Post Duration: 18 months
Salary Scale: Postdoctoral IUA Researcher Scale PD1 Point 1 – PD2 Point 4
Location: AI Accountability Lab, School of Computer Science & Statistics, Trinity College Dublin
Reports to: Dr. Abeba Birhane
Closing Date: 30 / 04 / 2025

The AIAL is an independent research lab with a mission to ensure that AI technologies work for the public, particularly those at the margins of society, who tend to be disproportionately negatively impacted. We believe rigorous research and empirical evidence are the antidote to most of the current issues plaguing the AI industry, by holding responsible bodies accountable for the adverse consequences, and ushering meaningful transformative change.

There exists widespread enthusiasm from big tech companies, AI vendors, and government bodies alike to integrate AI technologies into decision making processes. This emerges from the belief that doing so accelerates positive social transformation, societal benefits, and human flourishing in the long term. Yet, AI systems are integrated hastily into numerous social sectors, most of the time without rigorous vetting. As a result, AI systems built on social, cultural, and historical data and operating within such a realm tend to diminish fundamental rights, and keep systems of authority and power intact. As a result, they benefit a handful of corporations, and exacerbate and widen inequity, rather than contribute to societal benefits, positive transformation, and human flourishing. We believe rigorous research and empirical evidence are the antidote to most of the current issues plaguing the AI industry, by holding responsible bodies accountable for the adverse consequences, and ushering meaningful transformative change.

The AIAL is an independent research lab with a mission to ensure that AI technologies work for the public, particularly those at the margins of society, who tend to be disproportionately negatively impacted. Research and product development in AI currently benefits a few powerful actors, reinforces systems of power, and exacerbates and widens inequities. The AIAL is dedicated to ensuring that the wider AI ecology — from research and product development, to regulation — centres public interest, particularly, the most marginalised and disenfranchised in society.

Research excellence and technical rigour are paramount to us. As is research with practical implications that serve the disproportionately negatively impacted. Thus, we partner and collaborate with research centres, civil society, and rights groups across the globe. These collaborations, active conversations, and allyship will give our work the necessary weight and inertia to advance the laboratory’s central mission of asserting rights. Driven by concerns that affect the most marginalised, we strive to uncover, document, and study AI technologies that pervade society in order to:

• challenge and dismantle harmful technologies;
• inform evidence-driven policies;
• hold responsible bodies accountable; and
• pave the way for a future marked by just and equitable AI.

Research project/Challenge
The AIAL (AI Accountability Lab) is seeking to appoint a Postdoctoral Researcher to work on developing a justice-oriented audit framework synthesising computational methods, theories of justice, and existing regulations to premeditatively focus audits towards meaningful accountability. The goal of the framework is to provide audit practitioners with practical tools such as guiding questions and rubrics that shape perspectives towards rigorous justice oriented audits.

The position corresponds to work in one or more of the following areas:

1. Accountability
   1. Mapping accountability mechanisms and governance structures and their alignments with fundamental rights and freedoms and legal frameworks.
   2. Challenging existing accountability mechanisms that do not consider or sufficiently address social inequalities, power and resource asymmetries.
   3. Developing new methods for ensuring accountability beyond technical and organisational considerations that provide empirical evidence for holding stakeholders accountable for AI development, provision, and deployments.
2. Auditing
   1. Developing audit methodologies for specific stages in the AI lifecycle focused on ensuring justice, accountability, and transparency beyond merely satisfying legal requirements.
   2. Development of verifiable, replicable, and reproducible design methodologies and frameworks and using these in the execution of audits.
   3. Developing audit tools and frameworks to evaluate AI development and deployments with a specific focus on risk and harm mitigations beyond technical and organisational issues.
3. Dissemination
   1. Actively work with numerous audit practitioners, researchers, and civil society and rights groups to develop, refine, and disseminate the developed work.
   2. Participate in policy-making to shape AI accountability and auditing processes.
   3. Publish findings and participate in discussions to promote accountability and responsibilities concerning AI.

The appointment of the Postdoctoral Researcher will contribute to shaping best practices for AI systems evaluation and risk mitigation, ensuring compliance with emerging regulations, and supporting both public sector stakeholders in fostering a culture of accountability.

Duties and Responsibilities

• Manage and conduct the research under the leadership of the Principal Investigator
• Disseminating outcomes of research through project reports as well as peer-reviewed academic publications, technical reports, industry and public events, and other channels.
• Assist in the further development of research potential by pursuing external funding.
• Where relevant to the research topic, and within the bounds of TCD policies, assist in supervising students, interns, and other personnel.
• Organising meetings and workshops with civil societies, NGOs, and other stakeholders in context of the project.
• Administrative and management tasks associated with the project.
• Carry out any other duties within the scope and purpose of the job as requested by the PI.
• Comply with all TCD policies and regulations, including those in relation to Research Ethics and Health and Safety.

Qualifications, Skills and Experience Required
The ideal candidate will demonstrate the appropriate mix of knowledge, experience, skills, talent and abilities as outlined below:

Knowledge and Experience

• A PhD in a relevant discipline such as Machine Learning; AI; Statistics; Critical data, information, communication, and/or media studies, or equivalent (essential)
• Strong knowledge and experience regarding AI development, testing, and evaluation (essential)
• Strong knowledge and experience with developing and implementing AI audits (essential)
• Strong knowledge of the AI auditing landscape (essential)
• In-depth knowledge of theories of structural injustice, inequity, and power asymmetry (essential)
• Knowledge or awareness of legal frameworks such as AI Act, DSA, and GDPR privacy and data protection regulations such as GDPR (ideal).
• Experience working with civil society and rights groups involved in auditing and policy-making (ideal)
• Evidence of a research profile and publication record (essential).
• Knowledge of research techniques and methodologies (essential).

Skills, talents & abilities

• Capability for demonstrable high quality research (essential).
• Experience working on collaborative projects (ideal).
• Experience organising stakeholder discussions and workshops (ideal)
• Experience working on individual and collaborative funding proposals (ideal).
• Experience working in teams with diverse and multi-disciplinary cohorts (ideal).
• Demonstrable leadership and willingness to support others (ideal).

Salary Scale:

Postdoctoral Researcher – Point PD1.1 €45,847 to Point PD2.4 €58,479 as per the IUA payscales*.* Appointment will be commensurate with qualifications and experience and in line with current Government pay policy.

Closing date: 30/04/2025

Application Process
Interested candidates can submit their application by emailing aial@tcd.ie with the subject line: “AIAL Audit Framework Postdoc – Application”.
Applications must include:

• Cover Letter
• CV

Informal enquiries about the role can also be sent to the email aial@tcd.ie with the subject line “AIAL Audit Framework Postdoc – Enquiry”

Post Duration: 24 months
Salary Scale: Research Fellow Point 1 – Point 4
Location: AI Accountability Lab, School of Computer Science & Statistics, Trinity College Dublin
Reports to: Dr. Abeba Birhane
Closing Date: 20 / 04 / 2025

The AIAL is an independent research lab with a mission to ensure that AI technologies work for the public, particularly those at the margins of society, who tend to be disproportionately negatively impacted. We believe rigorous research and empirical evidence are the antidote to most of the current issues plaguing the AI industry, by holding responsible bodies accountable for the adverse consequences, and ushering meaningful transformative change.

There exists widespread enthusiasm from big tech companies, AI vendors, and government bodies alike to integrate AI technologies into decision making processes. This emerges from the belief that doing so accelerates positive social transformation, societal benefits, and human flourishing in the long term. Yet, AI systems are integrated hastily into numerous social sectors, most of the time without rigorous vetting. As a result, AI systems built on social, cultural, and historical data and operating within such a realm tend to diminish fundamental rights, and keep systems of authority and power intact. As a result, they benefit a handful of corporations, and exacerbate and widen inequity, rather than contribute to societal benefits, positive transformation, and human flourishing. We believe rigorous research and empirical evidence are the antidote to most of the current issues plaguing the AI industry, by holding responsible bodies accountable for the adverse consequences, and ushering meaningful transformative change.

The AIAL is an independent research lab with a mission to ensure that AI technologies work for the public, particularly those at the margins of society, who tend to be disproportionately negatively impacted. Research and product development in AI currently benefits a few powerful actors, reinforces systems of power, and exacerbates and widens inequities. The AIAL is dedicated to ensuring that the wider AI ecology — from research and product development, to regulation — centres public interest, particularly, the most marginalised and disenfranchised in society. Research excellence and technical rigour are paramount to us. As is research with practical implications that serve the disproportionately negatively impacted. Thus, we partner and collaborate with research centres, civil society, and rights groups across the globe. These collaborations, active conversations, and allyship will give our work the necessary weight and inertia to advance the laboratory’s central mission of asserting rights. Driven by concerns that affect the most marginalised, we strive to uncover, document, and study AI technologies that pervade society in order to:

• challenge and dismantle harmful technologies;
• inform evidence-driven policies;
• hold responsible bodies accountable; and
• pave the way for a future marked by just and equitable AI.

Research project/Challenge
The AIAL (AI Accountability Lab) is seeking a Research Fellow to work on Policy translation and AI evaluation under the broad topic of AI accountability. The primary role of the successful candidate in policy translation is to investigate existing policy gaps and produce policy insights both through novel research as well as skillful synthesis and interpretation of existing empirical evidence. The candidate will work on AI evaluation and execute audits to assess a broad range of AI systems. This position will focus on identifying, documenting, and bridging gaps between existing legal frameworks, industry practices, and deficiencies involving harms, discrimination, and other potential impacts on fundamental rights and freedoms.

The position corresponds to work in one or more of the following areas:

1. Policy translation:
   1. Analysis of regulatory frameworks such as the Digital Services Act (DSA), AI Act, GDPR, and other relevant directives and national laws to research regulatory loopholes, particularly those that further disadvantage communities at the margins with the aim of developing empirical investigation and/or developing policy recommendations using original and existing work.
   2. Awareness of relevant standards and certifications, and their role and sufficiency to address legal requirements, as well as identifying deficiencies and how they can be mitigated or reduced.
   3. Summarise, contextualise, and synthesise findings from technical work in language that speaks to and is digestible by a variety of stakeholders. This includes policy makers, government stakeholders, the research community, civil society groups, and impacted communities.
   4. In addition to (or as well as) academic research, the successful candidate will write policy briefs, opinion pieces, and policy analyses that synthesize and bridge gaps between the latest developments in AI research, regulations, and public debates.
2. Accountability
   1. Defining accountability mechanisms and governance structures that align with fundamental rights and legal frameworks.
   2. Conducting audits focused on justice, equity, and accountability beyond merely satisfying legal requirements and upholding fundamental rights and freedoms.
   3. Supporting the development of audit tools and frameworks to evaluate AI systems with a specific focus on risk and harm identification and mitigations beyond technical and organisational considerations.

Duties and Responsibilities

• Manage and conduct the research under the leadership of the Principal Investigator
• Conduct landscape analysis regarding developments concerning AI in academia, industry, and legal domains
• Support in managing communications, such as through newsletters and social media
• Developing policy briefs to address emerging issues and challenges
• Disseminating outcomes of research through project reports as well as peer-reviewed academic publications, technical reports, industry and public events, and other channels.
• Assist in the further development of research potential by pursuing external funding.
• Where relevant to the research topic, and within the bounds of TCD policies, assist in supervising students, interns, and other personnel.
• Administrative and management tasks associated with the project.
• Carry out any other duties within the scope and purpose of the job as requested by the PI.
• Comply with all TCD policies and regulations, including those in relation to Research Ethics and Health and Safety.

Qualifications, Skills and Experience Required
The ideal candidate will demonstrate the appropriate mix of knowledge, experience, skills, talent and abilities as outlined below:

Knowledge and Experience

• A PhD in Computer Science, Law, or a relevant field (essential).
• Strong knowledge of legal frameworks such as AI Act, DSA, and GDPR (essential).
• Knowledge regarding impact assessments and auditing practices (essential).
• Knowledge of standards and certifications relevant to AI development and auditing (ideal).
• Excellent writing and communication skills that can target specific stakeholders groups such as policy makers and regulators (essential).
• Knowledge of research techniques and methodologies (essential).

Skills, talents & abilities

• Evidence of an excellent research profile and publication record (essential).
• Experience engaging with policy makers and civil society groups (ideal).
• Experience working on collaborative projects (ideal).
• Experience working on individual and collaborative funding proposals (ideal).
• Experience working in teams with diverse and multi-disciplinary cohorts (ideal).
• Demonstrable leadership and willingness to support others (ideal).

Salary Scale:

Research Fellow – Point 1 €65,237 to Point 4 €71,085 as per the IUA payscales*.* Appointment will be commensurate with qualifications and experience and in line with current Government pay policy.

Closing date: 20/04/2025
Application Process
Interested candidates can submit their application by emailing aial@tcd.ie with the subject line: “AIAL Policy Translation Postdoc – Application”.
Applications must include:

• Cover Letter
• CV

Informal enquiries about the role can also be sent to the email aial@tcd.ie with the subject line “AIAL Policy Translation Postdoc – Enquiry”

I was part of the panel titled “Bending the arc of AI towards the public interest” at the Summit, which focused on the “Public Interest AI” theme. This piece reflects my thoughts on “Public Interest AI” and the potential it holds to meet actual public needs. In order to meet actual public needs, governments, policy makers, and international intergovernmental and philanthropic bodies need to be clear about what both “AI” and “public interest” (not to be confused with “public utility”) are and are not.

What AI in the public interest is/isn’t
One of the most common framings–and the biggest sins in this space–is the false dichotomy that presents AI only in terms of “opportunities and risks”. This doctrine sees the world as consisting only of those building AI and thus “unlocking opportunities” versus those uncovering and mitigating risks as “slowing progress”. While “innovation”, “advancement” and “economic” gains fit squarely in the former category, anything that calls for responsibility, accountability, and critical thinking tends to be sidelined as outside the purview of “opportunities”, “innovation”, and “advancement”. This hinges on the deeply mistaken assumption that AI technologies are inherently good and that mass adoption inevitably leads to public benefits. In reality, none of this is true. There is nothing that makes AI systems inherently good. Without intentional rectification and proper guardrails, AI often leads to surveillance, manipulation, inequity and erosion of fundamental rights and human agency while concentrating power, wealth and influence in the hands of AI developers and vendors.

In the context of this framing, “public interest AI” often comes down to three broad and overlapping forms. First, it involves the idea of equipping public institutions–such as academic universities and civil society organisations–with the resources to build their own versions of AI systems, often replicating existing industry trends, such as training large language models for European languages; Second is ”AI for good” type of initiatives that focus on leveraging AI to “solve” numerous social, cultural, historical and political issues such as tackling the UN’s Sustainable Development Goals (SDGs)¹ using AI. Finally, public interest AI is sometimes framed as improving existing systems–making them less biased, more representative, and better suited for “everyone”², which sounds great in theory. Some of the initiatives in this category focus on curating good quality training data, based on the assumption that better data leads to improved models. This is true to some extent. However, without a clear vision grounded in rigorous audits and empirical evidence, and geared towards shifting power, agency, and benefits from tech oligopolies towards the public, such initiatives can end up producing “more accurate” and “better-performing” models that power surveillance, aid genocide, or revive pseudoscience. Large scale, high quality, and representative health data in the hands of tech oligopolies or profit-first AI companies, for example, can still result in massive benefit for these companies and no meaningful benefit or even harm to the public.

At the core of these approaches to “public interest AI” is the common goal: giving the “public” more AI or feeding existing corporate models with more or ”better” data — mitigate numerous issues that arise from technology with more technology. Contrary to this belief, AI technologies—whether in the hands of big tech or civil society—are inherently conservative, exploitative, and inextricably entangled with capitalism.

Although it might be a mistake to cast all of these initiatives as unproductive and unhelpful, they fall largely within the technosolutionist paradigm–the belief that all or most problems can be solved through technology. This approach is unlikely to bring about any meaningful change to the public, as these techno-centric solutions are never developed outside of corporate silos. Any meaningful intervention that aims to centre the interest of the “public” needs to go beyond aligning with the “innovation” narrative.

What does the public want?
It would be difficult, if not impossible, to arrive at a clear consensus on what the public wants, how best it should be represented, or even who the “public” is. However, it would be even more absurd to assume that government interests or corporate practices align with those of the public, or that these bodies hold useful insight into the public needs and interests. If there was a shred of doubt that the tech industry might be interested in anything other than amassing unprecedented power, control, influence, and revenue at any cost, recent events should put that to rest. In the wake of politically fuelled fascism, it has become crystal clear that slogans like ‘benefiting humanity’ are nothing but a facade and empty corporate PR. Big tech corporations along with large social media and AI companies—including Google, Microsoft, Amazon, Meta, and OpenAI—are bending backwards to align with fascism. One by one, they have willingly and proactively abandoned their pledges to fact-check, prevent misinformation, respect diversity and equity, and refrain from using AI for weapons development.

Centering the vast interest in AI around the public is indeed one way to bring some balance, as it allows us to recognise the massive concentration of technological and market power. This growing imbalance is increasingly at odds with democratic and social structures, moving us away from a public-driven approach to techno-feudalism. But what are the most effective and productive ways to do this?

Parsing through the “About Us” pages of notable civil society organisations working at the intersection of AI technologies and public interest (e.g., Ada Lovelace Institute, Irish Council for Civil Liberties (ICCL), Data & Society, The AI Now Institute, Access Now, Research ICT Africa) makes it clear that none advocate for approaches that simply ask for “more AI to fix problems caused by AI”. In other words, the “more AI to mitigate existing issues from AI” narrative is squarely outside their purview. Despite the diversity and multiplicity in their missions and objectives, most civil society organisations have overlapping interests: defending and extending fundamental rights, justice, equity, and human dignity by tackling root causes such as power imbalances, resource asymmetry, corporate monopolies, and other structural obstacles.

It should be clear by now that technology in the public interest is not about adding more AI or equipping the public, civil society and research institutes with more compute, data and other resources to replicate corporate AI trends. And neither is generative AI a “magical solution” to a multitude of problems as the tech industry has sold it. Instead, these are problems tech companies have dumped on us. Meaningfully assessing– evaluating the merits, usefulness, and responsible development of AI through regulation and cleaning up the mess from the aftermath has become a task for policy makers, academics, and civil society and rights groups. In this, the public has little agency, power, or influence over the technology that is pushed down their throats or a meaningful mechanism to opt-out. At the same time, we have been experiencing unprecedented technological change over the past couple of decades, a pace that has also normalised a somewhat fabricated fear that the public is being left behind by the “new thing”.

In light of all of this, advancing technology in the public interest requires us to work across many forums and dimensions to address the multitude of problems and challenges from every possible angle. Below are some concrete examples that embody this approach from my perspective. Genuine intergovernmental, policy-making, and funding efforts aimed at fostering and supporting a public-interest technology ecosystem in the public interest might consider these and similar initiatives. This list is by no means comprehensive or representative but reflects my familiarity, domain expertise, experience and positionality.

Concrete steps forward:

1. Accountability
   a) Through nurturing independent and reliable knowledge ecosystems that feed into AI literacy and realistic public understanding of AI. Current AI technologies have captured the public not because these systems are reliable, necessarily useful, or beneficial to the public, but because the tech industry holds a monopoly over the public narrative and controls media and information. This has created a massive gap between public perception and the actual capabilities of AI systems both as 1) consumer products, which often fail to meet their promises and 2) as scientific research, which frequently fall short of basic scientific principles such as openness, a prerequisite to meaningful independent evaluation, reproducibility and replicability. Coordinated, cross-sector efforts that embrace the principle “critique is service” (the same way critique, for instance via the peer-review process strengthens and “steel-man” scientific work–can reduce hype, counter mis/dis-information and shift control of AI narratives from industry players to investigative journalists, academics, civil society and other public interest expert groups. Initiatives that train the next generation of tech journalists (such as the Pulitzer Centre for journalism) and organisations at the forefront of investigative AI reporting (e.g., ProPublica, The Markup, 404 Media) are essential in auditing AI technologies, and disseminating knowledge. In the face of corporate dominance over AI narratives and the information ecosystem, initiatives like these keep the lights on.

   b) Tools of accountability via audits and evaluation. Holding powerful actors answerable for the systems they chose to develop and deploy via rigours, independent audits grounded in theories of change (see here and here for detailed strategies and a roadmap for fostering an ecosystem of independent third-party audits).

   • Rigorous testing and examination of AI artefacts–and their downstream impacts on individuals, groups and society– are essential for understanding, diagnosing, and surfacing harms, risks, and rights violations, and thus a key to meaningful mitigations and changes.
   • Audit repositories and databases should make audit results accessible to accountability actors, ensuring they feed directly into a process of accountability.

   c) Tools of transparency. Transparency and open source/access are prerequisites not only for audits but also for fostering larger accountability ecosystems. Below is an initial list of some required information necessary for shedding light on the practices of developers, vendors and institutions using AI in public services. This list is by no means comprehensive or representative and each by itself may not be transformative. But together they serve as a prerequisites for meaningful accountability efforts.

   • Government AI inventory databases
   • Data on procurement practices - including details on AI funding, tendering processes and, assurances that AI systems are fit for purpose when procured
   • AI Incident databases
   • Data-visualisation, documentation, and storytelling - highlighting harmful AI practices (for example the Anti-Eviction Mapping Project and Surveillance Watch)
   • Benchmarking results and impact assessments
   • Transparency indexes and reports
   • Transparency databases - this might include data on:
     • Data centre operations and infrastructure
     • Energy, water, and other resources consumption
     • Break down of energy use for training vs inference
     • Carbon emissions
     • Government and military contracts
     • Government use of AI in defense and surveillance
     • Lobbying spending and political donations

2. Counter power-measures

   a) Tools of resistance
   Resistance, when rooted in indepth knowledge and a realistic and practical understanding of AI systems, allows the public to both participate in these impactful systems, and also to challenge and reject them. Rejecting from a place of knowledge preempts the lazy dismissal that they don’t understand the tech’.
   Alongside the “Tools of transparency” mentioned above, here are some examples of tools of resistance, as starting points, though not exhaustive or comprehensive).

   • Support a thriving community of technologists and researchers - Developing non-conventional tools of resistance and refusal (for example the Glaze Project, data poisoning tools)
   • Missing data - This can be further categorised into:
     • Public service data - Information that may not have direct monetary value, such as climate data, pollution levels, wildlife tracking, and crisis data (e.g.​​disaster response)
     • Data for transparency - see Tools of transparency above.
   • Local initiatives developing technologies based on “nothing about us without us” principles - Examples include Maori language technologies and Kabakoo Academies

   b) Unions, safe-harbors, and coordinated conveinings play a crucial role in collective action. Unions have historically been powerful in pushing back on harmful AI use, especially in the workplace, where they have driven concrete improvements in working conditions. Spaces and convenings are essential for coordination, organising, and forming allyships amongst all the efforts and initiatives mentioned in this piece (and more) as power comes from standing together. Examples of collective action initiatives include:

   • African data workers’ unions
   • UNI Global Union
   • Worker-owned cooperative structures - for companies, and worker-owned IP (like Karya)
   • Data and technology as public commons - along with institutions for safeguarding and cultivating these commons

   c) Legal actions (for example Foxglove)

If you are involved in or know of other similar initiatives, please let me know and I will add it to the list here.

My deepest gratitude to those, especially underfunded and underappreciated scholars, journalists, technologists, and civil society groups tirelessly working towards more equitable technological futures. I am grateful for the various conversations I took part in over the duration of the Summit in Paris (as well as continual conversations with friends and allies in this space). These have enriched this piece. Special thanks to Thomas Laurent, Jackie Key, Janet Haven, Deb Raji, Udbhav Tiwari, Harshvardhan Pandit and Nabiha Syed for feedback on an earlier version of the draft.

Post Summary
This position offers an exciting opportunity to join the AI Accountability Lab at the ADAPT Centre in the School of Computer Science and Statistics at Trinity College Dublin. The successful candidate will work on a project dedicated to algorithmic auditing. The role involves conceptual work particularly, developing (alongside other AIAL lab members) justice-oriented audit farmwork and audit execution of agentic systems, such as AI companions. This hands-on role is particularly suited to applicants with a background and strong interest in algorithmic audits.

Standard Duties and Responsibilities of the Post

• Developing audit methodologies for specific stages in the AI lifecycle focused on ensuring justice, accountability, and transparency beyond merely satisfying legal requirements.
• Development of verifiable, replicable, and reproducible design methodologies and frameworks and using these in the execution of audits.
• Actively work with numerous audit practitioners, researchers, and civil society and rights groups to develop, refine, and disseminate the developed work.
• Adherence to data protection and ethical guidelines in handling video content.
• Participation in project meetings, presenting updates on audits and audit framework development progress and any observations from the scoping and literature review.
• Contributing to reports, presentations, and academic publications arising from the research.

Funding Information
This project is funded by the AI Accountability Lab under which the successful candidate’s position will be supported.

Person Specification

Qualifications
The successful candidate must hold at least a Bachelor’s degree (or equivalent) in a relevant field (e.g., Computer Science, Law, Science and Technology Studies, Critical data/communication/media studies, or a cognate area).

Knowledge & Experience (Essential & Desirable)
Essential

• Demonstrable interest in accountability research, particularly algorithmic audits.
• Demonstrable interest in approaches and theories of justice, fundamental rights and systems of oppression.
• Familiarity with European regulations, particularly pertaining to mandatory audits and conformity assessments.

Desirable

• Knowledge of the broader algorithmic audit landscape.
• Prior experience executing algorithmic audits.
• Knowledge of (or willingness to learn) theories of Black/Afro feminism, race and decolonial approaches

Skills & Competencies

• Excellent communication skills for reporting progress and findings to the research team.
• Strong organisational skills.
• Ability to work both independently and collaboratively in a multidisciplinary environment.

Application Procedure
Applicants should submit a full Curriculum Vitae to include the names and contact details of 2 referees (including email addresses), to aial@tcd.ie with the subject line “AIAL RA”

Email: AIAL@tcd.ie

Mailing Address

AI Accountability Lab (AIAL),
Department of Computer Science & Statistics,
Trinity College Dublin,
College Green, Dublin 2,
Dublin, Ireland

Social Media

@aial.ie (BlueSky)

Research and product development in AI currently benefits a few powerful actors, reinforces systems of power, and exacerbates and widens inequities. Furthermore, existing governance structures and regulatory processes tend to overlook the concerns and fundamental rights of those who are likely to be disproportionately negatively impacted by ubiquitous integration of AI technologies into society.

There exists widespread enthusiasm from big tech companies, AI vendors, and government bodies alike to integrate AI technologies into decision making processes. This emerges from the belief that doing so accelerates positive social transformation, societal benefits, and human flourishing in the long term. Yet, AI systems are integrated hastily into numerous social sectors, most of the time without rigorous vetting. As a result, AI systems built on social, cultural, and historical data and operating within such a realm tend to diminish fundamental rights, and keep systems of authority and power intact. As a result, they benefit a handful of corporations, and exacerbate and widen inequity, rather than contribute to societal benefits, positive transformation, and human flourishing. We believe rigorous research and empirical evidence are the antidote to most of the current issues plaguing the AI industry, by holding responsible bodies accountable for the adverse consequences, and ushering meaningful transformative change.

The AIAL is an independent research lab with a mission to ensure that AI technologies work for the public, particularly those at the margins of society, who tend to be disproportionately negatively impacted. The AIAL is dedicated to ensuring that the wider AI ecology — from research and product development, to regulation — centres public interest, particularly, the most marginalised and disenfranchised in society. Research excellence and technical rigour are paramount to us. As is research with practical implications that serve the disproportionately negatively impacted.

Thus, we partner and collaborate with research centres, civil society, and rights groups across the globe. Driven by concerns that affect the most marginalised, the AIAL leverages empirical evidence to: inform evidence-driven policies; challenge and dismantle harmful technologies; hold responsible bodies accountable; and pave the way for a future marked by just and equitable AI.

Core Team

Dr Abeba Birhane(she/her)

Founder & Principal Investigator

Dr Abeba Birhane founded and leads the AI Accountability Lab (AIAL) at Trinity College Dublin. She received her PhD in 2022 from University College Dublin and is currently a Research Fellow at the School of Computer Science and Statistics in Trinity College Dublin.

Dr Birhane researches AI accountability, with a particular focus on audits of AI models and training datasets – work for which she was featured on the TIME100 Most Influential People in AI list in 2023. Dr. Birhane also served on the United Nations Secretary-General’s AI Advisory Body and the AI Advisory Council in Ireland.

BlueSky LinkedIn Personal Website

Dr Harshvardhan Pandit(he/him)

Research Fellow

Harsh works on understanding the implications of regulations on technology and how these solve or hinder real-world challenges associated with privacy, legal and regulatory compliance, and consent. His PhD (Computer Science, Trinity College Dublin) explored the application of linked data and semantic web technologies towards GDPR compliance, with a particular focus on consent and provenance.

He currently co-chairs the W3C Data Privacy Vocabularies and Controls Community Group (DPVCG) – which develops interoperable vocabularies for privacy and data protection activities based on legal and practical requirements, is a member of the National Standards Authority of Ireland and contributes to ISO/IEC, CEN/CENELEC, and IEEE standardisation activities regarding privacy and AI, and is a nominated Technical Expert by the European Data Protection Board.

harshvardhan.pandit@adaptcentre.ie | BlueSky LinkedIn Personal Website

Maribeth Rauh(she/her)

Researcher

Maribeth Rauh researches sociotechnical evaluation of AI systems, with particular attention to how they exacerbate structural inequality. Her current work focuses on justice-oriented audit frameworks and evaluations of AI "agents."

Prior to joining the AIAL, she worked as a Research Engineer at DeepMind, collaborating across engineering, research, and ethics teams on questions at the intersection of AI development and societal impact. Her research has been published at top conferences including NeurIPS, ICLR, ACM Conference on Fairness, Accountability, and Transparency (FAccT), and AAAI/ACM Conference on AI, Ethics, and Society (AIES).

maribeth.rauh@adaptcentre.ie | BlueSky LinkedIn Personal Website

Riccardo Angius(they/them)

PhD Researcher

Riccardo is a mixed-method doctorand with a decade-long experience in interdisciplinary teams investigating social dynamics, overtrust in decision support systems, and sociotechnical auditing of predictive and generative stochastic systems.

Their work in Sociolinguistics, Computer Supported Group Work and Science and Technology Studies at the Universities of Padova and Milano-Bicocca was published at impactful conferences such as ACM CHI and AAAI ICWSM. Riccardo's joint work with AI Forensics and Algorithm Watch has been featured by The Washington Post, Wired US, Die Zeit and Nieuwsuur, as well as cited by Pulitzer-winning Julia Angwin, the Reuters Institute and in formal proceedings of the European Commission.

angiusr@tcd.ie | BlueSky LinkedIn Personal Website

Nana Mgbechikwere Nwachukwu

PhD Researcher

Nana Nwachukwu is a PhD Candidate at the AI Accountability Lab, Trinity College Dublin, under the supervision of Dr. Abeba Birhane. Her research focuses on AI ecosystem accountability, algorithmic governance, and justice-oriented frameworks for evaluating socio-technical systems,especially within Global South contexts. She is also an Affiliate at the Berkman Klein Center for Internet & Society at Harvard University.

Nana holds advanced degrees in law, international humanitarian affairs, and digital communications, and has over a decade of experience working at the intersection of technology, human rights, and policy.

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Interns

Shane Gilligan

Intern

Shane Gilligan is currently a Master's student in Trinity College Dublin at the School of Computer Science and Statistics, where he also recently completed an undergraduate degree in computer science. He is undertaking research at the AI Accountability Lab under the supervision of Dr. Abeba Birhane. His work focuses on platform accountability, particularly audits of large platform recommender systems.

Visiting Researchers

Monique Munarini

Visiting Researcher

Monique Munarini (she/her) is a PhD candidate in AI for society at the University of Pisa- Italy. Qualified lawyer, researcher, and policy analyst, her PhD explores how equity can be a more inclusive approach to auditing AI systems than traditional fairness metrics by developing an equity-based framework rooted in feminist theory, participatory action research, and design justice principles, aiming to create AI audits that genuinely account for the needs and voices of all communities affected by AI. She also contributes to global policy debates through her work with the Center for AI and Digital Policy (CAIDP) and Digihumanism.

She uses her legal and political science expertise to engage with and unify the work of computer scientists, social scientists, legal scholars, and real world practitioners to develop realistic and impactful outcomes in the AI ecosystem. Monique's paper on equitable AI audits gained meaningful recognition, and won the Best Research Paper Award at ACM ICEGOV 2024. She was recognized as a CAIDP Fellow 2024 for her work as policy analyst that shaped AI policy through careful research, thoughtful analysis, active engagement with policymakers, and a commitment to human rights, democracy, and the rule of law.

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Dick Blankvoort(he/him)

Visiting Researcher

Dick Blankvoort is a Master's student at the Radboud University Nijmegen and a researcher at the European Open Source AI Index. His primary interest lies in exploring the applications of generative AI and its mathematical underpinnings. His current work consists of delineating and scrutinizing current practices in open-source AI, as well as on exploring the auditing of various more advanced AI systems.

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Past Members

Giovanni Occhipinti

Intern

Giovanni is an AI researcher in progress, studying language technology at the University of Bologna and is currently interested in LLM evaluation and alignment.

giovanni.occhipinti@adaptcentre.ie | LinkedIn

Mauricio Dal Pozzo Schneider

Intern

Mauricio is a legal professional specializing in the intersection of law and technology, with over six years of experience in Civil, Consumer, Property, and Contract Law. Mauricio's career has been shaped by a commitment to providing expert legal counsel, managing complex litigation, and ensuring compliance with regulatory standards.

Mauricio has actively contributed to global discussions through international conferences and collaborative projects focused on economic integration and digital transformation.

mauricio.dalpozzoschneider@adaptcentre.ie | LinkedIn

About AIAL

The AI Accountability Lab (AIAL) is a research lab hosted in the ADAPT Research Centre and the School of Computer Science and Statistics in Trinity College Dublin. We are driven by the urgent need to demystify, critically assess, and publicly communicate the operations and functionality of AI systems by looking under the hood as well as their downstream impact on the public with the aim of shaping public knowledge, shifting power, and reserving fundamental rights, freedoms, and autonomy. The AIAL is an innovative lab at the cutting-edge of AI dedicated to greater transparency on the use of AI systems in the public domain and concrete accountability for the downstream societal impact of these technologies.

The AIAL operates under the core principle that academic research, particularly research in AI, should be of a high-relevance and utility to the public. Thus, we actively engage and partner with local communities, civil society, and rights groups to ensure our work is grounded in real challenges and remains relevant and useful to these stakeholders. We are deeply committed to ensuring our work contributes to fostering transparency and driving a culture of AI accountability. Academic freedom and autonomy are of utmost importance to us and we take great care to ensure our lab is free from direct conflict of interest or implicit influence or pressure from the industry that we are studying. We are funded by philanthropic organisations such as the MacArthur foundation and European grants.

About the PhD

Audits and evaluation of AI systems — and the broader context that AI systems operate in — have become central to conceptualising, quantifying, measuring and understanding the operations, failures, limitations, underlying assumptions, and downstream societal implications of AI systems. Existing AI audit and evaluation efforts are fractured, done in a siloed and ad-hoc manner, and with little deliberation and reflection around conceptual rigour and methodological validity.

This PhD is for a candidate that is passionate about exploring what a conceptually cogent, methodologically sound, and well-founded AI evaluation and safety research might look like. This requires grappling with questions such as:

• What does it mean to represent “ground truth” in proxies, synthetic data, or computational simulation?
• How do we reliably measure abstract and complex phenomena?
• What are the epistemological or methodological implications of quantification and measurement approaches we choose to employ? Particularly, what underlying presuppositions, values, or perspectives do they entail?
• How do we ensure the lived experiences of impacted communities play a critical role in the development and justification of measurement metrics and proxies?
• Through exploration of these questions, the candidate is expected to engage with core concepts in the philosophy of science, history of science, Black feminist epistemologies, and similar schools of thought to develop an in-depth understanding of existing practices with the aim of applying it to advance shared standards and best practice in AI evaluation.

The candidate is expected to integrate empirical (for example, through analysis or evaluation of existing benchmarks) or practical (for example, by executing evaluation of AI systems) components into the overall work. Subsequently, this PhD is an undertaking that marries critical assessment of existing state-of-the-art AI evaluation practices with the aim of driving audits and evaluation towards rigorous science through applied audits.

Requirements

• Hold a bachelor’s degree in the following disciplines: philosophy of science, science and technology studies, computer science, machine learning, artificial intelligence, information systems studies, or cognate disciplines
• In-depth experience and/or publication track-record in the areas of AI evaluation or measurement science
• Familiarity with Black feminist epistemologies, decolonial studies and STS (Good to have)
• Self-driven, ambitious, passionate, and committed to driving AI evaluation towards rigorous science
• Good communicator who is able to articulate the gap between existing evaluation practices and rigorous science and its implications for accountability and AI regulations

Application Process

Application Closing date: 10/02/2026

Interested candidates can submit their application to the email aial@tcd.ie with the subject line “AIAL measurement phd – application” with CV and cover letter to be included. Late applications will not be accepted. Informal enquiries about the role can also be sent to the email aial@tcd.ie with the subject line “AIAL measurement phd – enquiry”.

Please ensure the subject line matches exactly as above. Emails without this subject line may not be considered.

About AIAL

The AI Accountability Lab (AIAL) is a research lab hosted in the ADAPT Research Centre and the School of Computer Science and Statistics in Trinity College Dublin. We are driven by the urgent need to demystify, critically assess, and publicly communicate the operations and functionality of AI systems by looking under the hood as well as their downstream impact on the public with the aim of shaping public knowledge, shifting power, and reserving fundamental rights, freedoms, and autonomy. The AIAL is an innovative lab at the cutting-edge of AI dedicated to greater transparency on the use of AI systems in the public domain and concrete accountability for the downstream societal impact of these technologies.

The AIAL operates under the core principle that academic research, particularly research in AI, should be of a high-relevance and utility to the public. Thus, we actively engage and partner with local communities, civil society, and rights groups to ensure our work is grounded in real challenges and remains relevant and useful to these stakeholders. We are deeply committed to ensuring our work contributes to fostering transparency and driving a culture of AI accountability. Academic freedom and autonomy are of utmost importance to us and we take great care to ensure our lab is free from direct conflict of interest or implicit influence or pressure from the industry that we are studying. We are funded by philanthropic organisations such as the MacArthur foundation and European grants.

About the role

Age verification systems are increasingly being deployed in ways that rely on inferring the age or age range of the user from data such as live selfies or conversational analysis. These systems pose challenges regarding accuracy, bias, privacy, and most importantly raise questions around the scientific legitimacy and methodological soundness underlying the very process. Furthermore, commercially deployed age-verification systems and processes operate in non-transparent ways with no recourse from errors and deficiencies. Currently, information on where, how, and when such technologies are being developed and deployed is extremely scarce, as well as which actors are becoming established as market leaders and key vendors. The AIAL seeks to understand this challenge better through high-quality, high-impact research that helps uphold the principles of scientific validity, methodological rigour, transparency, accountability, and equity.

To directly tackle this urgent problem, the AIAL is looking for a highly driven Post-Doctoral Researcher who can identify and map the current state of commercially used age verification systems, the techniques being used, and the key actors in the development and deployment pipeline. Based on publicly available information, the researcher will closely replicate the techniques used in commercial age-verification systems and undertake socio-technical audits with a focus on assessing the scientific legitimacy, epistemic and methodological validity of the overall approach as well as assessing the accuracy, discrimination, and privacy risks.

The output of this work is expected to contribute to advancing the state of the art in accountable governance and responsible practices, and will support the application of relevant laws such as the GDPR and the AI Act. Ultimately and in the best case scenario, insights from this work would feed legal or other mechanisms to gain access to real-world deployed age-verification systems.

Responsibilities

• Identify and map key age verification techniques and technologies, and major developers, vendors and deployers.
• Using publicly available information, reconstruct an open-source simulation that closely captures potential techniques, models, training data and evaluation benchmarks used in commercial age-verification systems
• Using the open-source simulation , undertake socio-technical audits with a focus on assessing the scientific legitimacy, epistemic and methodological validity of the overall approach as well as assessing the accuracy, discrimination, and privacy risks.
• Seek methods to gain information about contractual agreements and policies regarding the procurement and use of age verification systems to produce qualitative and quantitative insights into the responsibilities and liabilities of developer, vendor, and the deployer
• Grounded in key findings and insights from the work, drive concrete change on the ground, ideally gaining researcher and civil society access into real-world deployed age-verification systems.

Requirements

• Hold a PhD in the following disciplines: computer science, machine learning, artificial intelligence, computer vision, information systems studies, science and technology studies or cognate disciplines
• In-depth experience and publication track-record examining facial recognition, age prediction, (or relevant computer vision methods) and NLP techniques to infer information
• In-depth knowledge and understanding required to detect and debunk AI pseudoscience, race “sciences”, and physiognomic methods and their resurgence in ML models
• Demonstrable experience in audits, assessments, or evaluations computer vision and NLP •Comprehensive knowledge (with a publication track record) and hands on experience of black-box audits, particularly of CV and NLP
• Familiarity with regulations such as the GDPR, AI Act, DSA (Good to have)
• Self-driven, ambitious, passionate, and committed to transparency and accountability of age-verification technologies
• Good communicator who is well able to articulate transparency (lack thereof) and accountability in this domain, even when there is considerable pushback
• Experience working with litigators, civil society organisations, and relevant communities on AI transparency, accountability, equity and the rule of law (Good to have)

Salary Scale

Post-Doctoral Researcher, PD2 Point 1 €54,850 to Point 4 €59,654 as per the IUA payscales from 01.02.26. Appointment will be commensurate with qualifications and experience and in line with current Government pay policy.

Application Process

Application Closing date: 30/01/2026

Interested candidates can submit their application through this form or to the email aial@tcd.ie with the subject line “AIAL age verification postdoc – application” with CV and cover letter to be included. Late applications will not be accepted. Informal enquiries about the role can also be sent to the email aial@tcd.ie with the subject line “AIAL age verification postdoc – enquiry”.

If emailing, please ensure the subject line matches exactly as above. Emails without this subject line may not be considered.

Interested applicants should Apply Here

About AIAL

The AI Accountability Lab (AIAL) is a research lab hosted in the ADAPT Research Centre and the School of Computer Science and Statistics in Trinity College Dublin. We are driven by the urgent need to demystify, critically assess, and publicly communicate the operations and functionality of AI systems by looking under the hood as well as their downstream impact on the public with the aim of shaping public knowledge, shifting power, and reserving fundamental rights, freedoms, and autonomy. The AIAL is an innovative lab at the cutting-edge of AI dedicated to greater transparency on the use of AI systems in the public domain and concrete accountability for the downstream societal impact of these technologies.

The AIAL operates under the core principle that academic research, particularly research in AI, should be of a high-relevance and utility to the public. Thus, we actively engage and partner with local communities, civil society, and rights groups to ensure our work is grounded in real challenges and remains relevant and useful to these stakeholders. We are deeply committed to ensuring our work contributes to fostering transparency and driving a culture of AI accountability. Academic freedom and autonomy are of utmost importance to us and we take great care to ensure our lab is free from direct conflict of interest or implicit influence or pressure from the industry that we are studying. We are funded by philanthropic organisations such as the MacArthur foundation and European grants.

About the role

There is currently a growing move by governments worldwide, including the Irish government, to integrate generative AI technologies into public sector operations. However, information on the where, how, why, and when such technologies are being used remains scarce. Without this knowledge, it is difficult to understand, assess, and ensure the integrity of public services. The AIAL seeks to understand this challenge better and to conduct high-quality, high-impact research that helps uphold the principles of transparency, accountability, and equity.

To directly tackle this urgent problem, the AIAL is looking for a highly driven Post-Doctoral Researcher who can design and implement research that improves transparency and accountability regarding the use of generative AI in public services. The researcher will particularly focus on uncovering and critically examining the integration, procurement, and governance practices of generative AI in government functions and public service operations. The output of this work is expected to be a public-facing database that acts as a knowledge-base and provides transparency and critical evaluation into the use of generative AI with the aim of expanding public knowledge and feeding into regulations and standards. Applications are invited from suitably qualified candidates for a 2-year, in-person, full-time fixed term position as a Post-Doctoral Researcher at the AIAL.

Responsibilities

• Identify and map public sector organisations and government bodies that are integrating generative AI into internal as well as public-facing processes, in particular where these constitute decision making. The scope for this work will be Ireland in the initial stages
• Identify similar initiatives with value-aligned researchers/organisations across Europe and establish collaborations with the aim of painting a fuller-picture as well as performing cross-country comparisons and analysis. The appointed candidate will be expected to establish this collaboration to share findings from Ireland and learn from other EU initiatives to develop a pan-EU analysis
• Develop a public-facing database detailing the use of generative AI in public sectors. The database will provide key information such as modality, model version, vendor information, contractual agreements (if any), known risks, and will also facilitate information such as how it was procured, and where it is being used, for what purpose as well as any prior audits or testing done and main findings
• Identify and analyse contractual agreements and policies regarding the procurement and use of generative AI in public services. The appointed candidate will be expected to understand the procurement system involving government bodies and technology vendors, and the means to achieve this information where it isn’t readily available – such as through freedom of information requests. The analysis of obtained information will be through the lens of relevant European regulations to identify key information and transparency, accountability and equity concerns
• Assess the impact of generative AI in decision-making processes in government and the implications for institutional transparency, governance and accountability
• Investigate and effectively communicate existing efforts (if any) used by government bodies to inform the public on the use of generative AI in public services
• Document, catalogue, and assess usage guidelines, standards, principles and governance instruments issued by government bodies
• Grounded in key findings and insights from the work, drive concrete change on the ground through developing useful recommendation, effective public communication, community organisation and resource pooling, and other effective strategies

Requirements

• Hold a PhD in the following disciplines: computer science, machine learning, artificial intelligence, AI policy research, investigative journalism, management sciences, information systems studies, science and technology studies or cognate disciplines
• In-depth experience and publication track-record examining governance structures, power asymmetries and EU regulations
• Demonstrable experience in data visualisation, statistical analysis and a history of working with freedom of information (FoI) requests
• In-depth knowledge of European legislations and regulations such as the AI Act, DSA, GDPR
• Comprehensive knowledge (with a publication track record) of the AI audit landscape. Hands on experience with black-box auditing as well as executing qualitative and quantitative audits, particularly of generative AI
• Self-driven, passionate, and committed to transforming institutional transparency from a theoretical concept to a concrete, operationalisable, and practical mechanism for expanding public knowledge and fostering accountability
• Ambitious and passionate communicator who is well able to articulate transparency (lack thereof) and accountability in this domain, even when there is considerable pushback
• Experience working with civil society organisations and community organising with a particular focus on AI transparency, accountability, equity and the rule of law

Salary Scale

Post-Doctoral Researcher, PD2 Point 1 - 4 (€54,307 to €59,063) as per the IUA payscales. Appointment will be commensurate with qualifications and experience and in line with current Government pay policy.

Closing date: 09/12/2025

Application Process

Interested candidates can submit their application here.

Applications must include:

• Cover Letter
• CV

Informal enquiries about the role can also be sent to the email aial@tcd.ie with the subject line “AIAL GenAI in public postdoc – Enquiry”