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The Oxford Internet Institute is a leading centre for research into the governance of emerging technologies. Applications are invited from highly qualified individuals for the position of Postdoctoral Researcher. This fixed term position is available immediately for a duration of 14 months (or to funding end date of 1 April 2025,) with a possibility of renewal thereafter, subject to funding availability. The position is located in the centre of Oxford. They will work with Professor Brent Mittelstadt (Associate Professor and Director of Research) and Professor Chris Russell on bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-art machine learning methods. The post will be part of the ‘Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis’ project, funded by UKRI, which will advance ML to achieve real- world early detection and personalised disease outcome prediction of inflammatory arthritis. The position requires a candidate with the ability to coordinate and deliver a work package focused on technical aspects of bias, fairness, and trustworthiness in machine learning for medicine, and publish results in peer- reviewed international academic journals and conferences. The post-holder will be responsible for coordinating and leading work to measure trustworthiness and mitigate bias in LLMs, deep learning (including convolutional neural networks), semi-structured healthcare data, and unstructured GP records. They will also collaborate with researchers leading work to develop novel interpretability methods in medical AI. Excellent technical, analytic, and writing skills, and expertise in machine learning, natural language processing, computer vision, human-computer interaction, or similar relevant areas are required. A record of publication in international peer-reviewed journals and the proven ability to collaborate across disciplines, both commensurate with the stage of the candidate's academic career, are very desirable. The position is suited to candidates who hold or are near completion of a PhD/DPhil in a relevant field such as computer science, data science, statistics, or mathematics. They will ideally have experience working with deep learning methods particularly in the context of LLM training and fine- tuning. They will also have an interest in fairness, accountability, and ethics in machine learning and AI, as well as applications of machine learning/AI in medicine and healthcare. Full details of this post can be found in the Job Description link below. Please direct any queries regarding this role to Brent Mittelstadt – brent.mittelstadt@oii.ox.ac.uk. You will be required to upload a supporting statement, a CV, and details of two referees as part of your online application. The closing date for applications is 12.00 midday on 8 November 2023. Interviews for those short-listed are currently planned to take place the week commencing 20 November 2023. **_Committed to equality and valuing diversity._**
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Job Description _________________________________________________________________________ Job title Postdoctoral Researcher Division Social Sciences Department Oxford Internet Institute Location 1 St Giles – OX1 3JS Grade and salary Grade 7; salary £36,024 - £44,263 per annum Hours Full-time Contract type Fixed-term for 14 months or to funding end date of 1 April 2025 Vacancy reference 168496 Research topic Fairness, bias, interpretability, and trustworthiness in medical AI Principal Investigator / supervisor Professor Brent Mittelstadt (Associate Professor) Funding partners UKRI Overview of the role Fairness, accountability, transparency, and ethics in machine learning (ML) and artificial intelligence (AI) are now major areas of academic research, development, and policy. Many technical and organisational tools, such as computational methods to explain model outputs, statistical tests for fairness, or algorithmic impact assessments, have been developed to address these challenges. However, the mere existence of such tools is insufficient to ensure trustworthy AI. Little is known about the efficacy of different tools intended to make AI systems trustworthy and accountable, how their utility varies across different use cases and domains, how they can be used incorrectly or maliciously, or how best to motivate ‘buy-in’ among responsible communities of practice. As a result, it remains unclear whether such technical and organisational tools will actually produce machine learning and AI systems that remain trustworthy and accountable over time. The Governance of Emerging Technologies (GET) research programme addresses this gap in knowledge by undertaking applied research and developing tools and tests to evaluate the efficacy of existing AI accountability tools. The post-holder will lead a work package examining bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-art machine learning methods for predictive and personalised healthcare. The ‘Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis’ project, funded by UKRI, will advance ML to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. The post-holder will be responsible for coordinating and leading work to measure trustworthiness and mitigate bias in LLMs, deep learning (including convolutional neural networks), semi-structured healthcare data, and unstructured GP records. The complexity of LLMs makes them especially difficult to understand, debug, and validate, and there are concerns that these models might discriminate against groups that are not normally captured by standard bias checks. The role will also involve collaboration on interpretability and explainable AI led by other GET postdoctoral researchers and DPhil students, focusing on the development and evaluation of novel methods to produce counterfactual explanations for LLMs and deep learning in medical use cases. Counterfactual explanations will provide an additional form of validation for LLMs that complements existing approaches. The post-holder will report to Professor Brent Mittelstadt and collaborate with Professor Chris Russell as part of the GET programme. Additionally, the post will involve interdisciplinary collaboration with researchers working in medicine, public health, and computer science at the University of Reading, University of Birmingham, University of Leicester, and others, as well as GET researchers working in law, ethics, psychology, and the social sciences. The post will be part of the GET research programme at the Oxford Internet Institute. Responsibilities/duties • • • • • • • • • • • • • • • Coordinate and deliver a research work package on bias, fairness, interpretability, and trustworthiness in medical AI. Review relevant literature reporting on fairness testing and de-biasing methods in ML and data science, as well as other tools for trustworthy AI. Develop/deploy tools and methodologies for evaluating and mitigating model and dataset bias in LLMs, deep learning, and other state-of-the-art ML approaches in a medical context. Collaborate with other researchers leading work on interpretability and explainable AI. Assist in the development best practice guidelines for clinicians incorporating medical AI into clinical practice in rheumatology. Carry out multi-disciplinary collaborative work with colleagues in partner institutions, research groups, other members of the project team, and work package leaders. Lead and collaborate in the preparation of peer-reviewed research publications. Manage own academic research and administrative activities. This involves small scale project management, to co-ordinate multiple aspects of work to meet deadlines. Adapt existing and develop new research methodologies and materials. Collaborate in the preparation of research grants. Present papers at conferences or public meetings. Act as a source of information and advice to other members of the group on methodologies or procedures. Represent the research team at external meetings/seminars, media, conferences, and similar events either with other members of the group or alone. Participate actively in the OII’s programme of seminars and events, including those aimed at nonacademic audiences. Such other comparable duties as may be required by the Head of Department. Selection criteria Essential 1. Hold, or be near completion, of a PhD/DPhil in a relevant discipline such as computer science, data science, statistics, or mathematics 2. Expertise in machine learning, natural language processing, computer vision, human-computer interaction, or similar relevant areas 3. Experience in research or development on bias and fairness in machine learning 4. Interest in multi-disciplinary collaboration 5. Ability to manage own academic research and associated activities 6. Previous experience of leading or contributing to research articles for publication 7. Excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings Desirable 1. Strong publication record 2. Experience working with deep learning methods particularly in the context of LLM training and finetuning 3. Experience in research or development on interpretability in machine learning 4. Proven interest in fairness, accountability, and ethics in machine learning and AI 5. Proven interest in applications of machine learning/AI in medicine and healthcare Pre-employment screening Standard checks If you are offered the post, the offer will be subject to standard pre-employment checks. You will be asked to provide: proof of your right-to-work in the UK; proof of your identity; and (if we haven’t done so already) we will contact the referees you have nominated. You will also be asked to complete a health declaration so that you can tell us about any health conditions or disabilities for which you may need us to make appropriate adjustments. Please read the candidate notes on the University’s pre-employment screening procedures at: https://www.jobs.ox.ac.uk/pre-employment-checks About the University of Oxford Welcome to the University of Oxford. We aim to lead the world in research and education for the benefit of society both in the UK and globally. Oxford’s researchers engage with academic, commercial and cultural partners across the world to stimulate high-quality research and enable innovation through a broad range of social, policy and economic impacts. We believe our strengths lie both in empowering individuals and teams to address fundamental questions of global significance, while providing all our staff with a welcoming and inclusive workplace that enables everyone to develop and do their best work. Recognising that diversity is our strength, vital for innovation and creativity, we aspire to build a truly diverse community which values and respects every individual’s unique contribution. While we have long traditions of scholarship, we are also forward-looking, creative and cutting-edge. Oxford is one of Europe's most entrepreneurial universities and we rank first in the UK for university spin- outs, and in recent years we have spun out 15-20 new companies every year. We are also recognised as leaders in support for social enterprise. Join us and you will find a unique, democratic and international community, a great range of staff benefits and access to a vibrant array of cultural activities in the beautiful city of Oxford. For more information, please visit www.ox.ac.uk/about/organisation. The Oxford Internet Institute The Oxford Internet Institute – founded in 2001 - is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet. Digital connections are now embedded in almost every aspect of our daily lives, and research on individual and collective behaviour online is crucial to understanding our social, economic, and political world. o o o Research: We have unprecedented access to a huge volume of rich social data, and are developing new theories, concepts and methods to analyse it. Teaching: Our Masters and doctoral programmes bring students from all over the world, to work with our faculty at the cutting edge of their fields. Policy: We provide the empirical data and conceptual analysis that is so needed to design policy solutions to societal problems. Our academic faculty and graduate students are drawn from many different disciplines: we believe this combined approach is essential to tackle society’s ‘big questions’. Together, we aim to positively shape the development of our digital world for the public good. The OII aims to operate at the cutting edge in both quantitative, qualitative and computational methodologies that cut across disciplines and topics. The core of our activity is to develop rigorous peerreviewed research and disseminate the outputs in high-quality journals, while working together with partners and stakeholders to inform and shape policy and practice. Our research focuses on areas critical to the public interest and in many cases to advancing fairness in technology. Our research has already delivered significant impact. Our faculty were among the first to draw the world’s attention to “fake news” and defined the concept of “big data”. They have undertaken ground-breaking research into technology and wellbeing using real-time industry data and persuaded major global firms to adopt new methods and practices. OII researchers have developed the first global ratings system for firms operating in the gig economy and had a significant role in influencing the online harms debate in the UK. Our four teaching programmes graduate around 80 students a year across our two Masters programmes in addition to around seven doctoral students. Many of our talented alumni go on to perform important roles and achieve significant accomplishments in the world of policymaking, technology development, civil society and academia. In 2025, the OII will take up residence in the new Schwarzman Centre for the Humanities, moving from our current location across three sites on St Giles. For more information about the Oxford Internet Institute please visit https://www.oii.ox.ac.uk/. Social Sciences Division The Oxford Internet Institute is a department within the Social Sciences Division, one of four academic Divisions in the University, each with considerable devolved budgetary and financial authority, and responsibility for providing a broad strategic focus across its constituent disciplines. The Social Sciences Division represents the largest grouping of social sciences in the UK. It is home to a number of outstanding departments and to the internationally ranked Law Faculty; all are committed to research to develop a greater understanding of all aspects of society, from the impact of political, legal and economic systems on social and economic welfare to human rights and security. That research is disseminated through innovative graduate programmes and enhances undergraduate courses. For more information please visit http://www.socsci.ox.ac.uk/ How to apply Applications are made through our online recruitment portal. Information about how to apply is available on our Jobs website https://www.jobs.ox.ac.uk/how-to-apply. Your application will be judged solely on the basis of how you demonstrate that you meet the selection criteria stated in the job description. As part of your application you will be asked to provide details of two referees and indicate whether we can contact them now. You will be asked to upload a CV and a supporting statement. The supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. This may include experience gained in employment, education, or during career breaks (such as time out to care for dependants) Please upload all documents as PDF files with your name and the document type in the filename. All applications must be received by midday UK time on the closing date stated in the online advertisement. Information for priority candidates A priority candidate is a University employee who is seeking redeployment because they have been advised that they are at risk of redundancy, or on grounds of ill-health/disability. Priority candidates are issued with a redeployment letter by their employing department(s). If you are a priority candidate, please ensure that you attach your redeployment letter to your application (or email it to the contact address on the advert if the application form used for the vacancy does not allow attachments). If you need help Application FAQs, including technical troubleshooting https://staff.web.ox.ac.uk/recruitment-support-faqs advice is available at: Non-technical questions about this job should be addressed to the recruiting department directly recruit@oii.ox.ac.uk To return to the online application at any stage, please go to: www.recruit.ox.ac.uk. Please note that you will receive an automated email from our online recruitment portal to confirm receipt of your application. Please check your spam/junk mail if you do not receive this email. Important information for candidates Data Privacy Please note that any personal data submitted to the University as part of the job application process will be processed in accordance with the GDPR and related UK data protection legislation. For further information, please see the University’s Privacy Notice for Job Applicants at: https://compliance.admin.ox.ac.uk/job-applicant-privacy-policy. The University’s Policy on Data Protection is available at: https://compliance.admin.ox.ac.uk/data-protection-policy. The University’s policy on retirement The University operates an Employer Justified Retirement Age (EJRA) for all academic posts and some academic-related posts. The University has adopted an EJRA of 30 September before the 69th birthday for all academic and academic-related staff in posts at grade 8 and above. The justification for this is explained at: https://hr.admin.ox.ac.uk/the-ejra For existing employees, any employment beyond the retirement age is subject to approval through the procedures: https://hr.admin.ox.ac.uk/the-ejra There is no normal or fixed age at which staff in posts at grades 1–7 have to retire. Staff at these grades may elect to retire in accordance with the rules of the applicable pension scheme, as may be amended from time to time. Equality of opportunity Entry into employment with the University and progression within employment will be determined only by personal merit and the application of criteria which are related to the duties of each particular post and the relevant salary structure. In all cases, ability to perform the job will be the primary consideration. No applicant or member of staff shall be discriminated against because of age, disability, gender reassignment, marriage or civil partnership, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. Benefits of working at the University Employee benefits University employees enjoy 38 days’ paid holiday, generous pension schemes, travel discounts, and a variety of professional development opportunities. Our range of other employee benefits and discounts also includes free entry to the Botanic Gardens and University colleges, and discounts at University museums. See https://hr.admin.ox.ac.uk/staff-benefits University Club and sports facilities Membership of the University Club is free for all University staff. The University Club offers social, sporting, and hospitality facilities. Staff can also use the University Sports Centre on Iffley Road at discounted rates, including a fitness centre, powerlifting room, and swimming pool. See www.club.ox.ac.uk and https://www.sport.ox.ac.uk/. Information for staff new to Oxford If you are relocating to Oxfordshire from overseas or elsewhere in the UK, the University's Welcome Service website includes practical information about settling in the area, including advice on relocation, accommodation, and local schools. See https://welcome.ox.ac.uk/ There is also a visa loan scheme to cover the costs of UK visa applications for staff and their dependents. See https://staffimmigration.admin.ox.ac.uk/visa-loan-scheme Family-friendly benefits With one of the most generous family leave schemes in the Higher Education sector, and a range of flexible working options, Oxford aims to be a family-friendly employer. We also subscribe to the Work+Family Space, a service that provides practical advice and support for employees who have caring responsibilities. The service offers a free telephone advice line, and the ability to book emergency backup care for children, adult dependents and elderly relatives. See https://hr.admin.ox.ac.uk/my-familycare. The University has excellent childcare services, including five University nurseries as well as Universitysupported places at many other private nurseries. For full details, including how to apply and the costs, see https://childcare.admin.ox.ac.uk/ Disabled staff We are committed to supporting members of staff with disabilities or long-term health conditions. For further details, including information about how to make contact, in confidence, with the University’s Staff Disability Advisor, see https://edu.admin.ox.ac.uk/disability-support Staff networks The University has a number of staff networks including the Oxford Research Staff Society, BME staff network, LGBT+ staff network and a disabled staff network. You can find more information at https://edu.admin.ox.ac.uk/networks The University of Oxford Newcomers' Club The University of Oxford Newcomers' Club is an organisation run by volunteers that aims to assist the partners of new staff settle into Oxford, and provides them with an opportunity to meet people and make connections in the local area. See www.newcomers.ox.ac.uk.
Oxford Internet Institute, 1 St Giles, Oxford OX1 3JS
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The Oxford Internet Institute is a leading centre for research into the governance of emerging technologies. Applications are invited from highly qualified individuals for the position of Postdoctoral Researcher. This fixed term position is available immediately for a duration of 14 months (or to funding end date of 1 April 2025,) with a possibility of renewal thereafter, subject to funding availability. The position is located in the centre of Oxford. They will work with Professor Brent Mittelstadt (Associate Professor and Director of Research) and Professor Chris Russell on bias, fairness, interpretability, and trustworthiness in medical AI applications involving large language models (LLM), deep learning, and other state-of-the-art machine learning methods. The post will be part of the ‘Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis’ project, funded by UKRI, which will advance ML to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. The position requires a candidate with the ability to coordinate and deliver a work package focused on technical aspects of bias, fairness, and trustworthiness in machine learning for medicine, and publish results in peer-reviewed international academic journals and conferences. The post-holder will be responsible for coordinating and leading work to measure trustworthiness and mitigate bias in LLMs, deep learning (including convolutional neural networks), semi-structured healthcare data, and unstructured GP records. They will also collaborate with researchers leading work to develop novel interpretability methods in medical AI. Excellent technical, analytic, and writing skills, and expertise in machine learning, natural language processing, computer vision, human-computer interaction, or similar relevant areas are required. A record of publication in international peer-reviewed journals and the proven ability to collaborate across disciplines, both commensurate with the stage of the candidate's academic career, are very desirable. The position is suited to candidates who hold or are near completion of a PhD/DPhil in a relevant field such as computer science, data science, statistics, or mathematics. They will ideally have experience working with deep learning methods particularly in the context of LLM training and fine-tuning. They will also have an interest in fairness, accountability, and ethics in machine learning and AI, as well as applications of machine learning/AI in medicine and healthcare. Full details of this post can be found in the Job Description link below. Please direct any queries regarding this role to Brent Mittelstadt – brent.mittelstadt@oii.ox.ac.uk. You will be required to upload a supporting statement, a CV, and details of two referees as part of your online application. The closing date for applications is 12.00 midday on 8 November 2023. Interviews for those short-listed are currently planned to take place the week commencing 20 November 2023. Committed to equality and valuing diversity.
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