论文标题
自动化歧义：人工智能的挑战和陷阱
Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligence
论文作者
论文摘要
机器学习（ML）和人工智能（AI）工具越来越多地渗透到各种可能的社会，政治和经济领域；分类，分类学和预测复杂的人类行为和社会现象。但是，从关于复杂自适应系统的谬误和幼稚的基础到基础模型的数据集，这些系统都受到问题，挑战和局限性的困扰。它们仍然是不透明的，不可靠的，并且没有考虑社会和结构压迫系统，对社会边缘的人产生了不成比例的负面影响，同时使最强大的人受益。 这些系统的各种挑战，问题和陷阱是在各个领域的研究的热门话题，例如关键数据/算法研究，科学和技术研究（STS），体现和发起的认知科学，复杂性科学，非洲女权主义以及广泛解释的公平性，责任感，责任感和透明度（FACCT）。然而，这些探究领域经常在孤岛中进行。本文将看似不同的探究领域编织在一起，以研究AI的核心科学和道德挑战，陷阱和问题。 In this thesis I, a) review the historical and cultural ecology from which AI research emerges, b) examine the shaky scientific grounds of machine prediction of complex behaviour illustrating how predicting complex behaviour with precision is impossible in principle, c) audit large scale datasets behind current AI demonstrating how they embed societal historical and structural injustices, d) study the seemingly neutral values of ML research and put forward 67 prominent values underlying ML research, e)检查计算机视觉研究的一些阴险和令人担忧的应用，f）提出了一个框架，以应对ML系统周围的挑战，失败和问题以及前进的替代方式。
Machine learning (ML) and artificial intelligence (AI) tools increasingly permeate every possible social, political, and economic sphere; sorting, taxonomizing and predicting complex human behaviour and social phenomena. However, from fallacious and naive groundings regarding complex adaptive systems to datasets underlying models, these systems are beset by problems, challenges, and limitations. They remain opaque and unreliable, and fail to consider societal and structural oppressive systems, disproportionately negatively impacting those at the margins of society while benefiting the most powerful. The various challenges, problems and pitfalls of these systems are a hot topic of research in various areas, such as critical data/algorithm studies, science and technology studies (STS), embodied and enactive cognitive science, complexity science, Afro-feminism, and the broadly construed emerging field of Fairness, Accountability, and Transparency (FAccT). Yet, these fields of enquiry often proceed in silos. This thesis weaves together seemingly disparate fields of enquiry to examine core scientific and ethical challenges, pitfalls, and problems of AI. In this thesis I, a) review the historical and cultural ecology from which AI research emerges, b) examine the shaky scientific grounds of machine prediction of complex behaviour illustrating how predicting complex behaviour with precision is impossible in principle, c) audit large scale datasets behind current AI demonstrating how they embed societal historical and structural injustices, d) study the seemingly neutral values of ML research and put forward 67 prominent values underlying ML research, e) examine some of the insidious and worrying applications of computer vision research, and f) put forward a framework for approaching challenges, failures and problems surrounding ML systems as well as alternative ways forward.
