AI Accountability Lab

@aial.ie

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Trinity College Dublin’s Artificial Intelligence Accountability Lab (https://aial.ie/) is founded & led by Dr Abeba Birhane. The lab studies AI technologies & their downstream societal impact with the aim of fostering a greater ecology of AI accountability

Dr Sananda Sahoo Postdoctoral Researcher Sananda is a Research Fellow at the AI Accountability Lab in Trinity College Dublin. Her research sits at the intersection of critical data studies, automated distribution systems, algorithmic registers, and government accountability. Her postdoc project focuses on documenting, mapping, and evaluating uses of generative AI technologies in the public services. One line of her research looks at government’s accounting systems of residents. Another analyses public service delivery strategies to residents. Sananda holds a PhD from the University of Western Ontario (Western University) in Media Studies. She received her Masters from the University of Missouri and MPhil from Calcutta University, India. She has received funding and fellowships from Mitacs Canada and Data Justice Lab, University of Cardiff. Her research has been published in edited volumes and journals such as BJHS Themes, Antipode, Television and New Media, and Critical Studies in Media Communication.
Dr Hellina Hailu Nigatu Postdoctoral Researcher Hellina is a postdoctoral researcher at the AI Accountability Lab in Trinity College Dublin. Her research broadly lies in the intersection of Natural Language Processing, Human-Computer Interaction and AI Ethics with a focus on low-resourced languages and the Global South. Her postdoc project focuses on co-desiging for the Bamanakan language including setting up data infrastructure and policy framework as well as building language technologies such as speech recognition and machine translation by closely collaborating with community members. Hellina holds a PhD and MSc from University of California at Berkeley in Computer Science. She received her BSc from Addis Ababa University in Electrical and computer engineering. She has received multiple awards for her research including the Wikimedia Foundation Research of the Year Award, best paper Award at EMNLP and Black in AI, and fellowships including the SIGHPC computational and data science fellowship and the FAccT DEI Scholars program.
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.
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.
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.
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: Accountability Mapping accountability mechanisms and governance structures and their alignments with fundamental rights and freedoms and legal frameworks. Challenging existing accountability mechanisms that do not consider or sufficiently address social inequalities, power and resource asymmetries. 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. Auditing 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. 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.