A/HRC/45/14 are excluded from administrative records, such as undocumented older migrants, refugees and displaced persons. 40. The lack of a uniform approach and a uniform understanding of old age, and of standardized concepts and questions, variables and age groups, means that existing data is neither consistent nor comparable within and among countries.17 C. Data revolution 41. New technologies are leading to an exponential increase of the data available in terms of volume, level of detail, and speed. This data revolution creates unprecedented possibilities for addressing the data gap on older persons, through the integration and the broadening of the scope of data, by incorporating non-traditional sources that have so far not been utilized in official statistics.18 42. Much of the new data is collected passively, from the digital footprints people leave behind, or from sensor-enabled objects, or is inferred via algorithms. The risk of overlooking digitally invisible older persons constitutes an important challenge. The adoption rate among older persons of “smart” digital technology and of online social networks is significantly lower than the average in the population, and digital footprints may therefore not be representative of older persons and in particular of the oldest old. Barriers to the digital inclusion of older persons include low digital literacy, and connectivity differentials, as well as uneven power relations within households that may deny older persons access to digital devices, but also hearing, visual and cognitive impairments, and mental conditions, such as dementia. Any decision-making based on such data is likely to overlook the more vulnerable members of the age groups. There is a risk, furthermore, of algorithms reproducing the underlying lack of understanding of ageing and human bias vis-à-vis older persons.19 43. Data gathered through the use by older persons of assistive and smart ageing technology is particularly sensitive. It links personal and physiological or health data with data on behavioural patterns and with environmental data, such as air humidity and temperature, to enable an all-round analysis and continuous monitoring, in order to design tailored services, including more precise predictability of health conditions and care needs of older persons. While the objective is to enable older persons to lead autonomous and independent lives for longer, there are significant concerns about data protection and privacy. Data that relates sensitive personal information, including health status, should be handled only with the express consent of the older person concerned.20 As devices become more autonomous, data collection will further expand, since these systems function on the basis of data collection and analysis, and such concerns will be magnified. 44. Data generated passively and comprehensively, including, in particular, behavioural data, allows a categorization of older persons into those who are autonomous, those who are semi-autonomous and those who are dependent, and makes it possible to make individualized predictions of their needs, and prescriptions to address them. Respect for 17 18 19 20 10 Economic Commission for Europe, Recommendations on Ageing-related Statistics, prepared by the Task Force on Ageing-related Statistics (United Nations publication, 2016). See the report of the High-level Panel of Eminent Persons on the Post-2015 Development Agenda, available at www.post2020hlp.org/wp-content/uploads/docs/UN-Report.pdf. Mario Callegaro and Yongwei Yang, “The role of surveys in the era of ‘big data’”, The Palgrave Handbook of Survey Research (2017), pp. 175–192; Robin Allen and Dee Masters, “Artificial intelligence: the right to protection from discrimination caused by algorithms, machine learning and automated decision-making”, published online on 2 October 2019, pp. 1–14; and Anthony Flores et al., “False positives, false negatives, and false analyses: a rejoinder to ‘Machine bias: there’s software used across the country to predict future criminals. and it’s biased against blacks’”, Federal Probation Journal, vol. 80, No. 2 (2016), pp. 38–46. OHCHR, “A human rights-based approach to data: leaving no one behind in the 2030 Agenda for Sustainable Development” (2018), p. 17.