Expanding Imagined Affordance with Futuretypes:  Challenging Algorithmic Power with Collective 2040 Imagination

Brooks, Lonny Avi; Saveri, Andrea

doi:10.24251/hicss.2017.215

Cited by 2 publications

(1 citation statement)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, there is a need for a concerted effort within and between academic disciplines (e.g., IS, arts, engineering), policymakers, governments, and the wider society to discover innovative ways to use AI to achieve the sustainable development goals (SDGs). Second, while significant attention has been given to understanding the application of AI in a variety of contexts, there is a limited discourse about how to use AI for future-oriented inquiry, whereby IS researchers can explore future scenarios through immersive virtual experiences to better understand how to design resilient IS and incorporate these insights in future-oriented inquiry (Brooks & Saveri, 2017;Chiasson et al 2018). Third, future scholarship on AI governance could investigate auditing of AI systems (Minkkinen et al, 2022b) as a mechanism to foster transparency, accountability, and trust.…”

Section: The Special Issuementioning

confidence: 99%

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

et al. 2022

View full text Add to dashboard Cite

example the responsible design (Dennehy et al., 2021) and governance (Mäntymäki et al., 2022b) of AI systems. While organisations are increasingly investing in ethical AI and Responsible AI (RAI) (Zimmer et al., 2022), recent reports suggest that this comes at a cost and may lead to burnout in responsible-AI teams (Heikkilä, 2022). Thus, it is critical to consider how we educate about RAI (Grøder et al., 2022) and rethink our traditional learning designs (Pappas & Giannakos, 2021), as this can influence end-users' perceptions towards AI applications (Schmager et al., 2023) as well as how future employees approach the design and implementation of AI applications (Rakova et al., 2021;Vassilakopoulou et al., 2022).The use of algorithmic decision-making and decisionsupport processes, particularly AI is becoming increasingly pervasive in the public sector, also in high-risk application areas such as healthcare, traffic, and finance (European Commission, 2020). Against this backdrop, there is growing concern over the ethical use and safety of AI, fuelled by reports of ungoverned military applications (Butcher and Beridze, 2019; Dignum, 2020), privacy violations attributed to facial recognition technologies used by the police (Rezende, 2022), unwanted biases exhibited by AI applications used by courts (Imai et al., 2020), and racial biases in clinical algorithms (Vyas et al. 2020). The opacity and lack of explainability frequently attributed to AI systems makes evaluating the trustworthiness of algorithmic decisions challenging even for technical experts, let alone the public. Together with the algorithm-propelled proliferation of misinformation, hate speech and polarising content on social media platforms, there is a high risk for erosion of trust in algorithmic systems used by the public sector (Janssen et al., 2020). Ensuring that people can trust in the algorithmic processes is essential not only for reaping the potential benefits from AI (Dignum, 2020) but also for fostering trust and resilience at a societal level.AI researchers and practitioners have expressed their fears about AI systems being developed that are Denis Dennehy

show abstract

Section: The Special Issuementioning

confidence: 99%

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

et al. 2022

View full text Add to dashboard Cite

show abstract

Implementing artificial intelligence across task types: constraints of automation and affordances of augmentation

Mazurova,

Standaert

2024

ITP

View full text Add to dashboard Cite

PurposeThis study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.Design/methodology/approachQualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.FindingsWe identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.Research limitations/implicationsWhile the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.Practical implicationsOur research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.Originality/valueOur granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

show abstract

Expanding Imagined Affordance with Futuretypes: Challenging Algorithmic Power with Collective 2040 Imagination

Abstract: Abstract

Cited by 2 publications

References 13 publications

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

Artificial Intelligence (AI) and Information Systems: Perspectives to Responsible AI

Implementing artificial intelligence across task types: constraints of automation and affordances of augmentation

Contact Info

Product

Resources

About