Marieke Peeters

Utrecht University

Research Doctorate, Artificial Intelligence

January 1, 2010 – January 1, 2014

Utrecht University

Master, Cognitive Artificial Intelligence

January 1, 2006 – January 1, 2010

Maastricht University

Master, Educational Psychology

January 1, 2000 – January 1, 2006

Gemeente Eindhoven

Research consultant AI Ethics and Governance

October 1, 2023 – April 1, 2024

Eindhoven, Noord-Brabant, Nederland · Hybrid

Asimovo

Human-AI-robot teaming advisor

April 1, 2023 – Present

Rotterdam, Zuid-Holland, Nederland

NPO

Responsible AI and AI governance consultant

November 1, 2022 – March 1, 2024

Hilversum, Noord-Holland, Nederland

Nederlandse AI Coalitie | NL AIC

Lid Strategische Adviesraad

September 1, 2022 – January 1, 2025

Den Haag, Zuid-Holland, Nederland

Hogeschool Utrecht

Hogeschoolhoofddocent Responsible Applied AI

May 1, 2022 – Present

Utrecht, Nederland · Hybrid

PGGM

Projectleider en onderzoeker ethics & AI

May 1, 2022 – October 1, 2024

Zeist, Utrecht, Nederland

Amsterdam Data Academy

Teacher and course developer

March 1, 2022 – January 1, 2023

Amsterdam, Noord-Holland, Nederland

Mooncake AI

Freelance research consultant human-centered & responsible AI

January 1, 2022 – Present

Amsterdam, Noord-Holland, Nederland

Xomnia

Analytics Translator

February 1, 2020 – August 1, 2021

Amsterdam en omgeving, Nederland

TNO

Research scientist artificial intelligence (senior early 2019)

January 1, 2017 – December 1, 2019

Soesterberg

TU Delft

Postdoc in Artificial Intelligence and Human-Technology Interaction

February 1, 2014 – January 1, 2017

Delft Area, Netherlands

Universiteit Utrecht

PHD Candidate in Artificial Intelligence (degree obtained)

February 1, 2010 – January 1, 2014

TNO

Internship for Master Cognitive Artificial Intelligence

November 1, 2009 – February 1, 2010

Universiteit Utrecht

Master thesis project for Cognitive Artificial Intelligence

July 1, 2008 – August 1, 2009

Universiteit Utrecht

Teaching assistant - research methods and statistics

August 1, 2007 – July 1, 2009

Maastricht University

Teaching assistant - research methods and statistics

January 1, 2003 – May 1, 2004

Maastricht Area, Netherlands

Zeilschool de Kikkert

(Chief) Sailing instructor

June 1, 2000 – September 1, 2008

Lemmer, the Netherlands

SPRONG Responsible Applied AI

May 1, 2022 – Present

De Hogeschool van Amsterdam (HvA), Hogeschool Rotterdam (HR), Hogeschool Utrecht (HU) en de kernpartners Gemeenten Amsterdam en Rotterdam, Provincies Zuid-Holland en Utrecht, Cupola XS, Media Perspectives en CGI, hebben de ambitie om de komende acht jaar een krachtige onderzoeksgroep op te bouwen die regionaal en nationaal wordt (h)erkend als hét centrum voor praktijkgericht onderzoek op het gebied van Responsible Applied AI. Deze SPRONG-groep bouwt voort op bestaande onderzoeksgroepen met complementaire expertise van het Centre of Expertise Applied Artificial Intelligence van de HvA, het Datalab: Livinglab voor AI & Ethiek van HR en het Kenniscentrum Digital Business & Media van de HU. Huidig AI-onderzoek is veelal fundamenteel en op de technologie gericht en voorziet daarmee tot nu toe nauwelijks in antwoorden op vragen hoe AI op een verantwoorde wijze te implementeren. De SPRONG-groep verricht onderzoek naar verantwoorde AI oplossingen voor bedrijven en instellingen. Met de onderzoekservaringen en resultaten heeft de SPRONG-groep vervolgens het doel om een Responsible Applied AI methodologie te ontwikkelen die helpt om AI oplossingen te ontwerpen, ontwikkelen en implementeren. Om deze methodologie te ontwikkelen, is kennisopbouw en -deling nodig die onderzoekers samen ontwikkelen met de beroepspraktijk. Startpunt is daarom de (door)ontwikkeling van drie hybride leeromgevingen rondom de toepassingsgebieden Retail, Zakelijke dienstverlening en Media, waarin ontwerpers, AI-ontwikkelaars, probleemeigenaren, eindgebruikers, onderzoekers en studenten samen optrekken. Gedurende het SPRONG-programma wordt het aantal toepassingsgebieden uitgebreid en waar mogelijk nationaal opgeschaald. Aan iedere leeromgeving zijn specifieke opleidingen en praktijkpartners verbonden die meedenken over het programma. Doel is om vanuit de infrastructuur van de leeromgeving praktische tools, instrumenten, onderwijs en trainingen te ontwikkelen die breed inzetbaar zijn.

Ethics and AI - research consultant project

May 1, 2022 – Present

My research consultancy focuses on mapping the current state of the art regarding ethics and governance of AI, bringing together developments, best practices and lessons learned in academia, business, government, and practice.

Workshops for SMEs to help them create a(n even) bigger impact with AI

April 1, 2022 – July 1, 2023

I'm working with the NLAIC to create and pilot-test two workshop formats to be used by the NLAIC local hubs. The formats focus on aiding SMEs in their journey towards creating impact with AI. The two formats focus on different types of SMEs: 1) SMEs that aren't using data science or AI yet, but want to catch up with their competition (or outsmart them) by leveraging the possibilities of AI. 2) SMEs that offer AI services or products and want to explore (new) product-market fit(s) by learning more about the trends and developments within a particular sector.

Introductory course Python, Pandas, and SciKitLearn

March 1, 2022 – May 1, 2022

My first project was helping out Amsterdam Data Academy by developing and teaching a short course about the basics of Python, and working with data using Pandas and SciKitLearn. I handed over the course to a new teacher, and nowadays I only provide the ethics part of the training course, focusing on the ethical considerations of data science and machine learning.

Building my own business

September 1, 2021 – March 1, 2022

Between September 2021 and March 2022 I took time to build my own freelance company - deciding what I wanted to focus on, what my business plan would be, who my customers are, and creating my website.

NLAIC/HvA Teach the Teacher project

June 1, 2021 – September 1, 2021

Through the Netherlands AI Coalition, five Universities of Applied Sciences (UAS - from Amsterdam, Rotterdam, The Hague, Breda, and Eindhoven) joined forces to upskill their teachers with knowledge about AI-driven developments within their sector. The goal is for every teacher to understand the opportunities and risks involved in applying AI within their sector, to be able to provide examples and discuss them with their students, and to work this knowledge into their own educational programs for students. In addition, there will be more specialized courses for teachers who want to learn more, e.g. work with AI technology themselves, or even develop new tools and technology for their sector. Each UAS is initially developing courses for a specific sector and faculty (e.g. Amsterdam: Business and Economics; Rotterdam: Health; The Hague: Justice and Public Services, etc.). This means that there will be various "generic" modules, reused across faculties and educational programs. In addition, various sector-specific modules are developed that tie directly into the specifics of that application domain. I was the project leader for the entire project led by the NLAIC consortium, as well as the particular part of the project taking place at the HvA in Amsterdam. As part of the job, I coordinated the collaboration between the UAS's. Furthermore, I developed part of the generic courses reused across the UAS's, and coordinated the development of the courses specific to the faculty selected for Amsterdam to start with (Business and Economics faculty). Unfortunately, due to getting accepted for the Antler startup generator program, I had to carry over my tasks for this project to one of my Xomnia colleagues, Lisanne Rijnveld.

Ethical reviewer @ Dutch inspection organization

March 1, 2021 – September 1, 2021

In recent years, this client had developed a number of prediction models that support inspection practice. Internally, the question arose whether, and how, these models can be ethically tested. To this end, use was made of the checklist recently drawn up by the High Level Expert Group of the European Commission. I was asked to contribute as an external reviewer to the review process and to think about future steps to take the models and the working method within the institute to an (ethically) higher level.

Agent-based simulation @ Dutch grid operator

February 1, 2021 – April 1, 2021

This project focused on researching future scenarios in the energy market. In the future, more use will be made of energy generated by wind and sun. As a result, temporary storage capacity is created at times, which must then be prevented by making agreements with local “flex providers” (such as solar parks or wind farms), who can absorb the surplus by temporarily producing less energy. In this project we worked on a simulation that enables us to investigate which market strategies could develop in a local flex market, and how this remains manageable for the grid operator.

Strategic roadmap support @ App to support healthy habits

December 1, 2020 – April 1, 2021

At the beginning of each quarter, I supported this client during their roadmapping sessions. The client is working on a mobile application for people who want to develop healthy habits. During these sessions, I help them make important strategic decisions for the coming quarter. These decisions relate to the chosen focus, identifying and addressing the biggest challenges, and setting priorities for possible directions for improving intelligence in the app.

AI Oversight Lab

December 1, 2020 – September 1, 2021

I worked with TNO on setting up a consortium in the AI ​​Oversight Lab: an innovation lab of the NLAIC, led by TNO, consisting of three lab spaces. In the lab areas, research is being conducted into how the government can make responsible and reliable use of algorithms. Lab room 1 focuses on frameworks and guidelines, lab room 2 works on the development and collection of best practices, and lab room 3 develops tools and techniques for improving, testing and auditing the extent to which algorithms comply with the set frameworks and guidelines. Explorations are currently underway to cooperate with a consortium drawn up by the municipality of Rotterdam, consisting of various municipalities, provinces and government agencies.

Responsible AI Webinar Series

October 1, 2020 – September 1, 2021

An in-depth webinar series about the responsible use and application of AI. In this series, various invited speakers discuss a range of topics related to the ethical, accountable, and/or sustainable use of AI. Xomnia teams up with partners, experts, and guest speakers to cover a broad range of expertise and perspectives on the field. The series aims to contribute to issues identified at various levels within companies, ranging from individual employees to data science and business teams, to boardroom meetings. The aim is to inform, educate and support anyone who is interested in Responsible AI, by offering an overview of cutting edge tools, methods, best practices, and lessons learned.

Teaching training courses in Data Science

September 1, 2020 – September 1, 2021

Throughout the year I regularly teach training courses for professionals on the following subjects: - Introduction to Big Data: what are big data; what is the difference between Data Science, Machine Learning and Artificial Intelligence; what types of machine learning models are most popular, and for what types of problems are they used; what is the GDPR and how does it apply to "your" daily work? - First Aid in Data Analytics: basic charting in Excel and Tableau; what differentiates a "good" visualisation from a bad one? - Intro to Python: Basics of Python; Data Frames in Pandas; Plotting with Matplotlib, Seaborn, and Plotly. - Use Case Discovery: How to recognize, identify, and specify data science use cases that will increase your business value? - Storytelling with Dashboards: How to create a dashboard using either Tableau or PowerBI that helps you tell the story hidden within your data?

Internal projects at Xomnia

September 1, 2020 – September 1, 2021

I spent my time on multiple internal projects, including: - a monthly webinar on the topic of Responsible AI; - offering a variety of training courses to our clients about Big Data, Programming in Python, and using Tableau and PowerBI for visualisations; - co-authoring proposals for clients; - developing and amplifying new consultancy services; and - developing a new training on Responsible AI.

Blueprint Data Integratie Architectuur (Gemeente Amsterdam)

March 1, 2020 – July 1, 2020

The team (consisting of experts from Xomnia as well as experts from the City of Amsterdam) delivered a Blueprint for a new Data Integration Platform that will help the City move forward in their data driven innovation. In four months' time a conceptual, logical, and physical architecture has been created, depicting the way forward in liberating and integrating data in a safe and responsible manner. Gartner Group assisted in reviewing the main deliverables. Furthermore, the team developed a future vision on data modeling for data integration within the City of Amsterdam.

Effectief informatie-gestuurd optreden met behulp van data

July 1, 2019 – December 1, 2019

Om veiligheidsvraagstukken adequaat aan te kunnen pakken, werden de mogelijkheden verkend om meer met data te doen. Er werd wel al op verschillende plekken binnen de betreffende organisatie gebruik gemaakt van data, maar dit gebeurde nog niet volledig integraal. Ik werkte samen met mijn team aan het in kaart brengen van data-gebruik en structurering in werkwijze. Hierbij was het van belang goed te begrijpen wat er op dit gebied al gedaan werd binnen de betreffende organisatie. Door, waar mogelijk, mee te bewegen met bestaande initiatieven, werkten we aan het creëren van draagvlak voor een meer gestandaardiseerde manier van werken (en sturen) met data.

MATRX

January 1, 2019 – December 1, 2019

The field of human-agent teaming (HAT) research aims to improve the collaboration between humans and systems. Small tasks are often designed to explore and evaluate research in human experiments. Two examples are Block Worlds for Teams (BW4T) and Space Fortress. However, these tasks are often outdated, unreliable or uniquely developed to explore one dimension of HAT research. To remedy the lack of a team task library for HAT research, we developed the Human-Agent Teaming Rapid Experimentation software package, or MATRX for short. MATRX’s main purpose is to provide a suite of team tasks with one or multiple properties important to HAT research (e.g. varying team size, decision speed or inter-team dependencies). In addition to these premade tasks, MATRX facilitates the rapid development of new team tasks.

Science Office Kennisplan

July 1, 2018 – March 1, 2019

Gedurende mijn aanstelling bij TNO, heb ik gedurende 8 maanden een nevenrol gehad van 0,5 fte, naast mijn rol als research scientist. In deze rol heb ik nauw samengewerkt met de Director of Science van onze unit. Ik was onder meer verantwoordelijk voor het uitrollen van het “Kennisplan”: alle 16 afdelingen werden aangemoedigd om een lange termijn visie document te schrijven, waarin de kennisstrategie van de afdeling werd vastgelegd in de vorm van een IST / SOLL analyse. Na afloop van de eerste editie heb ik een uitgebreide evaluatie uitgevoerd met de betrokken afdelingen en advies uitgebracht aan de unit-directie over het vervolg van het kennisplan. Er vindt nu een twee-jaarlijkse update plaats met onder meer nieuwe ontwikkelingen in de buitenwereld (zowel in de markt als bij de universiteiten en andere kennisinstellingen), en wat dit betekent voor de kennis strategieën van de afdelingen in de unit.

Mens-machine samenwerking met semi-autonomous vehicles

January 1, 2017 – December 1, 2019

De mogelijkheden van onbemande voertuigen worden steeds meer tastbaar. Echter, de autonomie van dit soort voertuigen is nog beperkt. Zo kunnen deze systemen redelijk goed zelfstandig een gebied in kaart brengen, maar detecteren ze af en toe ook objecten die ze niet herkennen. In zulke gevallen is er een menselijke operator nodig om, bijvoorbeeld, een label toe te kennen aan het object, en de situatie op veiligheid te schatten. Ook is het vaak ingewikkeld voor meerdere onbemande voertuigen om de beelden en andere metingen te integreren tot één gedeeld situationeel overzicht. Voor een optimale inzet, is het noodzakelijk dat er nieuwe integratie- en interactiemogelijkheden worden uitgedacht die dit soort systemen in staat stelt een gezamenlijk beeld op te bouwen van de omgeving, en met elkaar en met menselijke operators samen te werken.

Human-AI Teaming

January 1, 2017 – December 1, 2019

The Human-AI Teaming project at TNO investigated how people and AI might work together in future settings. We investigated a range of possibilities through proof of concept demonstrators. Examples of such concepts include: Proactive communication: Can AI learn what information is essential to share with team members to improve the overall team performance? Management by exception: Can AI proactively determine when it requires assistance from a human team member, and gradually include the human into the loop, to ensure that the human is up to speed by the time the sh*t hits the fan? The SAIL framework: Can we add a modular layer of social AI functionalities as part of a larger multi-agent system, consisting of autonomous vehicles, intelligent information processing agents, and humans, collaborating in a large socio-technical structure? Does this layer improve team performance measures, such as information sharing, situation awareness, accuracy in handling events and threats, and so on? Trust calibration: Can we enrich AI systems with the ability to make an estimation of trust between team members and act in ways to establish calibrated trust between the team members? Can we model longitudinal trust dynamics to support this process? Work agreements: How can we ensure that an autonomous agent remains within acceptable boundaries when acting in a confined/predefined environment? Explainable AI: What is needed for a human team member to understand, e.g., what an intelligent system is doing, what it is proposing to do, and why, or how the human is able to intervene when the intelligent system is working with faulty assumptions and/or calculations?

ReJAM

January 1, 2015 – January 1, 2017

The ReJAM project aims to develop Robots engaging elderly in Joint Activities with Music. The objective of ReJAM is the promotion of physical, cognitive, emotional and social wellbeing through various music-related activities, e.g. physical exercises, games, reminiscence, and making music together. The activities are designed especially for group activities: ReJAM is well-suited for use in meeting centers or together with visitors at home.

SALIG++

February 1, 2014 – May 1, 2015

The SALIG++ project offers novel solutions based on ICT-support for self-care by elderly and the bidirectional awareness and interaction between elderly and informal carers in collaboration with formal care in order to promote and prolong the well-being of elderly in living at home. SALIG++ makes it possible for carers to, for example, visit the home of the elderly from a distance and experience it as if they were actually there. The primary benefit is that carers become fully informed about the status of the elderly, her medical status as well as her home and devices (such as stove and faucets). The expected result is a platform for the delivery of self-care @ home services, as well as for stimulating and supporting daily activities at home by means of technologies that include smart sensing environments integrated with adaptable information system to connect elderly people with informal carers.

Military Human Enhancement

February 1, 2014 – May 1, 2015

We investigated the use of advanced systems to enhance military personnel in targeting decisions. We examine how moral and legal objectives can best be implemented within the design and development of military command and control systems, and investigate how current and emerging combat systems and targeting technologies are challenging our understanding and interpretation of Just War Theory and the Laws of Armed Conflict. The proposed research (a) formulates design principles for computer assisted combat systems that are compliant with relevant legal and moral doctrines (b) investigates whether the principles of Just War Theory and ensuing rules of engagement can be reconciled with advanced forms of user enhancement by means of IT and (c) establishes whether the moral doctrines of war should be reformulated given the ubiquity of cognitive enhancement by computer systems. The project specifically looks at a future generation of Automated, Intelligent Combat and Decision Support Systems for Command and Control that are being developed by The Netherlands Navy, the Netherlands Defence Academy and the CAMS Force Vision team of the Dutch Ministry of Defence.

User Modeling

February 1, 2013 – July 1, 2013

During my time at TNO I contributed in several projects revolving around personalization and user modeling. Especially within the ePartners that Care program, we worked on a functional architecture to personalise virtual assistants that support behaviour change.

Personalised Educational Games

February 1, 2010 – February 1, 2014

Scenario-based training engages learners in the reenactment of short storylines ecompassing real-life situations, i.e. situations that learners may actually experience in their future profession. Even though scenario-based training is useful for any type of profession, it is particularly feasible for the training of complex high-risk professions that heavily rely on decision-making and situation awareness. Examples of such professions are the military, police, emergency health care, crisis management, and the fire department. Virtual worlds are increasingly used as a medium for scenario-based training so as to create more training opportunities for learners. To take away the need for additional people to control, for instance, non-player characters (e.g. opponents or team players), recent efforts have focused on the inclusion of Artificial Intelligence (AI) to automatically control such parts of the virtual world. The research aim of Personalised Educational Games is to add yet another layer of AI on top of the virtual world inhabited by AI-controlled characters: a director system. The director system monitors the learner while playing the game. Based on the performance of the learner, the director system selects and manipulates the content presented in the virtual world and/or the behavior of the characters in the storyline. The aim is to maximize the effectiveness of the training, i.e. to provide the learner with suitable exercises so as to maximize the learner’s training progress. An adaptive and personalized training curriculum is constructed with the use of solid didactic principles. In addition, the design of the director system leaves room for the instructor to take over control when and where he/she sees fit.

Workshop Facilitation

Mischief Makers B.V.

June 24, 2026 – Present

University Teaching Qualification (Basiskwalificatie Onderwijs)

VSNU / 3TU federatie / TU Delft

June 24, 2026 – Present

Leidinggeven zonder macht

SUAS - A Schouten & Nelissen Company

June 24, 2026 – Present