Human Data Interaction: Legibility, Agency, Negotiability

在几乎所有与数字经济相关的学科中，围绕人类及其直接产生的数据或作为其努力的副产品产生的数据，都存在着关键和新出现的问题。同样，数据经济刺激了三个部门（公共、私营和第三部门）中每一个部门的一系列举措，以及跨相关学科的广泛研究组合。然而，虽然这些重要的工作正在进行中，但这些努力往往是不同的，往往不会直接反馈到数据驱动系统本身的科学中。迫切需要指导实现富有成效的系统设计原则，同时符合更广泛社会可接受的道德和价值观。那些在系统开发、算法和分析方面的专家面临着具有挑战性的文化差距：首先是那些在艺术和人文等领域的专家，其次是那些不擅长技术但在日常生活中越来越受到技术影响的人。这些分歧的核心是社会对数据驱动系统的技术能力缺乏理解，各部门和学科的研发工作不一致，以及工业，社会和学术驱动因素与人类需求之间的紧张关系。这种紧张关系在几个领域都很明显，尽管很少有像健康这样尖锐的关键。人们只需要看看NHS保护个人医疗记录的努力，与DNA样本的公司货币化形成对比，因为个人利用低成本移动的自我监测和诊断的进步，寻求低成本的健康管理解决方案。在这里，国家、企业和个人层面的驱动因素导致了数据管理和价值的不一致。现在是团结、巩固和正式确定我们跨学科努力的时候了。我们现在必须寻求进一步的努力，同时考虑如何实现，接收和响应新的和新兴的系统-不仅在DE的范围内，而且跨部门，即在反映和支持日常人类活动和关注的组织和社区范围内。在行业层面，行业往往狭隘地关注企业对个人数据的货币化，或个人的效率和个人指标的短期优化（例如“量化自我”）。市场压力意味着技术进步越来越多地在社会和文化影响能够确定之前实施。然而，这意味着支持长期社会，文化和创造性利益的数据密集型系统很少。与此同时，学术研究往往侧重于对本部门比对其他部门更感兴趣的问题。与公共和第三部门组织的学术工作一直是分散的，互动往往侧重于短期创新周期，而不是长期的社会需求。这种挑战、分歧和紧张关系导致重复、矛盾和徒劳的努力。我们的网络是一个整体和包容性的网络方法，对此类系统的社会定位性质敏感。为了实现这一目标，我们将（a）在人类数据交互的旗帜下发展和维持一个合作的跨部门社区，（B）开发一系列系统设计项目，解决DE未充分探索的方面，（c）在人类发展指数旗帜下创建跨部门跨学科研究综合体，（d）在概念上发展和充实人类发展指数框架，（e）创建一套政策和面向公众的案例研究，（f）制定一套核心准则，旨在为设计面向人类的数据驱动系统提供信息。

Within almost every discipline related to the digital economy, there are critical and emerging issues around humans and the data they generate either directly, or as a byproduct of their endeavours. Equally, the data economy has stimulated a range of initiatives responses within each of the three sectors (public, private and third), as well as a broad portfolio of research across relevant disciplines. However, while such important work is ongoing, such these efforts are often disparate and tend not to feed directly back into the science of data-driven systems itself. There is an urgent need to guide the realisation of system design principles that are productive, and yet fit with the ethics and values acceptable to wider society. Those who are expert in development of the systems, algorithms and analytics that raise such issues face challenging culture gaps: firstly, with regard to those who are expert in areas such as the arts and humanities, and secondly with regard to those who are inexpert in technology but who are increasingly impacted by it in their everyday lives. Core to these divisions are issues such as a lack of social understanding of the technical capabilities of data-driven systems, inconsistency of research and development effort across sectors and disciplines, and tensions between industrial, societal and academic drivers, and human needs. Such tensions are visible in several domains, though few as pointedly critical as health. One need only look at NHS' efforts to protect individuals' medical records, in contrast to contrasted against the corporate monetization of DNA samples, as individuals take advantage of advances in low-cost mobile self-monitoring and diagnosiseek low cost solutions to their health-managements. Here, state, corporate and individual-level drivers create inconsistent approaches to the management and value of data. It is time to draw together, consolidate and formalise our efforts across disciplines. We must now seek to structure further endeavour, while considering how new and emerging systems are realised, received and responded to-not just within the bounds of the DE but cross-sector, i.e. within the range of organisations and communities that reflect and support daily human activity and concern. At a sectoral level, industry has often focused narrowly on either corporate monetisation of data from individuals, or individuals' efficiency and short-term optimisation of personal metrics (e.g. the 'quantified self'). Market pressures mean that technical advances are increasingly implemented before social and cultural effects can be determined. This means, however, that data-intensive systems to support long term social, cultural and creative benefits are rare. At the same time, academic research has often focused on questions of interest more to itself than to other sectors. Academic work with public and third sector organisations has been fragmented, with interactions often weighted in favour of shorter term innovation cycles rather than longer term social needs. Such challenges, divergences and tensions lead to duplications, contradictions, and unproductive effort. This is the problem space within which we operate.Our network a holistic and inclusive network approach, sensitive to the socially situated nature of such systems. To achieve this we will (a) develop and sustain a collaborative, cross-sectoral community under the banner of Human Data Interaction, (b) develop a portfolio of system design projects addressing underexplored aspects of the DE (c) create cross-sectoral interdisciplinary synthesis of research under the HDI banner (d) conceptually develop and flesh-out the HDI framework, (e) create a suite of policy and public-facing case studies, papers, prototypes and educational materials, and (f) develop a set of core guidelines intended to inform the design of human-facing data-driven systems.