Organizational knowledge bases are moving from passive archives to active entities in the flow of people's work. We are seeing machine learning used to enable systems that both collect and surface information as people are working, making it possible to bring out connections between people and content that were previously much less visible in order to automatically identify and highlight experts on a given topic. When these knowledge bases begin to actively bring attention to people and the content they work on, especially as that work is still ongoing, we run into important challenges at the intersection of work and the social. While such systems have the potential to make certain parts of people's work more productive or enjoyable, they may also introduce new workloads, for instance by putting people in the role of experts for others to reach out to. And these knowledge bases can also have profound social consequences by changing what parts of work are visible and, therefore, acknowledged. We pose a number of open questions that warrant attention and engagement across industry and academia. Addressing these questions is an essential step in ensuring that the future of work becomes a good future for those doing the work. With this position paper, we wish to enter into the cross-disciplinary discussion we believe is required to tackle the challenge of developing recommender systems that respect social values.
翻译:组织知识基础正在从被动的档案向活跃的实体转变,人们的工作正在流动。我们看到,机器学习被用来使收集和表面信息系统能够随着人们的工作而收集和表面信息,从而有可能把以前不太明显的人和内容联系起来,以便自动确定和突出某一专题的专家。当这些知识基础开始积极引起人们的注意和他们的工作内容时,特别是在这项工作仍在进行时,我们在工作和社会的交叉点遇到重大挑战。虽然这种系统有可能使人们工作的某些部分更有成效或更方便,但它们也可能带来新的工作量,例如通过让人们担任专家,让其他人接触这些新的工作量。这些知识基础也可能通过改变工作哪些部分是可见的,从而产生深刻的社会后果。我们提出了一系列需要整个行业和学术界关注和参与的公开问题。解决这些问题是确保工作的未来对从事这项工作的人来说是一个至关重要的步骤。有了这个立场文件,我们想进入跨学科的讨论,我们认为,为了应对发展系统的挑战,我们需要尊重提出这种社会价值。