AI/NLP Research Consultant – (Intersectional Bias Indexes)

Result of ServiceGenerate novel intersectional bias indexes, produce annotated datasets, train and evaluate LLMs for classification, and contribute to knowledge transfer and policy impact. The research will support the development of a global-first bilingual dataset and metrics system rooted in the regional context of the Arab states. Work LocationRemote, with potential missions to ESCWA headquarters (Beirut) or other locations in the Arab region as needed Expected duration12 months Duties and ResponsibilitiesBackground: The United Nations Economic and Social Commission for Western Asia (ESCWA), in collaboration with the United Nations University (UNU) Institute in Macau, is undertaking a pioneering research initiative to advance the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) in support of inclusive economic development and policy innovation in the Arab region. This project is implemented under the broader framework of the Arab Development Portal (ADP) – a regional platform supported by the Arab Coordination Group and ESCWA, aimed at enhancing evidence-based policymaking through accessible, high-quality data and cutting-edge digital tools. This AI-focused research stream will apply state-of-the-art NLP methodologies to identify and quantify intersectional biases—such as those based on gender, socioeconomic status, race, or origin—embedded in digital and media content. The goal is to create new data-driven indexes that inform inclusive economic development policies, support the SDGs, and position the Arab region as a leader in ethical and socially conscious AI research. Duties and Responsibilities: Under the guidance of the Senior Researcher and in close collaboration with the Chief of the ESCWA Decision Support and Data Science Division, the Consultant will: 1. Lead AI/NLP Research and Development: - Lead quantitative Natural Language Processing (NLP) research (in Arabic and English) to generate new intersectional biases indexes (covering socioeconomic status, gender, race and other) that have an impact on inclusive economic development in the Arab region. - Generate lexicons in Arabic and English to capture the required data in social networks and news media. - Deliver an intersectional bias annotated dataset in Arabic and English, which will constitute the first annotated dataset globally for intersectional biases affecting social and economic development. - Evaluate and finetune general-purpose and Arabic-centric LLMs to automate data classification and the generation of intersectional indexes. - Contribute to correlating the resulting NLP indexes with traditional socio-economic indicators and participate in providing new insights for economic development policy making. - Provide guidelines and train UN personnel and policy makers on updating the resulting project indexes overtime once the project ends. - Contribute to positioning ESCWA, and the Arab region, as a global leader in AI-supported data generation and intersectional data analysis for inclusive economic development policy making. - Conduct advanced NLP research (Arabic and English) to develop intersectional bias indexes relevant to inclusive growth. - Create lexicons and typologies to extract bias-related content from digital and social platforms. - Define annotation standards and typologies in consultation with regional experts. 2. Collaborative Engagement: - Engage with ESCWA officials, regional non-profit organizations, and domain experts to co-define use cases and validate outputs. - Support the integration of NLP indexes with conventional socio-economic data on the Arab Development Portal (ADP) 3. Capacity Building and Knowledge Transfer: - Produce methodological guidelines for updating indexes post-project. - Train policymakers and UN ESCWA staff on the use and updating of these indexes. 4. Research Dissemination: - Lead the publication of a minimum of 3 research papers as first author in the proceedings of top-tier AI conferences / journals. The publications will include ESCWA staff working on the project. - Enhance regional and global visibility of ESCWA’s AI work and the ADP platform. - Contribution to policy briefs and custom dashboards on inclusive development using NLP findings on the Arab Development Portal (ADP). Qualifications/special skillsA postgraduate degree (PhD or equivalent) in computer science, computational linguistics, natural language processing, artificial intelligence, or a related field is required. Candidates who have submitted their PhD for examination may also be considered. All candidates must submit a copy of the required educational degree. Incomplete applications will not be reviewed. A minimum of 2 years of demonstrated track record of academic publications in top-tier conferences/journals focused on Arabic-centric NLP research is required. A minimum of 2 years of proven experience in NLP research (including LLMs) related to contextualized social bias, AI fairness, and/or inclusive growth is required. Strong interpersonal and team collaboration skills; experience engaging with non-profits or diverse research stakeholders is required. Passion for the mission of the United Nations and commitment to inclusive economic development in the Arab region is required. Prior experience working in a multidisciplinary research environment is desirable. Residence in the Arab region for contextual engagement and regional alignment is desirable. Excellent writing and presentation skills in Arabic and English, is required. LanguagesEnglish and French are the working languages of the United Nations Secretariat; and Arabic is a working language of ESCWA. For this position, fluency in Arabic and English is required. Knowledge of an additional UN language is desirable. Note: “Fluency” equals a rating of ‘fluent’ in all four areas (speak, read, write, and understand) and “Knowledge of” equals a rating of ‘confident’ in two of the four areas. Additional InformationNot available. No FeeTHE UNITED NATIONS DOES NOT CHARGE A FEE AT ANY STAGE OF THE RECRUITMENT PROCESS (APPLICATION, INTERVIEW MEETING, PROCESSING, OR TRAINING). THE UNITED NATIONS DOES NOT CONCERN ITSELF WITH INFORMATION ON APPLICANTS’ BANK ACCOUNTS.