In contemporary societies, the practice is widespread that social assessment is undertaken by AI-based computer systems. Mostly decision making is not entrusted completely to algorithms (ADM: algorithmic decision-making), but often it remains opaque where and how algorithms come into play (Barocas et al. 2017, Silva and Kenney 2018). Applications can be found in medicine, human resource management and market research, policing and criminal justice, as well as governance and the social welfare system. However, occasional applications can also be found in various other domains such as predicting rising stars in the game of Cricket (Ahmad et al. 2017). In medicine AI systems are used for risk assessment of diseases such as mortality through cancer or suicide (Alaa et al. 2018), but also as a decision support system for doctors (Chan et al. 2013, Gross et al. 2013,) and patients (Rubrichi et al. 2014, Lampos et al. 2015), with applications ranging from psychiatry (Selvi and Pratp 2016) to internal medicine (Fraooq and Hussain, 2016). In human resource management algorithms support application processes, whereas applications in market research range from credit scoring (Umin 2019, Yanhao Wei 2019) to personality assessment (Jimenez et al. 2017). In the domain of policing, the approach of predictive policing gained public prominence in the recent years (Perry et al. 2013, Haskell 2014). However, algorithms are also applied for informing sentence decisions by forecasting criminal behavior (Berk and Bleich 2013), predicting the likelihood of recidivism (Zeng et al. 2017, Dressel and Farid 2018), or border surveillance (Barret 2017). Governance is supported by algorithmic procedures in the public administration (Pacini 2019), defense (De Spiegeleire et al. 2017), as well as in the social welfare system (Niklas et al. 2015). Research is also widespread in many domains such as the development of algorithms or ethical assessment of AI use (Dignum 2018, Mantelero 2018, Riterich 2018) and science and technology studies of its social implications (Fourcade and Healy 2017).
Sociology of AI-based assessment technology
In a sociological perspective technology is to be analyzed as a reflection of the social value system: “Algorithms do not make judgments they are the products and the tools of human judgments” (Burk 2019). AI based social assessment can be regarded as a continuation of the concept of bureaucratic governance (Peeters and Schuilenburg 2018) as it has been described by Max Weber (1922) which is characteristic for the process of – multiple – modernization (Eisenstadt 2000). The technology of AI-based assessment systems fulfils the characteristics of bureaucratic governance of division of labour between technical experts, rule-based operations and management of information (Muellerleile and Robertson 2017). At the first sight, the argument that AI-based technologies reinforce bureaucratic governance seems to be counterintuitive: At onset of the rise of the internet and modern information technologies, these technologies have often been perceived as emancipatory technological developments (Benkler 2006). A free flow of information seemed to foster openness and transparency, creating the vision of bottom-up network societies and flattening hierarchies (Kreiss et al. 2011), thereby weakening hierarchical, bureaucratic structures. However, with or without AI-based technology, social assessment remains a control instrument. Assessing individuals implies surveillance and subsequently assigning individuals to categorial scheme. Such an assignment produces a social sorting (Lyon 2003) of a population into predefined categories. This may be the case in government as well as private companies, for instance involved in marketing. Furthermore, assessing risk scores ranging from recidivism to mortality involves the calculation of, typically probabilistic numbers. Governance by numbers is a core element of bureaucratic transformation of governance (Porter 1995, Hacking 1990). According to Weber the effects of bureaucratic governance are
- Efficiency: appropriateness of means and ends in relation to organizational goals
- Objectivity: procedural neutrality
- Rationality: Weber’s concept of rationality can be differentiated into practical, theoretical, substantive, and formal rationality (Kalberg 1980)
At first sight computers seem to be efficient, objective, and follow the standards of formal rationality, as computers undertake complex calculations far more efficiently than humans. Algorithmic procedures objectively follow clearly defined rules that are characteristic of formal rationality. However, already the concept of “raw data” to be processed by computers is illusory (Gitelman 2013). Thus, AI-based assessment remains in danger of pitfalls that undermine bureaucratic rationality. For detecting sources of algorithmic bias, a scheme has been developed by Danks and London (2017) and extended by Silva and Kenney (2018). It provides a kind of algorithmic “value chain”(Silva and Kenney 2018) of the different stages of the development and applications of algorithms.
Following this model, sources of biases can be identified at every stage along this value chain. At the stage of the input data Danks and London (2017) differentiate between training data bias and algorithmic focus bias. Sources of bias at the stage of algorithms are denoted as algorithmic processing bias. At the output stage transfer context several sources of bias are distinguished: misinterpretation bias, automation bias, and non-transparency bias. Finally, at the stage the users, consumer bias, and feedback loop bias are identified. it is important to note that every stage has to be taken into account with equal weight. While algorithms may produce incorrect results in comparison with some empirical data, bias is not something purely technical in the software development process. Algorithms produce bias when they are applied which goes along with a violation of values. Values however are inherently a social concept, as values are guidelines for social conduct, defining what is socially accepted as good or desirable. For this reason, bias at all stages of the algorithmic value chain implies discrimination generated or enforced by AI-based social assessment systems. While ethically discrimination is certainly illegitimate, from a sociological perspective, discrimination provides a challenge for the objectivity of algorithmic bureaucracy as the neutrality of decision making is in question. A description of the potential biases in the algorithmic value chain can be found here.
The critical examination of assessment software mostly concentrates on the issue of discrimination. While certainly discrimination is unfair, the concept of fairness or unfairness of AI-based social assessment is broader. Discrimination does not equal unfairness. Besides a normative definition of fairness, a descriptive approach of what humans perceive as fairness reveals a multidimensional and context dependent concept. An empirical investigation whether feature used by the software COMPAS to calculate scores of recidivism, are perceived as fair revealed at least 8 distinct elements. The 8 dimensions might not be a complete list but are sufficient to explain fairness judgements in the survey (Grgić-Hlača et al. 2018). The survey shows that these dimensions are not perceived as unequivocally important but nevertheless agreed among the sample of different demographic characteristics and value orientations. These dimensions can be violated by software distinctly and need to be taken into account in development of fair assessment software. As with the issue of discrimination, violation of fairness principles is ethically illegitimate, but also a challenge for the bureaucratic governance by algorithms. Unfair algorithmic assessment violates the principle of procedural neutrality that is required for objective decision making as one of the core elements of bureaucratic governance. A description of the dimensions of the concept of fairness can be found here.
Algorithms provide exact calculations without errors. Thus, algorithmic decision making implies the promise of higher accuracy than assessment and decision making by humans. Big data and algorithms are perceived of high potential in psychology (Adjerid and Kelley 2018) and computer-based personality judgments are seemingly more accurate than those made by humans (Youyou et al. 2015). This should increase the efficiency of bureaucratic assessment procedures. However, this claim is rarely tested. The quality of algorithmic decision making depends on the multiple stages of the algorithmic value chain (Dank and London 2017). A recent examination of the accuracy of the software COMPAS for assessing the risk of recidivism of offenders shows that the predictions of software are only of modest quality (Dressel and Farid 2018). This software calculates the risk of recidivism of a criminal offender within the next two years based 137 features of an individual and his or her criminal record. However, the neutrality of the software has been questioned (Angwin et al. 2016), causing a public debate in the USA on algorithmic fairness and discrimination (Flores et al. 2016, Kleinberg et al. 2016). For this reason, the more fundamental question has been investigated how accurate the predictions are made by the COMPAS algorithm. The study compares the false positives and false negatives, i.e. wrongly predicting recidivism and wrongly predicting no recidivism, of the software with assessments made by humans. The test persons for the study had no prior experience in criminal justice. With information of only 7 features given to the humans no statistically significant differences in the accuracy of the predictions made by the software and the predictions made by non-expert humans could be found. This result questions the assumption of increased accuracy of decision making and thus of increased efficiency of bureaucratic governance by using algorithms.
Accounting for flexibility
In other respects, the potential of bureaucratic governance by algorithmic decision making is challenged by the fact that law and administrative regulation is situated in a dynamic, ever changing social environment and need to balance between competing demands. As rules are static this raises a problem for rule-based decision making. Fixed rules cannot take into account social dynamics such as value change or. In the domain of law this is reflected by the fact that law is not static but rather is subject to legal interpretation through human judges. This human element is essential for smooth operation of bureaucratic governance. It has already been outlined by Max Weber (1922) that formal rules are too rigid to fairly accommodate unforeseen circumstances (Burk 2019). For this reason, an efficient application of administrative procedures is artwork that implies a certain context-specific flexibility from the administrative professionals which implies a certain degree of ex ante uncertainty. Bureaucracy cannot be perfect in order to operate.
However, flexible adaption to varying circumstances provides a challenge for rules codified in algorithms, once the rules are hardwired in the system (Casey and Niblett 2017). While it could be argued that machine learning might detect patterns in for instance in court decisions which interpret and thereby develop the codified law, this comes at the cost that it freezes the standard of the time the implementation was encoded and that the further adoption of legal interpretations for instance due to changing social values will come to a halt. In consequence if legal governance and administration is increasingly reliant on data collection and algorithmic data processing (Sag 2017) the danger exist that this triggers an unintended and unexpected change of social practices and their guiding values (Burk 2019). For instance, by analysing the example of the legal balancing standard of “fair use” in the handling copyright and copyright exceptions for platforms such as YouTube or Facebook, Burk (2019) finds that fair use is not a static concept and that algorithmic implementation of automated decision making is already changing legal standards and social practices. Even if challenges of discrimination, fairness, and accuracy can be met, it is necessary that in reality the ideal of bureaucratic governance is always only approximated. Thus, the question remains whether AI based social assessment can account for the vague approximation of bureaucracy to account for dynamics of social life.
Whereas flexibility provides a challenge for bureaucratic governance, bias, discrimination, and fairness or unfairness of algorithmic decision-making raises ethical concerns. Autonomous decision making by machines leads to the problem of accountability for such possible bias or discrimination (Pasquale 2015 in Raw). Leaving important aspects of individual lives to the rule of AI systems risks “paving the way to a new feudal order” (Citron and Pasquale 2014: 19). For this reason, attempts at regulating AI use has become a major agenda of industrial and governmental entities in recent time (Delvaux 2016, National Science and Technology Council Committee on Technology 2016, IEEE 2016, Hern 2016). However, AI governance is in need of ethical guidelines. For developing such guidelines, the full life cycle of algorithmic systems has to be taken into account. Dignum (2018) distinguished between three dimensions in which ethical reason is relevant: ethics by design, which addresses the integration of ethical reasoning in artificial autonomous systems. Ethics in-design addresses methods for analysis of ethical implications of AI systems, whereas ethics for-design regulates the codes of conduct for ensuring integrity of developers and users of AI systems.
In the context of value sensitive design (Aldewereld et al. 2014) and evaluation (Diakopoulos 2015) of autonomous systems, Rahwan (2018) argues for the need of a new algorithmic social contract to prevent the danger of losing democratic control over algorithmic decision making by extending the concept of human-in-the loop (HITL) (Sheridan 2006 in Raw). HITL instantiates human supervisory control of AI systems for identifying misbehaviour and establishing an accountable entity. Due to their wider range, in the case of AI based social assessment systems human supervision additionally has to account for a trade-off between different values and how to agree on distribution of costs and benefits to different stakeholders in a new social contract. This is what Rahwan (2018) denotes as society-in-the-loop. For initiating a public feedback on regulations and legislations, an articulation of and negotiating between (different) values is needed as well as monitoring compliance. On the other hand, in the context of ethics-by design, it has been suggested to stop a system before it can become destructive (Ourseau and Armstrong 2016) or developing a moral Turing test (Wallach and Allen 2008). Instead Arnold and Scheutz (2018) suggest that AI systems should be tested in advance in a simulated environment that contains an ethical scenario generating mechanism. The objective of AI-FORA can be described as realizing the proposals made by Rahwan (2018) and Arnold and Scheutz (2018): Building a co-creation lab for initiating a public feedback on instantiating regulations on AI based social assessment systems that is informed by a scenario generating simulation.
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