Ethical Foresight Analysis: What it is and Why it is Needed?

In this paper, we adopt the definition of a “technology” as “a collection of techniques that are related to each other because of a common purpose, domain, or formal or functional features” (Brey 2012). An “artefact”, by contrast, refers to a physical or digital product, service, or platform created out of a technological field that produces a desired result. An “emerging” technological artefact refers to one that is in its design, research, development, or experimental stages, including beta testing.

Approaches to Ethical Foresight Analysis stem from the broader field of Foresight Analysis (FA), which has been used since the 1950s for anticipating or predicting the outcome of potential policy decisions, emerging technologies and artefacts, or economic/societal trends (Bell 2017). At its outset, FA was commonly used for the purpose of government planning, business strategy, and industry development, including environmental impact analyses and predictions of economic growth. Delphi (more on this presently) was one common FA methodology used for government and economic planning (Turoff and Linstone 1975). These methodologies are characterized by their use of a variety of quantitative and qualitative methods to predict potential future scenarios, including surveys, interviews, focus groups, scenario-analysis, and statistical modelling.

EFA also draws upon the field of Future-Oriented Technology Analysis (FOTA), which itself stems from the fields of innovation studies in the 1970s and 1980s. These fields sought to apply forecasting models to the specific task of identifying the characteristics and effects of future technological advancements (Miles 2010). FOTA methodologies draw heavily on concepts from the field of Science and Technology Studies (STS), which focuses on the interplay between technologies and different actors in a society (Pinch and Bijker 1984). There are several, similarly-named, sub-categories of FOTA, which seek to accomplish different ends (Nazarko 2017). We briefly discuss here four of them. Future Studies focuses on forecasting what possible or probable technologies may exist in the future and how those technologies may be used by a society. Technology Forecasting attempts to predict future characteristics of a particular technology. Technology Assessment evaluates the impacts of an existing or emerging technological artefact on an industry, environment, or society, and can include recommendations for actions to mitigate risks or improve outcomes. Lastly, Technology Foresight seeks to build a long-term vision of an entire technology, economic, or scientific sector by identifying strategic areas of research to improve overall welfare (Bell 2017; Tran and Daim 2008).

When compared to Future Studies and FOTA, Ethical Foresight Analysis (EFA) has a slightly different purpose: forecasting “ethical issues at the research, development, and introduction stages of technology development through [the] anticipation of possible future devices, applications, and social consequences” (Brey 2012). Thus, EFA seeks to identify what kinds of ethical issues an emerging technology or artefact will raise in the future, with some methodologies focused on long term forecasting and others focusing on shorter term projections.

EFA is rooted in several foundational concepts from the field of Science and Technology Studies (STS). One important idea that underlies EFA is that the uses and effects of a technological artefact on society are not determined solely by the artefact’s design but also by different “relevant social groups” who co-opt that technology for specific societal needs. These needs may be different from the intended use of the developer (Collins 1981; Mulkay 1979). For example, the initial main application of the telephone was thought to be that of broadcasting music. As Fischer (1992), 5 puts it in his analysis of the telephone’s development in the US, while a new technology may alter “the conditions of daily life, it does not determine the basic character of that life—instead, people turn new devices to various purposes.” Technological artefacts and society thus “co-evolve” to establish the ultimate uses of an artefact (Lucivero 2016). To take another example, the 19th century bicycle began as a tall front wheeled device with rubber tires but developed into customized models that fit the needs of different relevant social groups. Younger riders, for example, wanted a bicycle with a more forward-facing frame they could use to race competitively while the general public desired a safer design that included a cushioned seat. These desired uses for the bicycle resulted in the development of different types of frames for each relevant social group and the common adoption of an inflatable tire to maximize speed and create more shock absorption (Pinch and Bijker 1984).

Other key concepts from STS relevant to EFA are those of disruption, stabilization, and closure. Disruption refers to the period of time when a technology or artefact is introduced, characterized by rapid changes in both the technology’s development and uncertainty from society about how a technology or artefact should be used. This is the period of time when interpretive flexibility, or the capability of relevant social groups to impart different meanings, expectations, and uses of a technological artefact, is at its highest. Stabilization and closure refer to the periods in which an artefact’s use amongst members of a society settles and eventually becomes well accepted amongst developers and users (Pinch and Bijker 1984).

With emerging technologies, the initial period of disruption produces the greatest obstacle that ethical foresight methodologies attempt to solve: uncertainty (Cantin and Michel 2003). If a developer cannot envision the actual uses of their technology, it can be difficult fully to understand its ethical impact. For that reason, uncertainty is especially high in the R&D stages, when the specific functions and features of a technology are still unclear. Uncertainty also characterises how a technology may affect and change existing moral standards that would then affect its impact. Moral concepts such as autonomy and privacy may, for example, shift over time as technologies and artefacts change how a society defines ‘private space’ and the ‘self’ (Lucivero 2016). A smartphone app that seeks to use health data for diagnosing medical conditions could encounter resistance on the grounds that it invades privacy; alternatively, it may enjoy success by shifting how a society views the sensitive nature of health data in light of the overall medical benefits the app provides. EFA methodologies seek to resolve the uncertainty about which of those outcomes is more likely.

A final foundational concept of EFA from the field of Technology Assessment refers to the difficulty of measuring moral change. As Boenink et al. (2010) note, moral change occurs on several different levels. “Macro” change refers to extremely slow and gradual changes in abstract moral principles within different social groups and societies, while “meso” changes refer to alternations of institutional norms and practices (such as the concept of “informed consent” in modern privacy policies). “Micro” changes refer to niche moral issues occurring in local circumstances where negotiation and change frequently occur (Boenink et al. 2010). Successful EFA methodologies tend to forecast micro changes with higher reliability than meso and macro changes, as an ethicist has less complex unknown variables to account for in an ethical analysis.

Over the past several years, technology firms and research labs have increasingly sought to implement various forms of ethical review of their products, services, and research. These review processes often pursue two goals: (1) to analyse the ethical, political, or societal effects of a current or emerging technological artefact; and (2) to identify actionable ways to mitigate any risks of harm an artefact poses (e.g. through changes to product features, research design, or the cancellation of a project altogether). A common challenge in these review processes, particularly for emerging technologies, is the difficulty of holistically identifying and prioritizing potential harmful impacts of an artefact given the complex ecosystems and shifting socio-political environments into which a technology is deployed. Unlike legal reviews that evaluate an emerging technology against the language of existing legal codes, ethical evaluations require articulating what the moral harms of a particular technology are and how these may change over time.

EFA approaches seek to provide grounded methods for technology and research organisations to identify the ethical implications of an emerging artefact or technology, thereby informing recommendations for mitigating those harms. Importantly, these approaches should be understood as one tool in the toolbox for an ethical review process, not as a holistic or exhaustive form of ethical review in itself. When combined with other methods for ethical design, EFA can be a complementary structured method for assessing the risks of a particular technology.

Delphi is a qualitative form of consensus-based impact analysis that has been used in business strategy and government planning for over 70 years (Turoff and Linstone 1975). More recently, Delphi has been used to forecast potential ethical implications in areas like education, policy, and nursing (Manley 2013). The core tenets of the Delphi model involve anonymized interviews or focus groups with experts from a series of fields relating to specific topics. A Delphi analysis of government agricultural policy, for example, might start with reaching out to experts in biology, chemistry, farming, social policy, community organizations, and other organizations related to any potential relevant social group that may be impacted by the analysis. Once these experts are identified, they respond to a series of questions related to a proposed plan of action or, via more open-ended questions, to hypothesize solutions to likely issues that may arise. Feedback from individuals is anonymized and respondents are prohibited from speaking to one another to discourage cross-contamination of ideas. Once this feedback is collected, a facilitator evaluates the feedback to determine common themes and areas of agreement. Once common areas of agreement are identified, the facilitator then repeats the process of soliciting expert feedback until the list of likely outcomes and ethical issues has been narrowed down (Armstrong 2008). Various sub-methods of Delphi exist, including mini-Delphi—in which non-anonymized focus groups are used instead of interviews—and web-based Delphi, in which forum members interact anonymously in real time online (Battisti 2004). Delphi can also be combined with other methods like scenario analysis, which seeks to create likely future scenarios for a technology that provide multiple possible outcomes. Tapio (2003), for example, recommends taking common themes from respondents to envision multiple future scenarios that could occur. Delphi has been used in various forms of ethical analysis, including to characterize ethical issues raised by the use of novel biotechnologies (Millar et al. 2007). In these instances, a variety of experts and users were consulted on intended and unintended consequences of the introduction of these technologies and their results condensed into a set of most likely scenarios.

Delphi is not the only form of crowdsourced foresight analysis. Prediction markets are another method that collects vast amounts of information from individuals to collate it into a single predictive data point. Prediction markets are based on the idea that a market of predicting bidders can provide accurate crowdsourced forecasts of the likelihood of certain events or outcomes. Major tech firms, like Google and Microsoft, have used prediction markets internally to predict launch dates of potential products (Chansanchai 2014; Cowgill 2005). There are currently no recorded uses of prediction markets to forecast ethical outcomes of a technology or artefact, but this practice has been used widely in the healthcare industry to predict the likelihood of potential ethical issues (Polgreen et al. 2007).

Technology Assessment (TA) is defined as a “systematic attempt to foresee the consequences of introducing a particular [artefact] in all spheres it is likely to interact with” and the effects of that artefact on a society as it is introduced, extended, and modified (Braun 1998; Nazarko 2017). There are four general types of TA (Lucivero 2016):

TA relies on a variety of methodological tools to conduct analyses. One common method is structural modelling of complex systems to understand the interplay between different measurable variables (Keller and Ledergerber 1998). Modelling has been used primarily for assessing the economic impact of an emerging technological artefact, and this method is not well suited to ethical forecasting due to the extreme variability and complexity of ethical issues and the near impossibility of translating ethical considerations into continuous variables.

Two other related toolsets used in TA are impact analyses, which compare likely technological developments against a checklist of known issues (Palm and Hansson 2006), and scenario analyses, which forecast likely scenarios that may develop in the future (Diffenbach 1981; Boenink et al. 2010). Other commonly used methods include risk assessment, interviews, surveys, and focus groups to identify common elements of a technology that may raise ethical concerns (Tran and Daim 2008). Constructive Technology Assessment in particular relies on a series of ongoing focus groups and interviews with relevant stakeholders to provide consistent feedback during the development of a technology.

Related to TA are other forms of impact assessments that are often used to evaluate a particular technological artefact. Privacy Impact Assessments, for example, provide a step-by-step approach for evaluating a particular privacy practice within a company, organization, or product (Clarke 2009). Algorithmic Impact Assessments, by contrast, provide a structured step-by-step checklist for evaluating the health of an algorithmic decision-making system (Reisman et al. 2018). Impact assessment checklists are more commonly used to compare a new or existing artefact against certain standards that an organisation or regulatory body has previously agreed to, but they do not necessarily need to forecast what ethical outcomes may arise in the future. As we will see below, ethical checklists also play a useful role in other ethical foresight strategies.

Another closely related method is Real Time Technology Assessment (RTTA). This embraces the concept of socio-technical dialogue between producers and consumers in the process of determining what new technologies should exist. Through the use of surveys, opinion polling, and focus groups, consumers provide direct input into what kinds of technologies should be designed and into the kinds of ethical dilemmas a new technology can create. RTTA practitioners have occasionally used content analysis and survey research to track trends in perceptions of ethical beliefs about a technology over time. Additionally, RTTA practitioners have used scenario analysis techniques to map areas of ethical concern as they develop over time (Guston and Sarewitz 2002).

A more recent and promising method of TA is Designing by Debate (DbD), which combines elements of CTA with a PAR4P (Participatory Action Research for the development of Policies), a participative method of informing stakeholders about the state of a particular technology or policy area (Ausloos et al. 2018). Developed to address several issues with contemporary R&D with few previous analogues, DbD uses a resource-intensive four-step cycle to map potential challenges with traditionally unrepresented stakeholders and reach a consensus on potential solutions:

DbD also uses a set of components for situations that are ‘similar to ones where full DbD cycles have already been completed and/or where such full cycles are not feasible.’ (Ausloos et al. 2018). These components include creating decision trees from previous DbD cycles to show how a solution was reached, compiling a list of known stakeholders to consult regularly with on new projects, and creating practical guidelines based on previous cycles.

Ethical Technology Assessment (eTA), developed by Elin Palm and Sven Ove Hansson, is a form of TA that seeks to forecast potential ethical issues of an emerging technology through “a continuous dialogue” between the developers of that technology. This process continues from the early stages of research development until well after the technology’s launch (Palm and Hansson 2006). The approach seeks to avoid a “crystal ball” practice of speculating about what potential outcomes may arise in the far future via one single evaluation of a technology. Rather, a sustained and continued assessment ensures that a continuous feedback of emerging ethical concerns from a technology are worked into the next iteration of the design/application process (Tran and Daim 2008).

The process of curating this dialogue involves repeated interviews or surveys of technology developers and other relevant stakeholders of an emerging technology. As opposed to a wider-scale source of stakeholders used in Delphi, stakeholders of eTA tend to be developers or other groups from the specific company or industry working on the technology in question. This dialogue is guided by a checklist of common ethical issues that experts in a particular technological area have agreed upon. Palm and Hansson list nine areas of ethical concern to frame these conversations:

These dialogues are meant to produce a single conception of the ethical issues that may arise in an emerging technology, which are then fed back into artefact’s R&D and design process. In evaluating the responses from these dialogues, a practitioner of eTA is encouraged not to use a single ethical framework to produce one policy recommendation. As Palm and Hansson note, different ethical frameworks (e.g. utilitarianism, deontology) can produce radically different recommendations for ethical behaviour in a particular situation. Unlike crowdsourced and consensus-based practices, the goal of eTA is to flag areas of disagreement and concern between stakeholders to create a new moral framework with which to judge the development of a technology (Grunwald 2000). The end result of this process is to use different moral concepts to flag alternative recommendations for future iterations of the artefact or technology.

Pioneered by researchers in biotechnology ethics like Marianne Boenink, Tsjalling Swierstraa, and Dirk Stemerding, this approach uses “scenario-analysis” to evaluate the “mutual interaction between technology and morality” and to account for how one may affect the other (Boenink et al. 2010). Unlike eTA, TES does not require an ongoing procedure of dialogue, though regular check-ins are encouraged. Like eTA, TES seeks to develop the most likely scenario instead of multiple possible outcomes. However, TES is more long-term focused than most other ethical forecasting strategies and seeks to identify areas of micro change that may in turn create meso and macro changes over time. As an example, a TES practitioner might note that the development of self-driving cars may create micro-changes in urban planning that may affect the moral understanding of driving a petrol-powered car, which may in turn inspire meso-level changes in insurance policies and laws governing accountability for traffic accidents, thereby inspiring a macro-level change in moral accountability for negligence and death.

TES embraces the dynamism of ethics and society by approaching a forecast of the ethical implications of a specific technology in three stages: first, the forecaster begins by “sketching the moral landscape” to “delineate the subject and give…some idea about past and current controversies and how they were solved” (Boenink et al. 2010). This process provides the scope of the analysis and identifies the relevant historical context for identifying common ethical issues that similar technologies have encountered in the past. Second, the forecaster generates “potential moral controversies” using the NEST-(New and Emerging Science and Technology) ethics model, an analytical framework that uses three sub-steps to identify common patterns in ethical debates about emerging technologies:

NEST-ethics helps the forecaster estimate how the moral debate over a new technology or artefact may affect its development. As Brey (2012) notes, literature reviews of ethical issues and workshops with policymakers are good sources of material for this stage, and Lucivero (2016) adds public company and industry statements, surveys of potential users, and statements by members of policy organizations to that list. The end result of this second step is that the forecaster has generated a list of potential moral controversies and resolutions that are likely to arise from an emerging technology.

After generating this list, the final step in the TES methodology is to construct “closure by judging the plausibility of resolutions” (Boenink et al. 2010). Similar to the role of the facilitator in Delphi, the forecaster must narrow down which moral controversies and resolutions are most plausible based on an analysis of historical moral trends of a technology and analogous examples (Swierstra et al. 2010; Boenink et al. 2010). The end result of these three steps is to provide the forecaster with the material to craft potential scenarios of the ethical issues that an emerging technology or artefact may stir up. For example, Boenink and colleagues use this model to create a scenario for evaluating the moral debate over medical experimentation on human beings (Boenink et al. 2010). By evaluating existing ethical frameworks for human experimentation, the historical basis for these frameworks, and current advances in this field relating to stem cells and data mining, Boenink and his team create a scenario of the kinds of moral controversies that may arise in the medical sector.

ETICA (EThical Issues of emerging iCt Applications) is a form of foresight analysis used by organizations like the European Commission (Floridi 2014) to guide policy-making decisions. At its core, ETICA identifies multiple possible futures that a particular technology may take and maps its social impacts (Stahl and Flick 2011). Unlike other foresight methods, ETICA explicitly distinguishes between the features that a technology has, the artefacts that encompass those features, and the application of those artefacts in its analysis (Stahl et al. 2013).

The first stage of ETICA involves the identification of known ethical issues with a particular emerging technology. ETICA relies on various methods to achieve this purpose, including bibliometric analysis of existing literature from governments, academics, and public reports on a particular technology to identify common ethical concerns (European Commission Research and Innovation Policy 2011). This source data is then used to create “a range of projected artefacts and applications for particular emerging technologies, along with capabilities, constraints and social impacts” (Brey 2012). For example, a bibliometric analysis of ICT literature, funded by the EU Commission in 2011, identified numerous ethical issues such as privacy, autonomy, data protection, and digital divides in these areas (Directorate General for Internal Policies 2011). Another method used in ETICA is written thought experiments to flesh out the ethical issues with an emerging technology. For example, Stahl (2013) uses a science-fiction like short story of the decision of a virtual intelligence to annihilate itself to identify ethical issues relating to identity and agency in the field of artificial intelligence.

After ethical issues are collected, they are then prioritized and ordered in an evaluation stage. For example, an analysis of robotics identified several particular issues with robotic applications such as human-like robotic systems and behavioural autonomy (European Commission Research and Innovation Policy 2011). In the second evaluation stage of ICTs, the working group analysed these ethical issues according to four different perspectives: law, gender, institutional ethics, and technology assessment. They then compared and ordered common issues within ICTs, such as ambient intelligence, augmented and virtual reality, future internet, and robotics and artificial intelligence. This ranking is then used in the third stage of the ETICA process to formulate priorities for governance recommendations for policymakers. These recommendations include setting up stakeholder forums to provide feedback on the process and incorporating ethics into ICT research and development (European Commission Research and Innovation Policy 2011; Stahl et al. 2013).

Pioneered by Philip Brey and Deborah Johnson, Anticipatory Technology Ethics (ATE) evaluates technologies during the design phase as a means of reflecting how a ‘technology’s affordances will impact their use and potential consequence’ (Shilton 2015). ATE helps design teams consider the ethical values they bake into a technology by forecasting the kinds of issues that can arise at three different levels: an entire technology, artefacts of that technology, and applications of those artefacts by different relevant social groups (Brey 2012). ATE is founded on the principle that a robust form of EFA must analyse all three levels at once in order to provide a holistic understanding of a particular technology or artefact’s impact on society, thereby reducing ethical uncertainty. At the technology and artefact levels, ethical issues may arise due to inherent characteristics of the technology or artefact, unavoidable outcomes of its development, or certain applications that create such extreme moral controversy that the technology or artefact itself is morally suspect. As an example of this analysis at the technology level, the proliferation and development of nuclear technology enables the weaponization of that technology into artefacts that are so harmful they may justify ceasing development of any nuclear technology entirely. An example of this analysis at the artefact level is the ethical questions that arise from petrol-powered automobiles and their effects on the environment. In the latter analysis, the focus is not on the ethics of an entire technology (automotive) but simply on the development of a specific artefact (petrol-powered vehicles as opposed to electric or solar-powered vehicles).

Like ETICA, ATE uses an identification stage to flag ethical issues and an evaluation stage to analyse which issues are likely to arise and how we should prioritize them. Brey recommends using an ethics checklist during the identification stage as a means to cross-reference the kinds of ethical issues that are likely to arise. In the application level, the ethicist’s focus shifts to how different relevant social groups create a particular artefact’s “context of use” (Brey 2012). Here Brey differentiates between intended uses, unintended uses, and collateral effects of an application of a technology as a basis for analysing the ethical issues at play. Intended uses represent what developers of an emerging technology and relevant social groups understand the purpose of that technology to be at the development and introduction stage. For example, powerful video editing software is intended for use by creative professionals working in the film industry. Unintended uses, however, might include the use of that same technology by terrorist organizations to develop high-quality propaganda videos. An example of possible collateral outcomes would be if this new video editing software, which requires high levels of expertise and resources to master, becomes the status quo for the film industry, thereby making it more difficult and expensive for aspiring low-income film producers to break into the industry.

The next step in ATE is the development of clear ethical issues at each level of analysis. At the technology level, Brey recommends using interviews and focus groups with experts in a particular subject matter to flesh out the relevant ethical issues a technology may raise. Mirroring the practice of eTA, practitioners should prioritize consulting experts in a particular technology field as they are best placed to understand the plethora of ethical issues at stake. Brey also argues that most ethical issues arise at the artefact and application levels in any case (Brey 2012). An example of ethical foresight at this level is Grace et al. (2017)’s paper examining the likelihood of an artificial intelligence-caused extinction level event by surveying experts in AI and machine learning.

At the artefact and application level, the use of existing foresight methodologies like ETICA and TES help provide a clear understanding of the kinds of artefacts that may develop. Surveys and interviews are common methods at these levels to identify future artefacts and applications. This analysis would include forecasts of how an artefact or application is likely to evolve over time and how that artefact or application will combine with others to create new kinds of artefacts. The goal of this analysis in ATE is to develop multiple possible futures that are plausible, as opposed to one single prediction (as in the case of Delphi or prediction markets).