MAMMoth: Multi-Attribute, Multimodal Bias Mitigation in AI Systems

人工智能(AI)越来越多地被企业、政府和其他组织用来做出对个人和社会产生深远影响的决策。这为不同部门和日常生活中的自动化提供了巨大的机会，但同时也带来了基于所谓的受保护属性(如性别、种族和年龄)歧视少数群体和边缘人口群体的风险。尽管到目前为止有大量的研究，但所提出的方法在有限的环境中工作，在非常有限的假设下工作，并且不能反映现实世界应用的复杂性和要求。为此，这个庞大的项目侧重于表格、网络和多式联运数据的多分辨缓解。通过其计算机科学和人工智能专家，Mammoth旨在通过开发一个创新的公平意识人工智能数据驱动的基础来应对相关的科学挑战，该基础为发现和缓解(多)歧视提供必要的工具和技术，并确保人工智能系统在多个受保护属性以及传统表格数据和更复杂的网络和视觉数据方面的问责。该项目将从一开始就积极参与人工智能研究中众多弱势和/或代表性不足群体的社区，采用共同创造的方法，以确保实际用户需求和痛苦处于研究议程的中心，并作为项目活动的指导。社会科学和伦理学专家支持的以社会科学为主导的方法将指导项目研究，科学传播方法将增加成果的推广。该项目旨在通过试点将所开发的解决方案应用于三个相关部门：a)融资/贷款申请，b)身份验证系统，c)学术评估。

Artificial Intelligence (AI) is increasingly employed by businesses, governments, and other organizations to make decisions with far-reaching impacts on individuals and society. This offers big opportunities for automation in different sectors and daily life, but at the same time it brings risks for discrimination of minority and marginal population groups on the basis of the so-called protected attributes, like gender, race, and age. Despite the large body of research to date, the proposed methods work in limited settings, under very constrained assumptions, and do not reflect the complexity and requirements of real world applications. To this end, the MAMMOth project focuses on multi-discrimination mitigation for tabular, network and multimodal data. Through its computerscience and AI experts, MAMMOth aims at addressing the associated scientific challenges by developing an innovative fairness-aware AI-data driven foundation that provides the necessary tools and techniques for the discovery and mitigation of (multi-)discrimination and ensures the accountability of AI-systems with respect to multiple protected attributes and for traditional tabular data and more complex network and visual data. The project will actively engage with numerous communities of vulnerable and/or underrepresented groups in AI research right from the start, adopting a co-creation approach, to make sure that actual user needs and pains are at the centre of the research agenda and act as guidance to the project’s activities. A social science-driven approach supported by social science and ethics experts will guide project research, and a science communication approach will increase the outreach of the outcomes. The project aims to demonstrate through pilots the developed solutions into three relevant sectors of interest: a) finance/loan applications, b) identity verification systems, and c) academic evaluation.