Consultancy for conducting an Outlook study on Artificial Intelligence and Gender
hace 4 semanas
OVERVIEW
Parent Sector : Social and Human Sciences Sector (SHS)
Duty Station: Montevideo
Classification of duty station: [[filter12]]
Standard Duration of Assignement : [[filter13]]
Job Family: Social and Human Sciences
Type of contract : Non Staff
Duration of contract : From 1 to 6 months
Recruitment open to : External candidates
Application Deadline (Midnight Paris Time) : 15-06-2024
UNESCO Core Values: Commitment to the Organization, Integrity, Respect for Diversity, Professionalism
1. Introduction and Context:
The intersection of artificial intelligence (AI) and gender has emerged as a critical area of inquiry, reflecting the increasing integration of AI technologies into various aspects of society. As AI systems become more prevalent in areas such as healthcare, finance, education, and criminal justice, it is imperative to examine how these technologies impact gender dynamics and contribute to broader societal inequalities.
This recognition of gender as a key ethical consideration is reflected in the gender chapter of the UNESCO Recommendation on the Ethics of Artificial Intelligence. It emphasizes the importance of considering gender as a fundamental ethical dimension in the development and deployment of AI technologies and highlights the need to address gender biases, disparities, and discrimination to ensure that AI systems promote gender equality and respect for human dignity.
The gender chapter of the UNESCO Recommendation also acknowledges the intersectionality of gender with other social identities, such as race, ethnicity, sexuality, and disability, in the context of AI ethics. It recognizes that gender biases in AI can intersect with and exacerbate other forms of discrimination and inequality, necessitating an intersectional approach to address multiple dimensions of diversity and inclusion.
Understanding the complex interplay between AI and gender requires a multidisciplinary approach that draws on insights from disciplines such as computer science, social science, ethics, and law, among others. By examining the design, development, and deployment of AI systems through a gender lens, researchers can identify potential biases, disparities, and unintended consequences, as well as opportunities for innovation and intervention.
In this context, the proposed Outlook study aims to explore the current state of research and practice at the intersection of AI and gender, highlighting key trends, challenges, and opportunities. By examining the impact of AI on gender dynamics across various domains, the study seeks to inform policy, industry practices, and research agendas to promote equity and inclusivity in the AI life cycle.
Through a comprehensive review of literature and data analysis, combined with experts' interviews, this study will provide valuable insights into the ways in which AI shapes and is shaped by gender norms, stereotypes, and power dynamics. By elucidating the complex interactions between technology and society, the study aims to contribute to ongoing efforts to harness the potential of AI for the advancement of gender equality and social justice.
2. Objectives:
Impact Analysis of AI on Gender Dynamics: Evaluate how AI influences gender equity across various sectors including healthcare, finance, gig economy, education, and criminal justice. This includes assessing how AI technologies potentially reinforce or challenge existing gender norms, identities, relationships, and disparities. Ethical and Bias Considerations in AI: Examine the ethical implications and consequences of gender biases embedded in AI algorithms. This objective aims to analyze the existing and potential biases that may arise during the AI life cycle and their intersectional impacts on different social groups. Best Practices for Inclusive AI Development: Identify and recommend best practices and regulatory strategies to mitigate gender biases. This involves reviewing the effectiveness of current policies and advocating for inclusive practices in AI research and industry to foster diversity and gender equity. Policy and Actionable Recommendations: Provide concrete, actionable recommendations for policymakers, industry leaders, and researchers to create a gender-inclusive environment throughout the AI life cycle. Propose a roadmap for implementing these strategies effectively to ensure that AI development and deployment advance gender equality and social justice.3. Methodology
3. Methodology:
Literature Review: Conduct an extensive review of interdisciplinary literature encompassing computer science, social sciences, ethics, and law. Focus on recent advancements and key publications that address both the development of AI technologies and their societal impacts, specifically concerning gender. This review will help in identifying gaps in the current research and set the stage for further empirical investigation. Data Collection and Analysis: Gather and analyze data sources, including public datasets, industry reports, and case studies that highlight the intersection of AI and gender. Utilize statistical tools and AI-driven analytics to identify patterns and trends that impact gender dynamics. Experts Interviews: Conduct interviews with diverse experts in artificial intelligence and gender, using a multidisciplinary and multisectoral approach to obtain qualitative insights. Qualitative and Quantitative Analysis: Apply a mixed-methods approach that integrates qualitative and quantitative data to provide a deeper understanding of the context and the mechanisms through which AI impacts gender. Qualitative data from interviews and case studies will enrich the quantitative analysis, offering nuanced insights into complex issues like intersectionality and bias in AI. Stakeholder Workshops: Organize workshops with stakeholders from academia, industry, civil society, media, as well as policymakers to validate findings and refine recommendations. These workshops will serve as platforms for multi-stakeholder engagement, ensuring that the study's recommendations are practical and grounded in diverse real-world perspectives.Areas of Focus
4. Areas of Focus:
Bias and Discrimination in AI Systems: Investigate the presence and impacts of gender biases in AI algorithms and models. This includes examining how these biases affect decision-making in critical sectors. This area will also explore the broader cultural implications of AI-generated content in media and arts on gender perceptions and stereotypes. Workforce Dynamics and Gender Disparities in the Age of AI: Explore how AI technologies are reshaping job opportunities and work practices, and whether these changes are closing or widening the gender gap in various industries. This will investigate specific sectors where AI-driven automation and innovation may either threaten or enhance employment prospects for underrepresented genders. Additionally, it will assess AI’s role in creating or perpetuating gender inequalities in terms of job displacement, wage gaps, and career advancement opportunities. AI Applications in Gender-Specific Issues: Explore the applications of AI in addressing key gender-related challenges across sectors such as health, education, and justice. Focus on how AI tools can empower marginalized groups, enhance access to reproductive healthcare, and address gender-based violence. This area will also cover the use of AI in enhancing digital safety and privacy for individuals across all gender identities. Inclusivity and Accessibility of AI Technologies: Examine how inclusive and accessible AI technologies are to people of diverse gender identities, including transgender and non-binary communities. This will involve assessing the ethical considerations in the development and deployment of AI for gender identification purposes and the implications for privacy and human rights. Gender Equity in AI Policy: Assess opportunities and challenges in promoting gender equity within AI research and development. This includes investigating the representation and participation of diverse genders in AI research teams, funding schemes available to marginalized groups, and the effectiveness of policies aimed at ensuring equitable access to AI technologies and digital literacy, particularly for women and girls in underserved regions.Expected Outcomes
5. Expected Outcomes:
Detailed report presenting the research findings and recommendations for addressing identified challenges.
The report must address:
Potential areas of intervention to improve gender equity in artificial intelligence. Identification of research gaps and suggestions for future investigations. Development of recommendations for evaluating and addressing gender biases in AI algorithms, including guidelines for data collection, model training, and algorithmic auditing. Creation of a repository of best practices and case studies illustrating successful approaches to promoting gender equity in AI research, development, and deployment. Compilation of a toolkit of resources and tools for researchers, practitioners, and policymakers to assess and mitigate gender biases in AI technologies. Establishment of partnerships and networks to support ongoing research, capacity-building, and knowledge-sharing on the intersection of AI and gender.6. Profile:
Expertise: a minimum of 7 years of professional expertise in both artificial intelligence and gender studies, with a deep understanding of the technical aspects of AI algorithms, machine learning, and data science, as well as the social, cultural, and political dimensions of gender dynamics. Education: a master’s degree in computer science, data science, economics, gender studies, sociology, or a related field is preferred, with a strong academic background in both technical and social sciences. Experience: the ideal candidate should have extensive experience in conducting research and analysis in the fields of artificial intelligence and gender, with a proven track record of publications, projects, or initiatives related to this intersection. Skills: strong analytical and critical thinking skills are essential, along with the ability to apply interdisciplinary perspectives to complex problems. Excellent communication skills are also necessary, including the ability to convey technical concepts to non-technical audiences and engage with stakeholders from diverse backgrounds. Econometric skills are desirable. The individual should be able to work effectively in interdisciplinary teams and collaborate with researchers, practitioners, policymakers, and community stakeholders to achieve project goals and outcomes. Languages: working proficiency in English.COMPETENCIES (Core / Managerial)
Accountability (C)Communication (C)Innovation (C)Knowledge sharing and continuous improvement (C)Planning and organizing (C)Results focus (C)Teamwork (C)Professionalism (C)-
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