Innovations in AI have focused primarily on the questions of "what" and "how"-algorithms for finding patterns in web searches, for instance-without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate. In part, this is driven by incentives and forces in the tech industry, where a more product-driven focus tends to drown out broader reflective concerns about potential harms and misframings. But this focus on what and how is largely a reflection of the engineering and mathematics-focused training in computer science, which emphasizes the building of tools and development of computational concepts. As a result of this tight technical focus, and the rapid, worldwide explosion in its use, AI has come with a storm of unanticipated socio-technical problems, ranging from algorithms that act in racially or gender-biased ways, get caught in feedback loops that perpetuate inequalities, or enable unprecedented behavioral monitoring surveillance that challenges the fundamental values of free, democratic societies. Given that AI is no longer solely the domain of technologists but rather of society as a whole, we need tighter coupling of computer science and those disciplines that study society and societal values.
翻译:AI的创新主要侧重于“什么”和“如何”因素,以寻找网络搜索模式的“什么”和“如何”因素,例如,没有足够关注可能的伤害(如隐私、偏向或操纵),也没有充分考虑到这些系统运作的社会环境;部分原因在于技术产业的激励和力量,因为产品驱动的焦点往往淹没了对潜在伤害和差异的更广泛的反思关切;但这一重点主要在于计算机科学工程和数学重点培训的什么和如何反映,这种培训强调工具的建设和计算概念的发展;由于技术重点如此紧凑,而且其使用发生迅速的全球性爆炸,AI遇到了一场意想不到的社会-技术问题的暴风雨,从以种族或性别偏见方式行事的算法到长期维持不平等的反馈循环,或使前所未有的行为监测能够挑战自由、民主社会的基本价值观。鉴于AI不再仅仅是技术学家的范畴,而是整个社会的价值观,我们需要更紧密的计算机科学和学科研究。