“It would be pretty immoral to choose a random algorithm”

Abstract

Purpose The purpose of this paper is to report on empirical work conducted to open up algorithmic interpretability and transparency. In recent years, significant concerns have arisen regarding the increasing pervasiveness of algorithms and the impact of automated decision-making in our lives. Particularly problematic is the lack of transparency surrounding the development of these algorithmic systems and their use. It is often suggested that to make algorithms more fair, they should be made more transparent, but exactly how this can be achieved remains unclear. Design/methodology/approach An empirical study was conducted to begin unpacking issues around algorithmic interpretability and transparency. The study involved discussion-based experiments centred around a limited resource allocation scenario which required participants to select their most and least preferred algorithms in a particular context. In addition to collecting quantitative data about preferences, qualitative data captured participants’ expressed reasoning behind their selections. Findings Even when provided with the same information about the scenario, participants made different algorithm preference selections and rationalised their selections differently. The study results revealed diversity in participant responses but consistency in the emphasis they placed on normative concerns and the importance of context when accounting for their selections. The issues raised by participants as important to their selections resonate closely with values that have come to the fore in current debates over algorithm prevalence. Originality/value This work developed a novel empirical approach that demonstrates the value in pursuing algorithmic interpretability and transparency while also highlighting the complexities surrounding their accomplishment.

The ethics of algorithms: key problems and solutions

“…Reporting on empirical work conducted on algorithmic interpretability and transparency, Webb et al (2019) reveal that moral references, particularly on fairness, are consistent across participants discussing their preferences on algorithms. The study notes that people tend to go beyond personal preferences to focus instead on "right and wrong behaviour", as a way to indicate the need to understand the context of deployment of the algorithm and the difficulty of understanding the algorithm and its consequences (Webb et al 2019). In the context of recommender systems, Burke (2017) proposes a multi-stakeholder and multisided approach to defining fairness, moving beyond usercentric definitions to include the interests of other system stakeholders.…”

“…Inscrutable evidence focuses on problems related to the lack of transparency that often characterise algorithms (particularly ML algorithms and models); the socio-technical infrastructure in which they exist; and the decisions they support. Lack of transparency-whether inherent due to the limits of technology or acquired by design decisions and obfuscation of the underlying data (Lepri et al 2018;Dahl 2018;Ananny and Crawford 2018;Weller 2019)-often translates into a lack of scrutiny and/ or accountability (Oswald 2018;Fink 2018;Webb et al 2019) and leads to a lack of "trustworthiness" (see Al-Hleg 2019).…”

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…Although all researchers of this category state that an ethical framework is needed to use AI in organizational decision making, there is no agreement on the design. Some recommend an implementation of decision rules into AI systems (Webb et al 2019;Wong 2019), while others concentrate on making the machine learn moral guidelines by itself (Bogosian 2017), relating to top-down and bottom-up approaches of AI.…”

“…In an attempt to offer a new definition, several researchers have analyzed the behavior humans exhibit when working with artificial agents, especially in terms of attributing human values and shortcomings to the machines. The UnBias project by Webb et al (2019) demonstrates that fairness is the guiding principle in decisions, though the understanding of fairness differs among participants. Wong (2019) lists conditions to ensure fairness.…”

“…Some studies have provided frameworks for analyzing the readiness of an organization or the necessary steps to become more AI-based (see Table 2; Watson 2017; Canhoto and Clear 2019; Yablonsky 2019). Nonetheless, ethical frameworks must still be developed, although this perspective is discussed with increasing awareness (Bellamy et al 2019;Parisi 2019;Shank et al 2019;Webb et al 2019). Managers are hence required to actively engage in this when developing and extending the use of AI.…”