouping of agents with a particular
    purpose, and the provision of legal, regulatory
    and social norms that may possibly span
    multiple organisations
    22

    View full-size slide

  23. Kampik, T., Mansour, A., Boissier, O., Kirrane, S., Padget, J., Payne, T.R., Singh, M.P., Tamma, V. and Zimmermann, A., 2022. Governance of Autonomous
    Agents on the Web: Challenges and Opportunities. ACM Transactions on Internet Technology.
    23
    Decentralised KG management
    Assessing Effectiveness
    Health Service
    for State A
    National Health
    Policy
    Distributed
    Patient Data
    Health Service
    for State B
    National Health
    Policy
    Distributed
    Patient Data
    John,

    Patient
    Vaccine

    Passport
    Clinics Clinics
    Vaccination
    Centre
    Jane,
    Physician
    m
    anages
    m
    anages
    releases
    Vaccine Centre
    Organisation
    Vaccination
    Records
    Scheduling
    System
    Batch of
    Vaccine Doses
    Vaccine
    Storage Vaccine
    Guard
    Vaccination Motivating Scenario
    Instantiation of the Conceptual Framework
    23

    View full-size slide

  24. Towards Explainable and Neuro-Symbolic AI
    KG-based AI for Self-determination Conceptualisation
    Dieber, J. and Kirrane, S., 2022. A novel model usability evaluation
    framework (MUsE) for explainable artificial intelligence. Information
    Fusion.
    Filtz, E., Kirrane, S. and Polleres, A., 2021. The linked legal data
    landscape: linking legal data across different countries. Artificial
    Intelligence and Law.
    Navas-Loro, M., Filtz, E., Rodríguez-Doncel, V., Polleres, A. and Kirrane,
    S., 2019. TempCourt: evaluation of temporal taggers on a new corpus of
    court decisions. The Knowledge Engineering Review.
    Filtz, E., Navas-Loro, M., Santos, C., Polleres, A. and Kirrane, S., 2020.
    Events Matter: Extraction of Events from Court Decisions. Proceedings of
    the 33rd International Conference on Legal Knowledge and Information
    Systems.
    24

    View full-size slide

  25. Towards Explainable and Neuro-Symbolic AI
    EU Linked Legal Data Community
    Member State 27
    European Union
    Legal KG
    Legal Metadata
    Legal Documents
    AI
    EU Linked Legal Data Community
    Member State 1
    Legal KG
    Legal Metadata Legal Documents
    AI
    .
    .
    .
    UI
    Legal KG
    Legal Metadata Legal Documents
    AI UI
    UI
    Linked Legal Data KGs
    25

    View full-size slide

  26. 26
    Towards Explainable and Neuro-Symbolic AI
    Extraction of Entities from German Legal Text
    Filtz, E., Kirrane, S. and Polleres, A., 2021. The linked legal data landscape: linking legal data across different countries. Artificial Intelligence and Law.
    Austrian Supreme Court Decision
    Green. = court
    Pink = legal rule (e.g., RS0053483)
    Blue. = reference to a specific
    article/paragraph (e.g., §1323 ABGB =
    civil code)
    Red. = other cases that have been
    decided by a court, in this snippet all of
    them are from the Supreme Court (e.g.,
    6Ob161/10k)
    turquoise = literature (eg ZLB 2013/38
    should be the journal "Österreichische
    Zeitschrift für Liegenschaftsbewertung")

    View full-size slide

  27. 27
    Towards Explainable and Neuro-Symbolic AI
    Assessing Effectiveness
    Approach Case
    Reference
    Contributor Court Legal
    Provision
    Law
    Gazette
    Legal
    Rule
    Literature
    Rules Rules (lenient) 0.9824 0.8426 0.9801 0.9090 0.9460 1 0.8674
    ML /
    DL
    CRF 0.9787 0.9328 0.9616 0.9459 0.9473 0.9997 0.8866
    BERT 0.9712 0.9583 0.9616 0.9489 0.9396 0.9986 0.8448
    DistilBERT 0.9772 0.9551 0.9586 0.9521 0.9437 0.9989 0.8626
    ∆ 0.0112 0.1157 0.0215 0.0431 0.0077 0.0014 0.0418
    „[...] vgl. Mayrhofer/Tangl in
    Fenyves/Kerschner/Vonkilch, Klang3 §
    6 Abs 1 Z 2 KSchG Rz 1 [...]”
    „[...] zugunsten des obsiegenden Klägers (RIS-Justiz
    RS0079624 [T14]). Ein berechtigtes Interesse des
    obsiegenden Beklagten an der Urteilsveröffentlichung ist dann
    gegeben, wenn der Rechtsstreit eine gewisse Publizität erlangt
    hat (RIS-Justiz RS0079511), etwa wenn [...]“
    F-Scores
    Filtz, E., Kirrane, S. and Polleres, A., 2021. The linked legal data landscape: linking legal data across different countries. Artificial Intelligence and Law.
    50 Austrian Supreme Court decisions

    View full-size slide

  28. § European Court of Human Rights (ECHR)
    § Quick overview over case
    v Dates (“when“)
    v Subjects (“who“)
    v Core (“what“)
    28
    Filtz, E., Navas-Loro, M., Santos, C., Polleres, A. and Kirrane, S., 2020. Events Matter: Extraction of Events from Court Decisions. Proceedings of the 33rd
    International Conference on Legal Knowledge and Information Systems.
    Towards Explainable and Neuro-Symbolic AI
    Extraction of Events from English Legal Text
    28

    View full-size slide

  29. 29
    Approach What When Who
    Rules Rules (lenient) 0.2195 0.6977 0.6857
    ML/DL
    CRF 0.8050 0.8658 0.7834
    BERT 0.6583 0.9022 0.9044
    DistilBERT 0.6237 0.8823 0.8998
    ∆ 0.5855 0.2045 0.2187
    „On 15 January 2000 the District Court
    upheld the Judgment. “
    „The applicant refused to see his
    son in June 1999.“
    F-Scores
    Filtz, E., Navas-Loro, M., Santos, C., Polleres, A. and Kirrane, S., 2020. Events Matter: Extraction of Events from Court Decisions. Proceedings of the 33rd
    International Conference on Legal Knowledge and Information Systems.
    Towards Explainable and Neuro-Symbolic AI
    Assessing Effectiveness
    30 court decisions of European Court of Human Rights (ECHR)
    29

    View full-size slide

  30. Towards Explainable and Neuro-Symbolic AI
    Model Agnostic Explainability
    Dieber, J. and Kirrane, S., 2022. A novel model usability evaluation framework (MUsE) for explainable artificial intelligence. Information Fusion.
    30

    View full-size slide

  31. Towards Explainable and Neuro-Symbolic AI
    Model Agnostic Explainability
    Dieber, J. and Kirrane, S., 2022. A novel model usability evaluation framework (MUsE) for explainable artificial intelligence. Information Fusion.
    31

    View full-size slide

  32. KG-based AI for Self-Determination
    Challenges & Opportunities
    § General-purpose policy languages could be used for risk-
    based conformance checking such as that envisaged in the
    proposed EU AI Act
    § Policy profiles with well-defined semantics and complexity
    classes are needed for (semi)automatic compliance checking
    and to facilitate negotiation
    § Performance and scalability are major challenges as
    applications will need to interact with multiple distributed
    data sources
    § Self Sovereign Identity (SSI) technologies are relatively new
    and may suffer from vulnerabilities (e.g., identity theft)
    § The W3C recommendations for decentralized provenance
    management provides a mechanism for attributing data to
    its sources or contributors.
    § For approaches involving the interaction between LLM and
    KGs, the transparency of the LLM itself still depends on the
    owner
    § Studies report limitations of LLMs in human-like tasks
    (e.g., explanations, memories, and reasoning over factual
    statements)
    § Neuro-symbolic systems play a vital role in enhancing
    trustworthiness by enabling communication between
    modules and facilitating tracing
    32

    View full-size slide

  33. § My colleagues, students, friends, and family
    § My co-authors
    § My habilitation thesis reviewers
    § My habilitation committee members
    § The SPECIAL & PENNI project funding agencies
    Thank you / contact details
    The PENNI project is funded by the FWF Austrian Science Fund and the
    Internet Foundation Austria under the FWF Elise Richter and netidee
    SCIENCE programmes as project number V 759-N.
    The project SPECIAL (Scalable Policy-awarE linked data arChitecture for
    prIvacy, trAnsparency and compLiance) has received funding from the
    European Union’s Horizon 2020 research and innovation programme
    under grant agreement No 731601 as part of the ICT-18-2016 topic Big
    data PPP: privacy-preserving big data technologies.

    View full-size slide