Trend report on biometrics: Some new insights, experiences and developments

Digital discretion and public administration in Africa: Implications for the use of artificial intelligence

“…Civil society advocates in the EU and United States have therefore proposed similar responses to these issues, including giving data subjects the legal power to request that data be deleted from a database and pursuing regulations restricting the integration or merging of specific data sources and types (Lynch, 2019: 25). Technical measures to mitigate centralisation risk include the development of exchange protocols between data silos of government entities, instead of consolidating data on a single database (cc:eGov, 2007: 13); distorting raw biometric data so that it is less reusable (Grijpink, 2008); and using AI-based methods themselves to reduce the need for large-scale data harvesting, such as data augmentation, transfer learning and synthetic data sets (Polonetsky and Renieris, 2020).…”

“…Endorsing this view, a study of front-line healthcare workers in a number of Africa countries found that “staff’s ability to say no to senior staff and colleagues for demands/decisions not supported by evidence was relatively low” (Nove et al ., 2014: 105). An emphasis on hierarchy and central authority is evident in similar e-governance research where the focus tends to be on reducing variation between centrally defined policies and their implementation by street-level bureaucrats, and how high-level policies and standards may be incorporated into the design of systems (Barata and Cain, 2001; Reddick et al ., 2011). Whilst the use of technology to enforce objectivity and consistency in front-line decision-making has demonstrated value in the delivery of critical welfare services, it does raise a broader question about whether there has simply been a redistribution of subjectivity (and corruption, discussed above) to central actors, and an increase in system fragility by creating a central point of failure (as discussed below) (Donovan, 2015).…”

Digital discretion and public administration in Africa: Implications for the use of artificial intelligence

“…In the biometrics field there is the possibility of distorting raw data so that it is less reusable (Grijpink, 2008) and there are AI-based tools that can reduce the need for large-scale biometric data harvesting such as data augmentation, transfer learning and synthetic data sets (Polonetsky & Renieris, 2020). In the United States, privacy advocates are seeking similar limits to the EU around, especially, biometric and facial recognition data collection and processing including limiting the amount and type of data stored and retained, such as images of crowds, because " [d]ata that is deemed to be 'safe' from a privacy perspective today could become highly identifying tomorrow"; limiting the combination of more than one biometric on a single database to reduce the risk of harm from security breaches; and implementing clear rules and limits on biometric data sharing between different entities (Lynch, 2019, p.25).…”

Characteristic trade-offs in designing large-scale biometric-based identity management systems

“…Grijpink (2008) discusses the various mechanisms used to avoid storing biometric data either on tokens or centrally on databases, and instead storing some irreversible representation of it which can then be compared every time an individual is to be authenticated: a fresh sample is obtained and processed to a similar representation and then compared with the stored value. Shaikh and Dimitriadis (2008) adopt a more critical view towards privacy implications for such systems.…”